index-B5NAFLmZ.js

js sprag.ai collected 2026-10-05 12:42:44 UTC 1,098,177 bytes, 1,295 lines download raw bytes

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165Voice agents usually chain ASR, an LLM, and TTS. Every handoff adds latency, and transcription throws away tone and emphasis before the synthesizer sees them. Omni models collapse that into one system. We built Symphony on Qwen3-Omni: against our own Rhythm + Chorus pipeline, it finishes a turn about 500 ms sooner. Pipelines still win for c

165omponent swaps, compliance transcripts, and voice cloning.
166
167## The traditional stack
168
169Most AI speech-to-speech pipelines look the same.
170
171**user speech â ASR â transcript â LLM / tool loop â TTS â audio response**
172
173The modularity of this pipeline is highly favorable to developers (though each step can add latency and increase the margin of error for lost context, addressed in the next section).
174
175In April we benchmarked this loop against native speech-to-speech and found the native path has become more competitive (see: [Speech-to-Speech vs Pipelined Voice Agents](/blog/speech-to-speech-vs-pipelined-voice-agents)). The user never hears ASR or the LLM. They hear synthesized audio, so that stage carries whatever the rest of the stack did, and it is the stage that changes the most when the loop collapses into a single model.
176
177## What the boundaries cost
178
179**Latency stacks.** Time to first audio is the sum of three model calls plus the network between them, and none of it overlaps. ASR waits until it is confident the user stopped talking. The LLM waits for that transcript, then has to produce enough text for synthesis to start. TTS waits for that text, then produces its first packet. Every stage is blocked on the one before it, so the delays add up instead of absorbing each other, and the slowest box sets the floor for everything downstream. The TTFA numbers further down are what that addition costs in practice.
180
181**Text is a lossy middleman.** This is the part that is discussed much less. The user's audio carried hesitation, stress, pitch, pacing, and whatever was audible in how they said it. ASR returns a string. By the time TTS runs, every acoustic cue is gone, and the synthesizer is reconstructing prosody from punctuation and whatever style tags the application remembered to attach. The pipeline asks a model to guess at something another model in the same request already knew and discarded. These characteristics of speech are arguably as vital to understanding the messaging of what someone said as the actual words they spoke.
182
183## What an omni model changes
184
185An omni model can take audio in and return audio out, so the synthesizer is not reading a transcript. It is generating from the same internal representation that processed the input.
186
187Qwen3-Omni is the open model that makes this practical. It uses a Thinker-Talker Mixture-of-Experts architecture, where the Thinker handles multimodal understanding and text generation and the Talker produces streaming speech tokens directly from the Thinker's representation. It uses multi-codebook speech codec prediction and a lightweight causal ConvNet for streaming generation, with a theoretical cold-start first-packet latency of 234 ms. It supports text interaction in 119 languages, speech understanding in 19, and speech generation in 10 (see: [Qwen3-Omni Technical Report](https://arxiv.org/abs/2509.17765)). Sprag built on top of Qwen3-Omni to produce Symphony while leveraging vLLM-Omni as hosted inference.
188
189Weights are open and the architecture is documented in the [Qwen3-Omni repo](https://github.com/QwenLM/Qwen3-Omni).
190
191The practical difference for the TTS stage: synthesis stops being a final hop with a text-shaped input and becomes part of generation.
192
193## Serving has been a blocker
194
195Omni models have existed for a while. What kept them out of production stacks was the operational cost of deploying and running one.
196
197vLLM-Omni has changed that. It decomposes multimodal architectures into interconnected stages with independent execution backends, per-stage batching, flexible GPU allocation, and connectors between stages. In the project's own evaluation, that reduces job completion time by up to 91.4 percent against baseline methods.
198
199The direct mapping of Qwen3-Omni is a good example. Thinker handles understanding and semantic generation, Talker converts that to speech codec codes, and Code2Wav turns codes into waveform. Each stage batches and streams independently rather than forcing the whole request down one execution path, which is what keeps the speech stage from reintroducing the serialization tax the pipeline was paying.
200
201## The numbers
202
203Our agentic benchmark runs 30 caller turns from the Harper Valley Bank set through each pipeline to the next spoken reply. Cascaded rows carry the full sequential STT, LLM, and TTS latency. The interactive chart lives on the [benchmarks page](/benchmarks?tab=agentic).
204
205| Provider / Model | TTFA p50 | TTFA p95 | E2E p50 |
206| --- | --- | --- | --- |
207| Sprag Symphony | 1,181 ms | 1,780 ms | 1,632 ms |
208| Sprag Rhythm + Chorus | 1,212 ms | 2,007 ms | 2,150 ms |
209| Cartesia Ink + Sonic 3 | 1,310 ms | 2,995 ms | 3,445 ms |
210| OpenAI GPT Audio | 1,458 ms | 1,975 ms | 3,168 ms |
211| Cartesia Line Agent | 1,896 ms | 2,893 ms | 7,443 ms |
212| ElevenLabs Scribe v2 + Flash v2.5 | 2,864 ms | 3,389 ms | 5,115 ms |
213| AssemblyAI Voice Agent | 3,107 ms | 4,127 ms | 9,076 ms |
214| Deepgram Voice Agent | 3,299 ms | 4,447 ms | 5,338 ms |
215| Deepgram Nova 3 + Aura 2 | 4,246 ms | 6,846 ms | 4,588 ms |
216
217The cleanest comparison in that table is between our own two rows. Symphony and Rhythm + Chorus run on the same infrastru

217cture, against the same samples, with the same tuning effort behind them. The only difference is that one collapses the loop and the other chains STT and TTS around an LLM. They start speaking at nearly the same moment, 1,181 ms against 1,212 ms, but the full turn completes in 1,632 ms versus 2,150 ms.
218
219:::callout{type="insight"}
220Time to first audio hides more than it reveals. Cartesia's Line Agent starts talking at 1,896 ms and does not finish the turn until 7,443 ms. Deepgram's Nova 3 + Aura 2 is the slowest to start in the set and still finishes ahead of it. The goal is to optimize for the whole turn and not just the first packet.
221:::
222
223## Where pipelines still win
224
225The traditional pipeline still has advantages, especially in the TTS stage.
226
227**Component swaps.** If a better reasoning model ships next month, a modular pipeline takes it without touching the voice path. An omni model upgrades as one unit.
228
229**Compliance that needs transcripts.** Some workloads require a raw, auditable transcript of both sides. A traditional pipeline produces these as a byproduct. With an omni model you are adding a path to get that component out.
230
231**Deeper voice customization.** Cloning from reference audio and designing a voice from a description are still better served by dedicated TTS models. Qwen3-TTS can still be run for this use, but it is a different tool from the agent loop.
232
233## Takeaway
234
235Traditional pipelines are not going away in the short term. But the pipeline is no longer the obvious default. Symphony starts a turn at the same moment our pipeline does and finishes it half a second sooner, without a transcript in the middle. Keeping a separate TTS hop now means paying for three sequential boundaries and a text bottleneck to get modularity that you may not need.
236
237If you are building realtime voice, benchmark the native path against your own workload before assuming the pipeline is the safe default. [The Sprag team can help you set that up](/contact-sales).
238`},{slug:`a-transcript-is-a-choice`,title:`A Transcript Is a Choice`,category:`Benchmarks`,excerpt:`Symphony nearly matches a purpose-built verbatim transcription model on spoken disfluencies, and can turn the same recording into the representation each part of a voice agent needs.`,heroImage:`/blog/a-transcript-is-a-choice-hero.webp`,heroImageFit:`cover`,author:{name:`Ian Eaves`,initials:`IE`,role:`Founder`,avatar:`/team/ian-eaves.webp`},date:`2026-08-17`,description:`Symphony nearly matches a purpose-built verbatim ASR model on disfluencies, and can steer the same audio into verbatim, clean, or structured transcripts.`,content:`
239A voice agent hears:
240
241> "Um, can you move my Tuesday appointment to Thurs, Thursday at, uh, three thirty? Actually, make that four. Four works better."
242
243What should its transcript say?
244
245A verbatim transcript would preserve the fillers, the interrupted word, the abandoned time, and the eventual correction. A clean transcript would say something much simpler: the appointment should be moved to Thursday at 4\\:00. Agentic tasks might even need a structured interpretation explaining that 3\\:30 was proposed but ultimately replaced.
246
247All three outputs can be reasonably described as "accurate." They simply answer different questions.
248
249## Disfluency is not the same as transcription error
250
251People rarely speak in finished sentences. Spontaneous speech contains fillers such as "um," repeated words, interrupted fragments, false starts, and corrections made halfway through an instruction.
252
253These features are collectively described as **disfluencies**, but the category includes more than meaningless noise. In the example above, the correction from 3\\:30 to 4\\:00 determines what the agent actually needs to do. A system can preserve what someone said verbatim, or it can produce the clean text they apparently intended.
254
255Most speech recognition systems do not expose that decision explicitly, which can lead to inconsistent behavior across similar utterances. Making matters worse, ordinary Word Error Rate (WER) does not capture this distinction cleanly. A filler counted as an error against a clean reference might be exactly what a verbatim system was asked to preserve.
256
257## Purpose-built vs. general-purpose models
258
259Some companies, like Nyra Labs, are working to address this by purpose-building ASR models explicitly designed to handle disfluencies, and their published benchmarks show strong results. Steerable models like Symphony handle disfluencies (among other issues) as the caller needs, rather than being locked into a single purpose-built workflow. When we tested Symphony against Nyra's complete English benchmark using a simple verbatim-transcription instruction, Symphony performed almost identically to the frontier CrisperWhisper model.
260
261### Sound-excluded disfluency metric, English
262
263| Metric | Symphony | CrisperWhisper 2.0 | Difference |
264| :-- | --: | --: | --: |
265| Precision | 88.93% | 88.29% | +0.64 pp |
266| Recall | 94.93% | 97.03% | -2.10 pp |
267| F1 | 91.83% | 92.46% | approximately -0.6 pp |
268
269::: callout{type="note" title="One qualification"}
270Symphony does not currently emit canonical vocal-sound labels such as [laughter] or [breath] under this configuration. We therefore recalculated the disfluency metric over the three categories both models produce: fillers, repetitions, and cut-offs.
271:::
272
273On these shared categories, Symphony comes within roughly six-tenths of a percentage point of CrisperWhisper 2.0 on disfluency F1.
274
275The precision and recall split is informative. Symphony was slightly less likely to introduce a disfluency that was not present, while CrisperWhisper recovered slightly more of the disfluencies in the reference transcript. Overall, the models were near parity despite CrisperWhisper being purpose-built for controllable verbatim speech recognition.
276
277## On the vagaries of transcription
278
279Recognizing disfluencies is useful, but a voice agent often needs more than a verbatim transcript. Take this example snippet from a hypothetical phone conversation
280
281::audio{src="/blog/audio/transcript-choice-source.mp3" label="Source recording" transcript="Um, can you move my Tuesday appointment to Thurs, Thursday at uh three thirty? Actually, make that four. Four works better."}
282
283Depending on application requirements an agent might need any of
284
2851. A verbatim account of what was spoken.
2862. A clean transcript of the speaker's final intended meaning.
2873. A structured (JSON) interpretation of the corrections and uncertainty in the request.
288
289For applications which narrowly require a solution like (1) traditional ASR models can be great solutions. Likewise, applications which need a pure intent driven transcript like (2) can benefit from purpose built models like CrisperWhisper. The muddy middle is where things get difficult; what if you need structured outputs, or customization over how the transcript is processed? What if you need both a verbatim and cleaned transcript in a single request?
290
291For these sorts of tasks steerable multilingual models, like Symphony, can be a better match because the application can define it's own transcription protocol. In practice this actually ends up being much simpler than it sounds. If you need a single unadulturated transcript you can us something like this
292
293**Instructions**
294
295\`\`\`
296Transcribe this audio verbatim.

297Preserve filler words, repetitions, false starts, interrupted words,
298and self-corrections exactly as spoken.
299Return only the transcript.
300\`\`\`
301
302**Transcript**
303
304> Um, can you move my Tuesday appointment to Thurs, Thursday at uh three thirty, Actually, make that four. Four works better.
305
306The output will respect the natural flow of the users real speech. Similarly, if you need disfluency handling you can use an instruction like this
307
308**Instructions**
309
310\`\`\`
311Produce a clean transcript of the speaker's final intended request.
312Remove incidental filler words, false starts, restarts, and abandoned values.
313Apply explicit self-corrections so that only the speaker's final resolved values
314remain. Preserve any uncertainty that the speaker does not resolve. Do not infer
315or add details that are not supported by the audio.
316Output only the clean intended transcript.
317\`\`\`
318
319**Transcript**
320
321> Can you move my Tuesday appointment to Thursday at 4\\:00?
322
323It can even flexibly produce JSON output matching your specific transcription requirements.
324
325**Instructions**
326
327\`\`\`
328Interpret the attached voice request. Return one bare JSON object and nothing else.
329Use exactly these fields:
330- "final_request": the speaker's final resolved request as a string
331- "corrections": an array of objects with string fields "abandoned" and "replacement"
332- "unresolved_uncertainty": an array of strings
333- "material_disfluencies": an array of short strings
334Identify explicit corrections and distinguish abandoned values from final values.
335Include only uncertainty that remains unresolved. In "material_disfluencies", list
336each distinct audible filler, interrupted word or restart, repetition, and
337self-correction exactly once, in order of occurrence. Do not duplicate entries.
338Use no more than ten entries. Do not infer unsupported details. Do not include a
339transcript, explanation, or Markdown fence.
340\`\`\`
341
342**Response**
343
344\`\`\`json
345{
346  "final_request": "Can you move my Tuesday appointment to Thursday at 4:00?",
347  "corrections": [
348    {
349      "abandoned": "3:30",
350      "replacement": "4:00"
351    }
352  ],
353  "unresolved_uncertainty": [],
354  "material_disfluencies": [
355    "um",
356    "uh",
357    "thurs",
358    "three thirty",
359    "actually",
360    "make that four",
361    "four works better"
362  ]
363}
364\`\`\`
365
366
367
368Under a strict output-format test, the cut-off notation was the one limitation. Symphony recognized that the speaker began and restarted the word, but it normalized the boundary rather than marking it with the requested symbol. That is better understood as a formatting limitation than a failure to recover the spoken repair. Nyra's own benchmark reports several cut-off metrics because exact fragments are acoustically ambiguous, and its headline cut-off F1 gives credit when a model preserves an aligned token at the interruption even without a dedicated cut-off marker ([Nyra Labs](https://nyra-labs.com/research/nyra-verbatim-speech-benchmark)).
369
370## Not all repetition means the same thing
371
372We also tested whether Symphony could distinguish two recordings containing superficially similar repetition:
373
374> "I really, really need this fixed today."
375
376and:
377
378> "I, I need this fixed today."
379
380In both cases, a purely mechanical transcriber can observe that a word was repeated. The more useful question is why.
381
382::audio{src="/blog/audio/transcript-choice-emphasis.mp3" label="Repetition as emphasis"}
383
384For the first recording, Symphony preserved both instances of "really" and classified the repetition as emphasis. Its explanation treated the repetition as an intensifier rather than as an abandoned attempt to form the sentence.
385
386::audio{src="/blog/audio/transcript-choice-repair.mp3" label="Repetition as speech repair"}
387
388For the second recording, Symphony preserved both instances of "I" but classified the repetition as a speech repair. It interpreted the first "I" as a false start followed by an immediate restart.
389
390The words alone do not fully explain this distinction. The same surface behavior, a repeated token, can carry different functions depending on the word, its delivery, and the surrounding language.
391
392## The transcript should fit the task
393
394On Nyra's English benchmark, Symphony nearly matches a purpose-built verbatim transcription model on fillers, repetitions, and cut-offs. In controlled tests, it also used those signals to recover the speaker's final request, identify a corrected value, and distinguish deliberate emphasis from a speech repair.
395
396Every transcription task has its own specific needs and requirements. Disfluency is an extremely important part of that story, but it is only one part. The broader problem is to build systems that can reliably preserve, remove, or interpret the features of the transcript required for its task.
397
398To get started with Symphony, [create an account](https://sprag.ai/auth/signup).
399`},{slug:`qwen3-8-27b-free-on-sprag`,title:`Qwen3.8-27B is free on Sprag through the end of August`,category:`Announcement`,excerpt:`Qwen3.8-27B is open weights under Apache 2.0 and built to be run locally. Running it locally still takes a 24GB card, so we're serving it for free through the end of August.`,heroImage:`/blog/qwen3-8-27b-free-hero.webp`,heroImageFit:`cover`,author:{name:`Sprag`,initials:`SP`,role:`Sprag Team`,avatar:`/brand/sprag-mark-dark.svg`},date:`2026-08-16`,description:`Qwen3.8-27B is free on the Sprag API through at least the end of August. Generate a key and try it before you buy the 24GB card.`,content:`
400Qwen3.8-27B is open weights under Apache 2.0 and built to be run locally. Running it locally still takes a 24GB card, and plenty of the people most interested in this model don't have one.
401
402## Why we're hosting it for free
403
404So we're hosting it on the Sprag API at no cost through at least the end of August. Generate a key and use it. If the model turns out to be worth the hardware, go buy the card and run it yourself.
405
406## Why we care about this
407
408Sprag is built on vLLM-Omni, which we develop in the open. Making models like this one practical to serve is the work we already do, and for the next few weeks we're covering the GPUs.
409
410## Getting started
411
412Everything you need is on the model page.
413
414[Qwen3.8-27B on Sprag](/models/qwen/qwen38-27b) â
415`},{slug:`issen-delivers-ai-language-tutoring-at-scale-with-sprag`,title:`How ISSEN delivers AI language tutoring at scale with Sprag`,category:`Customers`,excerpt:`ISSEN (YC F24) replaced a deprecated Gemini Flash 2.0 transcriber with Qwen3-Omni served on Sprag, reaching sub-300ms multilingual STT latency and 90% accuracy on hard code-switched evals.`,heroImage:`/__l5e/assets-v1/748cc31c-cba9-4395-8aef-9773e8695dda/issen-sprag-thumbnail.png`,heroImageFit:`cover`,author:{name:`Alex Smith`,initials:`AS`,role:`CEO`,avatar:`/__l5e/assets-v1/1c5b3744-fd0d-4be9-b3da-7adfeff0b672/alex-smith.jpg`},date:`2026-07-16`,description:`How ISSEN moved its multilingual voice tutor to Sprag-served Qwen3-Omni for sub-300ms transcription across 50+ languages.`,content:`
416[**ISSEN (YC F24)**](https://www.issen.com/) is redefining what a language learning app can be. Instead of preset prompts and gamified drills, ISSEN gives learners free-flowing, human-like voice conversations with an AI tutor that adapts to their level, interests, and goals across more than 50 languages. Whether a learner is practicing Japanese, Spanish, or Gujarati, ISSEN re

416creates the experience of a dedicated human tutor.
417
418That experience depends on a fast, accurate voice pipeline. A tutor that mishears a learner, lags, or struggles across languages breaks the immersion instantly. To power the speech-to-text (STT) layer behind those conversations, ISSEN turned to Sprag, the voice AI inference provider lowering latency and cost across the voice stack.
419
420Built on Sprag, ISSEN's transcription runs on a high-quality multimodal model with low latency, broad language coverage, and a cost profile the two teams continue to optimize together.
421
422## The challenge: replacing a core model without regressing quality
423
424ISSEN relied on Gemini Flash 2.0 because it offered strong pricing and performance across many languages, which is essential when you're supporting users that speak more than 50 of them. When that model was abruptly deprecated, ISSEN needed a replacement immediately, and it could not be a step down.
425
426The bar was high on three fronts:
427
428- **Multilingual accuracy.** Speech recognition is one of the biggest sources of friction in voice AI. ISSEN's learners switch languages, accents, and proficiency levels constantly, and any weakness in transcription is felt directly in the product.
429- **Latency.** Realtime conversation means transcription cannot add perceptible delay.
430- **Cost at scale.** With a steady, always-on user base across many time zones, small per-request differences compound quickly.
431
432## The solution: transcription served and optimized on Sprag
433
434ISSEN turned to Sprag to run a large multimodal transcription model in production without the usual penalties in cost, latency, or scale. Key elements included:
435
436- **Higher-quality transcription.** ISSEN runs a large multimodal model as its transcriber rather than the smaller, dedicated ASR model, which keeps accuracy high across all of its supported languages. Sprag's serving stack makes the larger model practical to run at production latency and cost.
437- **Low-latency serving.** Sprag serves the model on a vLLM gateway, with context caching and prefix-aware routing enabled by default and STT billed on input audio duration. In production, ISSEN sees less than 300ms transcription latency, fast enough to keep realtime conversations feeling natural.
438- **Throughput on demand.** As ISSEN rolled out to its full user base, Sprag raised throughput limits the same day and provisioned headroom ahead of further growth, so the deployment was never gated by capacity.
439- **Joint cost optimization.** Because transcription cost is driven mostly by audio duration, Sprag optimized it alongside ISSEN's team to reduce costs, matching the model's native sample rate, trimming silence to cut wasted tokens, quantizing the model to shrink its memory footprint, and running dedicated instances tuned to ISSEN's transcription-only workload.
440
441> "I've been working with multilingual voice AI for years, testing dozens of transcribers and companies, and Qwen-Omni on Sprag has been the highest performing model at very low latency. We have difficult evals that test noisy, multilingual code-switched audio, with a variety of accents. Most models like Deepgram, 11labs scribe, etc land at around 70% on our evals, while Sprag has reached 90%, with a latency under 300ms."
442>
443> - Mariano Sorgente, Founder and CEO of ISSEN
444
445## The impact
446
447By moving their STT to Sprag, ISSEN replaced a deprecated model with a stronger one, and did it quickly:
448
449- **A fast, seamless cutover.** ISSEN migrated its transcription workload to Sprag and rolled it out to its full user base without degrading its learner experience.
450- **Realtime latency.** Less than 300ms transcription latency keeps voice conversations feeling natural.
451- **A clear path on cost.** Silence trimming and a quantized model variant meaningfully reduced per-request cost, with further optimization underway.
452- **Room to grow.** Throughput was provisioned well beyond ISSEN's current peak, so scaling further does not require re-architecting.
453
454> "Sprag has been great to work with and extremely helpful with deployment, custom deals and support. I look forward to working with them in the future."
455>
456> - Mariano Sorgente, Founder and CEO of ISSEN
457
458Want to lower latency and cost across your voice AI pipeline? [Learn more about Sprag](https://sprag.ai).
459`},{slug:`multimodal-api-compatibility-from-the-adapter-layer`,title:`Multimodal API compatibility from the adapter layer`,category:`Engineering`,excerpt:`Implementation notes from building compatibility across OpenAI, OpenRouter, Hugging Face, Cartesia, and ElevenLabs. Request shape, response shape, and usage units should be treated as three separate problems.`,heroImage:`/blog/multimodal-adapter-hero.webp`,heroImageFit:`cover`,author:{name:`Ian Eaves`,initials:`IE`,role:`Founder`,avatar:`/team/ian-eaves.webp`},date:`2026-07-13`,description:`Notes on multimodal adapter design: why request, response, and usage compatibility are separate problems across providers.`,content:`
460*Implementation notes on request shape, response shape, and usage units*
461
462We recently implemented compatibility across several AI gateways and model-provider APIs, including OpenAI-style chat APIs, OpenRouter, Hugging Face Inference Providers, Cartesia, and ElevenLabs.
463The work was mostly straightforward in places where the underlying task looked like chat.
464It required more judgment in places where the task involved audio, images, realtime sessions, task-s

464pecific response objects, or provider-specific usage fields.
465
466The distinction between these different modalities and model capabilities ends up really mattering if you are building against more than one provider.
467A request can be easy to send even when the response needs task-specific parsing.
468A response can be easy to parse while the usage object still needs provider-specific interpretation.
469A usage object can include a number called \`audio_tokens\` or \`total_tokens\` while leaving open what that number means for pricing, display, or comparison.
470
471The useful framing for us was to separate compatibility into three layers:
472
4731. **Request shape:** what does the caller send?
4742. **Response shape:** what does the provider return?
4753. **Usage and pricing semantics:** what was counted, in what unit, and how should the caller compare it?
476
477::figure{src="/blog/multimodal-three-layers.svg" caption="Request shape, response shape, and usage semantics each diverge independently per provider. Compatibility on one does not imply compatibility on the others." width="wide" alt="Three side-by-side boxes labeled Request shape, Response shape, and Usage semantics, each listing example fields the layer is responsible for"}
478
479For basic chat completions, those three layers have some loose convergence.
480A caller usually sends messages.
481The messages usually contain roles and content.
482The response usually contains choices, messages, finish reasons, or a close equivalent.
483The usage object usually includes some version of input tokens, output tokens, and total tokens.
484
485That is enough to build against in many text-chat cases but it is not enough to make multimodal compatibility automatic.
486Once the same adapter layer has to support speech-to-text, text-to-speech, image tasks, realtime audio, routed LLM calls, and multimodal chat, the three layers start to diverge.
487
488This post is an after-action report from that implementation work.
489The goal is not to say one provider's API shape is better than another's.
490Most of the shapes we looked at were reasonable for the task they served.
491The main lesson was that request compatibility, response compatibility, and usage compatibility should be treated as separate problems.
492
493## Request Shape
494
495The request layer is where OpenAI-style compatibility has been most useful.
496For chat, a shared request shape gets you a long way.
497A caller can usually reason in terms of \`messages\`, \`role\`, \`content\`, model selection, generation parameters, and sometimes tools.
498
499That pattern is less complete once the task is no longer primarily chat.
500Hugging Face's provider docs describe this split directly.
501For LLM and VLM tasks, providers that support the OpenAI API can often avoid much of the custom integration work.
502For other tasks, including text-to-image and automatic speech recognition, Hugging Face documents task-specific schemas and notes that provider adapters may need to translate parameter names and output names.
503
504That matched what we saw in practice.
505Chat requests often fit a familiar outer shape.
506Multimodal requests often carry task-specific fields that should remain visible.
507
508A text-to-speech request is a simple example.
509The caller is not only asking a model to produce an output.
510The caller is asking for speech with a particular voice, format, language, timing behavior, and sometimes continuity across multiple inputs.
511
512Cartesia's TTS API includes fields such as \`model_id\`, \`transcript\`, \`voice\`, \`output_format\`, \`context_id\`, timestamp options, pronunciation dictionary settings, and generation config.
513
514\`\`\`json
515// Cartesia TTS request, simplified
516{
517  "model_id": "sonic-model",
518  "transcript": "Text to speak",
519  "voice": {
520    "id": "voice-id"
521  },
522  "output_format": {},
523  "context_id": "context-id",
524  "add_timestamps": true,
525  "add_phoneme_timestamps": false,
526  "generation_config": {
527    "speed": 1
528  }
529}
530\`\`\`
531
532ElevenLabs exposes a different but still voice-native shape.
533Its standard TTS route puts \`voice_id\` in the path and accepts fields such as \`text\`, \`model_id\`, \`output_format\`, and \`voice_settings\` in the body.
534
535\`\`\`json
536// ElevenLabs TTS request, simplified
537POST /v1/text-to-speech/:voice_id
538
539{
540  "text": "Text to speak",
541  "model_id": "eleven_multilingual_v2",
542  "voice_settings": {}
543}
544\`\`\`
545
546In some cases these are not just naming differences, rather they encode task behavior.
547A voice API generally needs to describe the voice, the output audio format, and the timing or streaming behavior.
548An image API may need resolution, aspect ratio, source image, mask, seed, number of outputs, or output format.
549A realtime API may need session state, buffering behavior, turn detection, interruption behavior, and event-level controls.
550
551For the adapter layer, the request-shape question was therefore practical.
552Which fields are common enough to normalize?
553Which fields need to remain explicit task parameters?
554Which fields can be passed through without pretending they are portable?
555
556A chat-compatible request shape helped when the task was chat-like.
557For task-native multimodal APIs, it was usually cleaner to preserve the task's request model and normalize around it.
558
559## Response Shape
560
561Even if two providers let you send similar requests, the output may not have the same shape or respect the same API concerns.
562While partly a function of the API it's also impacted by the underlying model-capability.
563
564ASR timestamps are a good example of this.
565At the interface layer, timestamps can look like a response-format option.
566Transcription requests somewhere like HuggingFace might include a field like \`return_timestamps\` indicating that the response should include timestamped chunks alongside the raw transcription.
567In practice the ability to generate those timestamps might not exist, indeed often does not exist, intrinsically to the model.
568
569Take the case of Qwen3-ASR.
570The vLLM-backed Qwen3-ASR example accepts audio through a chat-style request and returns the transcription through a completion-like response.
571The caller sends an audio message and reads the output from \`choices[0].message.content\`.
572
573However, actually generating timestamps is delegated to a subordinate model designed to accept both the transcription and the raw speech.
574This would be something like Qwen3-ForcedAligner-0.6B used for timestamp prediction.
575So timestamps become not only a formatting choice on the ASR response but may actually require a second alignment path coordinated by the backend to generate the response expected by the user.
576
577::figure{src="/blog/qwen3-asr-aligner.svg" caption="Qwen3-ASR produces a transcript with no timing. Generating timestamps requires a second model that consumes both the transcript and the original audio." width="wide" alt="Pipeline diagram. Audio feeds Qwen3-ASR, which produces a transcript with no timing. The original audio and the transcript both feed a second model, Qwen3-ForcedAligner, which produces the timestamped output."}
578
579These details expose complicated interactions between the various serving modes as well.
580While Qwen3-ASR supports streaming inference through the vLLM backend, it's not easily possible to stream timestamp aligned output because the subordinate timestamp generation requires a complicated transcript and associated text to complete.
581So an adapter cannot easily model timestamps as \`supported: true\` for the model family.
582
583The same pattern shows up on the speech-generation side.
584vLLM-Omni exposes an OpenAI-compatible /v1/audio/speech endpoint for text-to-speech and for Qwen3-TTS, that interface can make several model operations look similar from the outside despite performing substantively different operations internally.
585With CustomVoice, the caller selects a predefined speaker and can optionally provide style instructions:
586
587\`\`\`json
588{
589  "input": "Hello world",
590  "task_type": "VoiceDesign",
591  "instructions": "A warm, friendly female voice with a gentle tone"
592}
593\`\`\`
594
595With VoiceDesign, the caller describes the desired voice in natural language:
596
597\`\`\`json
598{
599  "type": "speech",
600  "audio": "<binary or url>",
601  "format": "wav",
602  "task_type": "VoiceDesign",
603  "voice_source": {
604    "type": "natural_language_description"
605  }
606}
607\`\`\`
608
609With Base, the caller is doing voice cloning from reference audio:
610
611\`\`\`json
612{
613  "input": "Hello, this is a cloned voice",
614  "task_type": "Base",
615  "ref_audio": "https://example.com/reference.wav",
616  "ref_text": "Original transcript of the reference audio"
617}
618\`\`\`
619
620All three calls look completion-like and generate text-to-audio responses but the response semantics are different because the voice source is different.

621For CustomVoice, the voice source is a named or predefined speaker. For VoiceDesign, the voice source is a natural-language description. For Base, the voice source is reference audio.
622If the adapter only returns bytes, playback works but the response no longer explains which kind of speech-generation operation happened or even all of the artifacts (like the id for a persistent voice clone) which might have been produced.
623
624## Usage and Pricing Semantics
625
626Usage was the hardest layer to normalize because it sits between API design and pricing.
627For text chat, the classic usage object is fairly easy to understand being composed principally of prompt tokens, completion tokens, and total tokens (and putting aside the tokenization differences between different models).
628Those fields map reasonably well to input text, output text, and total model-token volume.
629
630That model becomes less clean for multimodal APIs.
631A multimodal request may include text, audio, images, video, files, tool results, cached context, or some mix of those.
632A multimodal response may produce text, audio, images, video, structured data, or intermediate reasoning.
633Those inputs and outputs can have very different computational and caching profiles.
634
635So rather than just answering "how many tokens" the usage object has to answer questions like:
636
637- Was this input or output?
638- Which modality was counted?
639- Was the quantity measured in tokens, seconds, characters, images, frames, bytes, credits, or another unit?
640- If the provider reports media tokens, what does one token mean?
641- If the provider reports seconds, frames, or generated media count, what compute detail is being hidden?
642- Is the usage object meant for user display, provider billing, routing decisions, or all three?
643
644OpenRouter is a useful example because it keeps an OpenAI-style chat surface while adding more usage and cost detail.
645Its usage accounting can include prompt tokens, completion tokens, reasoning tokens, cached tokens, cost, upstream inference cost, and token counts calculated with the model's native tokenizer.
646
647\`\`\`json
648// OpenRouter usage, simplified from its documented shape
649{
650  "usage": {
651    "prompt_tokens": 194,
652    "completion_tokens": 2,
653    "total_tokens": 196,
654    "prompt_tokens_details": {
655      "cached_tokens": 0,
656      "cache_write_tokens": 100,
657      "audio_tokens": 0
658    },
659    "completion_tokens_details": {
660      "reasoning_tokens": 0
661    },
662    "cost": 0.95,
663    "cost_details": {
664      "upstream_inference_cost": 19
665    }
666  }
667}
668\`\`\`
669
670OpenAI's chat usage shape also has more detail than the old three-field summary.
671It can include nested detail objects for cached tokens, audio tokens, reasoning tokens, and accepted or rejected prediction tokens.
672
673\`\`\`json
674// OpenAI-style chat usage, simplified
675{
676  "usage": {
677    "prompt_tokens": 1200,
678    "completion_tokens": 340,
679    "total_tokens": 1540,
680    "prompt_tokens_details": {
681      "cached_tokens": 800,
682      "audio_tokens": 0
683    },
684    "completion_tokens_details": {
685      "reasoning_tokens": 0,
686      "audio_tokens": 0
687    }
688  }
689}
690\`\`\`
691
692That helps, but the structure is still anchored around prompt and completion tokens.
693Once the output is not text, the words \`prompt\` and \`completion\` start to carry less information.
694A text output token, an audio output token, and a generated image are all output-side usage.
695They are not the same kind of thing.
696
697Take a look at OpenAI's Realtime API usage response:
698Its usage object can break input and output tokens into modality-specific detail buckets, including text, audio, image, and cached tokens.
699
700\`\`\`json
701// OpenAI Realtime usage, simplified
702{
703  "usage": {
704    "total_tokens": 253,
705    "input_tokens": 132,
706    "output_tokens": 121,
707    "input_token_details": {
708      "text_tokens": 119,
709      "audio_tokens": 13,
710      "image_tokens": 0,
711      "cached_tokens": 64
712    },
713    "output_token_details": {
714      "text_tokens": 30,
715      "audio_tokens": 91
716    }
717  }
718}
719\`\`\`
720
721That is closer to what a user really needs.
722It separates input from output and gives modality-specific detail but it also shows the pricing problem.
723
724OpenAI's Realtime api computes input tokens at a ratio of 100ms audio / token but output at 50ms audio / token.
725Others, like Alibaba, have variable conversion rates based on the referenced model.
726This ambiguity makes interrogating usage objects particularly tricky, particularly when comparing across model providers.
727
728This was a broad pattern which emerges across multimodal pricing.
729The lack of structured units or standardized output objects make it very difficult to apply consistent output schemas especially when dealing with more custom model behaviors like the creation or usage of custom voices.
730This issue becomes especially pronounced when the visible API unit and the pricing unit are not always the same.
731
732Seconds are easy for users to understand even though they are often an incomplete proxy for actual provider cost.
733A five-second audio output can vary by model, sample rate, codec, voice settings, and generation path.
734A five-second video can vary by resolution, frame rate, architecture, denoising steps, and batching behavior.
735One generated image can be cheap or expensive depending on resolution, model architecture, quality tier, and generation settings.
736
737With newer serving architectures like vLLM-Omni make this more than simple bookkeeping.
738Any-to-any multimodal serving work increasingly describes these systems as multi-stage pipelines that can combine autoregressive LLM stages, diffusion stages, encoders, vocoders, and connector logic between stages.
739Each of these involve different underlying caching behaviors and compute profiles and different proxies can be more or less relevant depending on the balance amongst them.
740
741For the caller, the result may be one audio response but for the provider, that response may have crossed several components before it was eventually returned.
742That is why usage standardization is harder than adding a few fields to \`total_tokens\`.
743
744## Usage Shape
745
746The structure we kept wanting was itemized.
747Instead of making \`completion_tokens\` carry every output concept, usage could be reported as typed input and output items with explicit units.
748
749\`\`\`jsonc
750{
751  "usage": {
752    "inputs": [
753      {
754        "type": "text",
755        "unit": "token",
756        "quantity": 1200
757      },
758      {
759        "type": "audio",
760        "unit": "second",
761        "quantity": 12.4,
762        "provider_units": [
763          {
764            "unit": "audio_token",
765            "quantity": 124,
766            "unit_duration_ms": 100
767          }
768        ]
769      }
770    ],
771    "outputs": [
772      {
773        "type": "text",
774        "unit": "token",
775        "quantity": 340
776      },
777      {
778        "type": "audio",
779        "unit": "second",
780        "quantity": 8.7,
781        "provider_units": [
782          {
783            "unit": "audio_token",
784            "quantity": 174,
785            "unit_duration_ms": 50
786          }
787        ]
788      },
789      {
790        "type": "image",
791        "unit": "image",
792        "quantity": 1,
793        "attributes": {
794          "width": 1024,
795          "height": 1024
796        }
797      }
798    ],
799    "totals": [
800      {
801        "unit": "token",
802        "quantity": 1878
803      }
804    ]
805  }
806}
807\`\`\`
808
809The important fields are small:
810
811\`\`\`jsonc
812{
813  "type": "audio",
814  "unit": "second",
815  "quantity": 8.7
816}
817\`\`\`
818
819\`type\` preserves the modality.
820\`unit\` says how the quantity should be read.
821\`quantity\` gives the count.
822\`provider_units\` gives the provider room to expose native accounting units without making those units look universal.
823
824With that structure, a text-only chat response can still produce familiar derived fields.
825\`completion_tokens\` can be derived from \`outputs[type=text, unit=token]\`.
826A speech response can expose seconds for user-facing display and audio tokens for provider-native billing.
827An image response can expose image count and resolution without pretending the output was a text completion.
828
829This does not require every provider to expose the same internals.
830It only asks each usage item to say what was counted and how it was counted.
831To be clear this is still a work in progress on our end as well but our goal is to make this as transparent as possible for the end user.
832
833## Adapter Design
834
835The implementation pattern that ended up working was not to choose one provider shape as the internal standard.
836Instead, we defined canonical objects for the three parts of the interaction:
837
838- request
839- response
840- usage
841
842Each provider integration then became a pair of translators around those canonical objects.
843
844On the way in, the integration accepts the provider's external shape and converts it into the canonical request.
845
846\`\`\`text
847Hugging Face request
848-> Hugging Face request parser
849-> canonical request
850-> model execution
851\`\`\`
852
853On the way out, the process runs in the other direction, but with one important addition.
854
855When the model response is received, we compute canonical usage from the execution result.
856Then we convert both the canonical response and the canonical usage into the provider-specific response shape.
857
858\`\`\`text
859model execution result
860-> canonical response
861-> canonical usage
862-> Hugging Face response formatter
863->

863 Hugging Face-shaped response
864\`\`\`
865
866For a Hugging Face-style integration, the full path looks roughly like this:
867
868::figure{src="/blog/adapter-canonical-pipeline.svg" caption="Each adapter translates between its provider's vernacular and the canonical layer. The model only ever sees canonical objects; provider-specific shapes stay at the edge." width="wide" alt="Three-band architecture diagram. Top band: Hugging Face request and response. Middle band: canonical request and canonical response plus usage. Bottom band: execute and model result. Arrows show parsing down from the edge into canonical, forwarding into the model, and formatting back up to the provider response."}
869
870That structure made the compatibility problem easier to reason about.
871
872The Hugging Face adapter did not need the model backend to speak Hugging Face.
873The OpenRouter adapter did not need the model backend to speak OpenRouter.
874The OpenAI-compatible adapter did not need every internal task to behave like an OpenAI chat completion.
875Each external integration only needed to translate between its own vernacular and the canonical layer.
876
877This also kept request, response, and usage separate.
878
879A provider's request shape might be easy to support while its response shape required task-specific metadata.
880A provider's response shape might be easy to produce while its usage object required provider-specific accounting fields.
881A provider might use familiar chat-style request and response objects while still needing usage fields that did not map cleanly onto \`prompt_tokens\`, \`completion_tokens\`, and \`total_tokens\`.
882The canonical layer gave us a place to preserve the richer internal meaning before projecting it back into a provider-specific shape.
883
884For requests, the canonical object had to describe the task without losing modality-specific inputs.
885
886\`\`\`json
887{
888  "task": "speech_generation",
889  "inputs": [
890    {
891      "type": "text",
892      "text": "Hello world"
893    }
894  ],
895  "parameters": {
896    "voice_source": {
897      "type": "named_voice",
898      "voice": "vivian"
899    },
900    "format": "wav"
901  }
902}
903\`\`\`
904
905For responses, the canonical object had to describe what the model actually produced, including any task variant or capability boundary.
906
907\`\`\`json
908{
909  "type": "speech",
910  "outputs": [
911    {
912      "type": "audio",
913      "format": "wav",
914      "data": "<binary or url>"
915    }
916  ],
917  "metadata": {
918    "task_type": "CustomVoice",
919    "voice_source": {
920      "type": "named_voice",
921      "voice": "vivian"
922    }
923  }
924}
925\`\`\`
926
927If the caller expects an OpenAI-style response, the adapter can derive something like \`completion_tokens\` where that field makes sense.
928If the caller expects an OpenRouter-style response, the adapter can include cost and provider-specific usage details.
929If the caller expects a Hugging Face task response, the adapter can return the task-shaped result while still computing usage internally in the same canonical format.
930
931The point is not that every provider should expose the canonical object directly, rather that the adapter layer needs some representation that is more stable than any one provider's API shape.
932
933Provider-specific shapes are edge contracts.
934The canonical objects are our internal contracts.
935
936When a new provider had a different request body, we wrote a new request translator.
937When a provider expected a different response shape, we wrote a new response formatter.
938When a provider used different usage units, we mapped from canonical usage into that provider's usage vocabulary.
939This way the model backend never needed to know which external API shape the caller used and because the responses were always converted at the edge our internal logic could be consistently structured around coherent and well defined typed objects.
940
941## Takeaways
942
943The adapter work ended up being less about choosing one provider's API as the internal standard and more about deciding where translation should happen.
944
945For chat-shaped APIs, the translation layer can be fairly thin.
946The external request, internal request, model call, internal response, and external response all roughly share the same structure.
947There are still differences, but the basic contract is familiar enough that an adapter mostly has to handle naming, validation, and a few provider-specific fields.
948
949Multimodal APIs required a thicker adapter layer.
950
951The request shape had to preserve the caller's external API contract.
952A Hugging Face-style request should be accepted as Hugging Face-sh

952aped input.
953An OpenAI-compatible request should be accepted as OpenAI-shaped input.
954A task-native speech or image request should be accepted in the form that makes sense for that task.
955
956Internally, though, those requests needed to become canonical requests.
957That gave the model backend one representation to execute against instead of forcing every model path to understand every provider's vocabulary.
958
959The same pattern applied on the way out.
960The model result became a canonical response.
961Usage was computed in a canonical form.
962Then the adapter converted the canonical response and canonical usage back into the external shape the caller expected.
963
964That architecture made the implementation tractable, but it also made the standardization gap clearer.
965
966Where for chat, the provider-facing contract is usually standardized around openai completions, for multimodal APIs, canonicalization becomes part of the core implementation.
967The external API may look familiar while the model path depends on task variants, optional sidecar models, response capabilities, or serving-mode constraints.
968The response may contain the same final artifact while carrying different semantic meaning.
969The usage object may contain a familiar token field while relying on a provider-specific unit definition.
970
971Pricing made that last point the most visible.
972A single product surface may need to price text tokens, cached tokens, reasoning tokens, audio seconds, audio tokens, generated images, video duration, frames, resolution, or provider credits and those units truly are not interchangeable.
973Some are user-readable.
974Some are provider-native.
975Some are closer to compute.
976Some are only useful after applying a conversion rule.
977
978So the practical takeaway is not that every multimodal API should converge on one request body.
979The more useful target is a clearer separation between edge contracts and internal contracts.
980
981External integrations can speak the provider's vernacular.
982The internal system can normalize request, response, and usage separately.
983Then each adapter can project the canonical objects back into the shape the caller expects.
984
985That is roughly where multimodal API compatibility seems to be today.
986It can be implemented, but it requires explicit translation.
987The request and response contracts are still less uniform than chat.
988The usage and pricing contract is even less uniform because the units themselves vary by modality, provider, and model path.
989
990A usage schema built around \`type\`, \`unit\`, and \`quantity\` would not fully remove that complexity but it would make the accounting layer easier to reason about for end users.
991Our advice to anyone building across gateways or providers is to normalize internally, preserve provider semantics at the edge, and avoid treating request compatibility, response compatibility, and usage compatibility as the same problem.
992`},{slug:`voice-agent-economics-in-2026`,title:`Voice Agent Economics in 2026`,category:`Architecture`,excerpt:`Native speech-to-speech now runs 3â12Ã cheaper than cascaded STT â LLM â TTS stacks at comparable latency and task success. Here's when to pick native, when cascades still win, and what changed.`,heroImage:`/blog/voice-agent-economics-hero.webp`,heroImageFit:`cover`,author:{name:`Ian Eaves`,initials:`IE`,role:`Founder`,avatar:`/team/ian-eaves.webp`},date:`2026-06-01`,description:`How native and cascaded voice agents compare on cost, latency, and control, with provider pricing as of May 2026.`,content:`
993For most enterprises, cascaded architectures remain the workhorse strategy for building voice agents.
994
995A user speaks. The system transcribes the audio with a speech-to-text model. The transcript is passed to an LLM, which reasons, calls tools, retrieves data, or updates state. The response is then sent to a text-to-speech model, which generates audio back to the user.
996
997::figure{src="/blog/cascade-vs-native.svg" caption="Cascaded voice agents pass audio through four model boundaries. A native multimodal agent collapses STT, reasoning, and TTS into one." width="wide" alt="Diagram comparing a four-stage cascaded voice agent (user audio, STT, LLM, TTS, agent audio) against a one-model native multimodal agent (user audio, multimodal model, agent audio)"}
998
999That architecture, though complex, remains valuable. It is modular, observable, and easy to control. Enterprise buyers often prefer cascades because each stage c

999an be upgraded, instrumented, and governed independently. A team can swap the STT model, change the reasoning model, replace the TTS provider, add compliance checks between stages, or route a request into a specialist control loop.
1000
1001Direct speech-to-speech architectures have been available for some time, but they have historically struggled with the reasoning, tool use, and control requirements that traditional LLMs support in cascaded systems. Native speech-to-speech agents now appear to be crossing that practical threshold. For a growing set of applications, especially low-latency and multimodal ones, a single speech/vision/text model can now be the better choice.
1002
1003The right architecture depends on the balance of price, latency, visual grounding, tool reliability, control, and engineering simplicity.
1004
1005For many hobbyist, consumer, startup, and multimodal applications, native speech-to-speech is now the strongest option. For enterprise workflows that maximize accuracy, auditability, tool calling, and deterministic control, cascades will continue to coexist with native agents.
1006
1007## What changed
1008
1009Open models like Qwen3-Omni now make native speech-to-speech practical for workloads that also require vision, text, or video understanding.
1010
1011Qwen3-Omni uses a ThinkerâTalker Mixture-of-Experts architecture. The Thinker handles multimodal understanding and text generation. The Talker generates streaming speech tokens. The architecture unifies perception and generation across text, images, audio, and video, and supports speech output in addition to text output. Qwen3-Omni supports text interaction in 119 languages, speech understanding in 19 languages, and speech generation in 10 languages. It also uses multi-codebook speech codec prediction and a lightweight causal ConvNet for streaming speech generation, with a theoretical cold-start first-packet latency of 234 ms. [arXiv](https://arxiv.org/abs/2509.17765)
1012
1013Sprag hosts Qwen3-Omni-30B-A3B-Instruct for text, image, audio, and video workflows, with text and audio outputs available through OpenAI-compatible chat and responses-style requests. [Sprag](/models/qwen/qwen3-omni-30b-a3b-instruct)
1014
1015Now, a native multimodal model can listen to a user, interpret an image or video frame, reason over text context, and respond directly in speech. The builder no longer has to manage state across separate STT, LLM, vision, and TTS systems on every turn.
1016
1017The strongest practical benefits are:
1018
1019- lower price
1020- lower latency
1021- native visual grounding
1022- reduced engineering surface area
1023- simpler architecture
1024
1025Traditional cascades still have advantages. They can use the strongest specialized tool-calling models and the most polished standalone TTS systems. Their control points are better understood and their failure modes are easier to isolate. Still, the architectural frontier has moved far enough that native speech-to-speech deserves direct evaluation.
1026
1027## Why vLLM-Omni-style serving matters
1028
1029A native multimodal model still needs the right serving stack. A simple implementation can create hidden sequential bottlenecks: process the user's speech, finish reasoning, prepare speech tokens, synthesize waveform audio, then return the response. That shape recreates some of the pipeline latency inside the model stack.
1030
1031vLLM-Omni addresses this serving problem for any-to-any multimodal models. It introduces a stage abstraction that decomposes complex multimodal architectures into interconnected stages, with independent execution backends, per-stage batching, flexible GPU allocation, and connectors for routing data across stages. In the vLLM-Omni evaluation, this serving approach reduces job completion time by up to 91.4% compared with baseline methods. [arXiv](https://arxiv.org/abs/2602.02204)
1032
1033For Qwen3-Omni, the serving path maps naturally onto three stages:
1034
1035- **Thinker:** multimodal understanding and text/semantic generation
1036- **Talker:** semantic representation to speech codec codes
1037- **Code2Wav:** speech codec codes to audio waveform
1038
1039::figure{src="/blog/vllm-omni-stages.svg" caption="The Qwen3-Omni request decomposes into three vLLM-Omni stages that batch and stream independently." width="wide" alt="Three connected stages â Thinker, Talker, Code2Wav â showing how a Qwen3-Omni request flows through vLLM-Omni with each stage batched and allocated independently"}
1040
1041The serving stack can move work through the speech-generation path while upstream stages continue processing. Each stage c

1041an be batched and allocated independently rather than forcing the entire request through one monolithic execution path.
1042
1043vLLM-Omni-style serving helps turn native speech-to-speech from a model capability into a production serving architecture. Sprag runs Qwen3-Omni on top of vLLM-Omni, which makes the low-latency and cost profile practically relevant rather than merely theoretical.
1044
1045## Benchmarking the tradeoff
1046
1047Sprag benchmarks voice systems across STT, TTS, and agentic workflows. Agentic benchmarks compare native speech-to-speech agents against staged transcribe â reason â speak systems using provider APIs. [Sprag Benchmarks](/benchmarks?tab=agentic)
1048
1049In our measurements, Sprag-hosted Qwen3-Omni-30B is highly competitive on time to first audio and wins overall on end-to-end latency across the benchmarked agentic task.
1050
1051::figure{src="/blog/s2s-agentic-latency.webp" caption="TTFA p50 across the eight benchmarked pipelines. Sprag / Qwen3-Omni-30B lands near the fastest cluster while still running a full agent loop." width="wide" alt="Agentic TTFA p50 latency across pipelines, with Sprag's Qwen3-Omni-30B near the fastest at 1176 ms"}
1052
1053The TTFA distribution shows Sprag / Qwen3-Omni-30B with a max TTFA of **1.176 seconds**. Comparable systems in the chart include Cartesia Ink + Sonic 3 at **1.445 seconds**, OpenAI GPT Audio at **1.759 seconds**, ElevenLabs Scribe v2 + TTS at **2.093 seconds**, Deepgram Voice Agent at **2.913 seconds**, AssemblyAI Voice Agent at **3.000 seconds**, and Deepgram Nova-3 + Aura-2 at **4.373 seconds**.
1054
1055Speed alone is not the full story. The scatter below plots end-to-end latency against task success on the agentic benchmark. Top-left is the quadrant that matters: fast AND actually completes the turn. Several systems start talking quickly but fail the underlying task; Qwen3-Omni-30B is the only system in the set that pairs sub-second responsiveness with high task success.
1056
1057::figure{src="/blog/s2s-latency-vs-task-success.webp" caption="Latency vs task success across the benchmarked pipelines. Top-left is fastest and most successful. Qwen3-Omni-30B is the only system in that quadrant." width="wide" alt="Latency vs task success scatter plot, with Sprag's Qwen3-Omni-30B sitting in the top-left fastest-and-most-successful quadrant"}
1058
1059The latency result matters because voice interfaces are highly sensitive to delay. A sub-second or near-sub-second response changes the feel of the product. The user experiences the agent less like a web request and more like an interactive system.
1060
1061## Pricing changes the architecture discussion
1062
1063Cost is one of the clearest reasons native speech-to-speech is becoming interesting for buyers.
1064
1065A cascaded voice agent pays for multiple components:
1066
1067- speech-to-text
1068- LLM reasoning and tool use
1069- text-to-speech
1070- orchestration and monitoring
1071
1072A native speech-to-speech model can collapse much of that stack into a single multimodal serving path. Actual bills still depend on speech ratio, silence handling, context length, tool schemas, retries, provider-specific billing rules, image/video use, and generated response length.
1073
1074Using Sprag's current Qwen3-Omni rate card:
1075
1076- Audio input: **$0.00255 / minute**
1077- Audio output: **$0.0102 / minute**
1078
1079For a normalized **50/50 call-minute** â thirty seconds of user speech and thirty seconds of generated agent speech â the audio cost is:
1080
1081**0.5 Ã $0.00255 + 0.5 Ã $0.0102 = $0.006375**
1082
1083So the Sprag/Qwen3-Omni audio cost is approximately **$0.0064 per 50/50 call-minute**.
1084
1085For cascades, the full cost of an agent round trip includes STT, LLM, and TTS. The table below uses a conservative GPT-4o-class reasoning estimate: **1,000 input tokens + 250 output tokens per call-minute â $0.005 per call-minute**. GPT-4o is priced at $2.50 per 1M input tokens and $10.00 per 1M output tokens. [OpenAI Developers](https://developers.openai.com/api/docs/models/gpt-4o)
1086
1087That LLM estimate can increase with long system prompts, tool schemas, RAG context, conversation history, retries, and verbose tool outputs.
1088
1089Pricing as of May 2026:
1090
1091| Stack | Cost basis | Approx. 50/50 call-minute | Multiple vs. Sprag |
1092| :-: | --: | --: | --: |
1093| **Sprag / Qwen3-Omni-30B** | Native audio input + audio output | **$0.0064** | **1.0Ã** |
1094| **Cartesia Ink + Sonic 3 + GPT-4o-class LLM** | STT + TTS + LLM | **$0.0195** | **3.1Ã** |
1095| **Deepgram Nova-3 + Aura-2 + GPT-4o-class LLM** | STT + TTS + LLM | **$0.0224** | **3.5Ã** |
1096| **ElevenLabs Scribe v2 Realtime + Flash/Turbo + GPT-4o-class LLM** | STT + TTS + LLM | **$0.0308** | **4.8Ã** |
1097| **OpenAI GPT Audio / GPT-Realtime-2** | Native realtime audio model | **~$0.0480** | **~7.5Ã** |
1098| **Deepgram Voice Agent API** | Bundled voice-agent API | **$0.0750** | **11.8Ã** |
1099| **AssemblyAI Voice Agent API** | Bundled voice-agent API | **$0.0750** | **11.8Ã** |
1100
1101The table uses provider-published pricing and a normalized call-minute. Cartesia lists Scale annual pricing at $239/month with 8M model credits, Sonic at 1 credit per character, and Ink at 1 credit per second of audio, with Ink described as $0.13/hr on Scale. [Cartesia](https://cartesia.ai/pricing) Deepgram lists Nova-3 streaming rates, Aura-2 at $0.030 per 1K characters, and Voice Agent API Standard at $0.075/minute based on WebSocket connection time. [Deepgram](https://deepgram.com/pricing) ElevenLabs lists Scribe v2 Realtime at $0.39/hour and Flash/Turbo TTS at $0.05 per 1K characters. [ElevenLabs](https://elevenlabs.io/pricing/api) AssemblyAI lists its Voice Agent API at $4.50/hr. [AssemblyAI](https://www.assemblyai.com/products/voice-agent-api) OpenAI lists GPT-Realtime-2 audio at $32/M input audio tokens and $64/M output audio tokens, and its realtime cost guide documents user audio at 1 token per 100 ms and assistant audio at 1 token per 50 ms. [OpenAI](https://

1101openai.com/api/pricing/)
1102
1103Pricing comparisons favor native speech-to-speech even before accounting for engineering surface area. A pipeline can still be cost-effective with inexpensive STT and TTS components, especially at low volume or with a cheaper LLM. At scale, the combined cost of STT, LLM, TTS, orchestration, logging, retries, and integration work becomes part of the architectural decision.
1104
1105## Tool calling is now viable in native speech-to-speech
1106
1107Historical skepticism toward direct speech-to-speech agents was justified. Early systems were fast and natural but difficult to control. Enterprise applications often need structured tool calls, strict validation, audit logs, policy checks, database access, and deterministic routing. Native speech-to-speech systems are improving on those dimensions.
1108
1109Qwen3-Omni supports native function-calling workflows, and its architecture leaves room for control. Because the Thinker and Talker are decoupled, external modules such as RAG, function calling, and safety filters can intervene on the Thinker's textual output before speech synthesis. [arXiv](https://arxiv.org/abs/2509.17765)
1110
1111That makes native speech-to-speech with tools a real option. The practical enterprise question becomes:
1112
1113:::callout{type="insight"}
1114Does this workflow require best-in-class tool-calling reliability and orchestration control, or is good tool calling enough given the latency, cost, and architecture benefits?
1115:::
1116
1117For many use cases, good tool calling is enough. A consumer assistant, visual troubleshooting agent, robotics-adjacent interface, or lightweight support workflow may benefit more from native multimodality and low latency than from maximum orchestration control.
1118
1119For workflows involving inventory databases, order-management systems, EHRs, regulated actions, or complicated escalation paths, cascades remain attractive. A staged architecture gives teams explicit checkpoints: transcript, LLM request, tool call, tool result, policy decision, final response, speech output.
1120
1121## Why cascades will continue to matter
1122
1123For enterprise buyers, cascades retain several advantages.
1124
1125First, they are flexible. A team can upgrade the reasoning model when a better LLM becomes available. They can replace the voice model without changing the tool loop. They can tune STT separately for domain vocabulary. They can add validation, redaction, human review, or compliance checks between components.
1126
1127Second, cascades are easier to instrument. Each stage produces artifacts that can be logged, tested, replayed, and evaluated separately. Teams can measure transcription accuracy, tool-call validity, policy compliance, TTS latency, and end-to-end user experience with clear attribution.
1128
1129Third, cascades support complicated control loops. Consider an order-management assistant that has to identify a customer, inspect inventory, check refund eligibility, update a record, detect regulated cases, and escalate to a specialist agent. In a cascade, routing decisions can happen between components. The system can redirect the request before generating the final spoken response.
1130
1131Native speech-to-speech systems can support many of these patterns, especially when they expose transcripts, text outputs, and tool-call surfaces. The tooling is improving quickly. Enterprise teams still need to evaluate the maturity of the runtime control layer, especially for mid-turn redirection, policy gates, audit trails, and multi-agent handoff.
1132
1133## Native multimodality matters beyond voice
1134
1135Voice plus vision is one of the strongest reasons to care about native speech-to-speech.
1136
1137A user might speak while showing the agent an object, a document, a product, a damaged part, or a live environment. A cascade can support this by separately capturing an image, summarizing it, inserting that summary into a prompt, and then generating speech. That approach works, but every modality handoff adds orchestration complexity.
1138
1139Native multimodal models offer a cleaner abstraction. The same model can hear the instruction, see the relevant visual context, use text or tool context, and generate a spoken response with major practical advantages:
1140
1141- shared context across speech, vision, video, and text
1142- fewer modality handoffs
1143- less application glue
1144- faster prototyping
1145- cleaner multimodal state management
1146
1147Robotics and embodied assistants are a natural forward-looking example. A robot or field assistant needs to jointly interpret what it sees and what a person says. Commands like "pick up the red connector next to the loose cable" or "is this part installed correctly?" require visual grounding and spoken interaction at the same time.
1148

1149A staged architecture can be made to support that interaction. Native speech + vision is a more direct model of the task.
1150
1151## How buyers should choose
1152
1153Native speech-to-speech should be favored when:
1154
1155- low latency matters
1156- cost matters
1157- voice + vision matter
1158- architecture simplicity matters
1159- the product is highly interactive
1160- good tool calling is sufficient
1161- the team wants to reduce orchestration work
1162
1163Cascades should be favored when:
1164
1165- best-in-class tool calling is mandatory
1166- strict auditability is required
1167- deterministic control matters
1168- compliance checkpoints are central
1169- each model component needs to be swapped independently
1170- the workflow depends on complex routing across tools, policies, databases, and specialist agents
1171
1172Hobbyists and consumer-app builders will often get the most immediate value from native speech-to-speech. Low latency, simpler architecture, lower price, and native voice + vision support make it easier to build compelling products quickly.
1173
1174Startup builders care about time to market and engineering surface area. A cascade requires teams to select, integrate, prompt, monitor, and debug multiple providers. A native multimodal agent collapses much of that application layer.
1175
1176Enterprise buyers will need to be more selective. Accuracy, tool calling, auditability, and control tend to dominate. Native speech-to-speech becomes compelling when the workflow can tolerate good tool calling in exchange for lower latency, lower cost, and simpler multimodal architecture.
1177
1178## The takeaway
1179
1180Speech-to-speech agents are finally viable.
1181
1182Cascades will continue to be the right architecture for many enterprise systems. Their strengths are real: control, modularity, auditability, and access to the strongest specialized components.
1183
1184Native multimodal speech agents now deserve a serious place in the architecture discussion. Models like Qwen3-Omni, served through vLLM-Omni-style infrastru

1184cture, can deliver low-latency speech interaction, native visual grounding, tool-capable workflows, and a materially cheaper cost profile.
1185
1186If you're building voice agents, the next step is to benchmark native speech-to-speech and cascaded agents against your actual workloads. Sprag hosts Qwen3-Omni for text, image, audio, and video workflows, with text and audio outputs through OpenAI-compatible chat and responses-style endpoints. If you want to test native agents against your workload, we would be happy to help.
1187`},{slug:`speech-to-speech-vs-pipelined-voice-agents`,title:`Speech-to-Speech vs Pipelined Voice Agents`,category:`Benchmarks`,excerpt:`We benchmarked Qwen3-Omni against traditional ASR â LLM â TTS pipelines on real voice-agent workloads. For the first time, the open native path actually looks preferable.`,heroImage:`/blog/s2s-hero.webp`,heroImageFit:`cover`,author:{name:`Ian Eaves`,initials:`IE`,role:`Founder`,avatar:`/team/ian-eaves.webp`},date:`2026-04-24`,description:`Sprag benchmarks Qwen3-Omni against traditional ASR, LLM, and TTS voice-agent pipelines.`,content:`
1188## Introduction
1189
1190We've been talking to companies building voice agent workflows (that is to say, a user speaks and the agent responds with speech) about how they've architected their agent systems. The standard system follows a pipelined loop that looks something like this.
1191
1192**user speech â ASR â transcript â LLM/tool loop â TTS â audio response**
1193
1194Theoretically natively multimodal models with the internal machinery to convert speech directly back to speech should have a latency advantage over pipelined approaches but historically most companies still prefer the pipelined solution. Although more complicated, they have a few distinct advantages:
1195
1196* They are modular allowing companies to take advantage of the latest frontier models for the thinking component of the LLM / tool loop.
1197
1198* Generating raw transcripts simplifies compliance requirements
1199
1200* Serving infrastructure for multimodal models was much less mature
1201
1202The tradeoff is, of course, speed; each call boundary adds latency and implementation complexity to the final response. A lot has changed in the past year with major developments both in the quality of open source multimodal models like Qwen3-Omni and the infrastructure necessary to efficiently serve them with vLLM-Omni so we wanted to know how a native multimodal model would stack up against traditional approaches from frontier labs.
1203
1204## Methodology
1205
1206**STT:** Samples were drawn from the LibriSpeech dataset with each run evaluating the same utterances across STT models. Reference comparisons were drawn from the attached transcripts for each sample.
1207
1208**TTS:** Samples were drawn from LibriTTS-R, with each run evaluating the same prompts across TTS models. During the verification stage each output speech sample was run through ASR to compute WER/CER against normalized source text.
1209
1210**Agentic:** Samples were drawn from Harper Valley Bank caller turns and reused across all workflow variants. Each workflow produced the next spoken agent reply, either via native speech-to-speech or staged transcribe â answer â speak pipelines. A verifier ASR transcribed the spoken output for transcript correctness, and a provider-backed judge assessed task success for each turn.
1211
1212## Results
1213
1214The headline result is that native speech-to-speech finally feels like more than a demo.
1215
1216::figure{src="/blog/s2s-agentic-latency.webp" caption="TTFA p50 across the eight pipelines we tested. Sprag's Qwen3-Omni-30B lands near the fastest cluster while still running a full agent loop." width="wide" alt="Agentic TTFA p50 latency across pipelines, with Sprag's Qwen3-Omni-30B near the fastest at 1176 ms"}
1217
1218In our benchmarks, Qwen3-Omni provided the best overall balance between latency and quality as measured by task success. It combined the strongest task-level score in the set with the lowest average end-to-end latency, and it did so without the dropped-turn failures that showed up elsewhere.
1219
1220A few systems were able to start talking very quickly, but "starts talking" and "successfully completes the turn" were often very different things. The end-to-e

1220nd Cartesia agent is a good example: it was responsive and sounded polished, but it mostly failed the underlying banking tasks. The stronger multistage pipelines were more balanced, but they still paid the usual tax for going through transcription, text reasoning, and synthesis as separate steps.
1221
1222::figure{src="/blog/s2s-latency-vs-task-success.webp" caption="Latency vs task success. Top-left is fastest and most successful. Qwen3-Omni-30B is the only system in that quadrant." width="wide" alt="Latency vs task success scatter plot, with Sprag's Qwen3-Omni-30B sitting in the top-left fastest-and-most-successful quadrant"}
1223
1224:::callout{type="insight"}
1225Fast-talking is not the same as turn-completion. Most of the sub-second systems in this set lost significant task-success ground relative to Qwen3-Omni, which is the pairing worth optimizing for.
1226:::
1227
1228So the interesting outcome here isn't that pipelines are suddenly obsolete. It's that, for the first time, the open native path actually looks preferable. If you care about realtime interaction, the extra boundaries in a transcribe â answer â speak stack are no longer obviously buying enough to justify themselves.
1229
1230## Why vLLM-Omni matters
1231
1232Qwen3-Omni is the kind of model that makes this shift possible: it is a natively end-to-end omni model that can take text, images, audio, and video and stream back both text and natural speech, and the Qwen project explicitly recommends the latest vLLM-Omni for deploying the Qwen3-Omni series.
1233
1234But the model is only half the story. The reason vLLM-Omni changes the game is that it turns multimodal serving into infrastructure instead of bespoke glue code. The project extends vLLM beyond text-only generation into text, image, video, and audio workloads, supports streaming outputs and an OpenAI-compatible API server, and introduces a stage-oriented, disaggregated execution model for more complex multimodal systems.
1235
1236That matters for direct agent to agent workflows because every forced conversion back into text is a design concession. If agent A has to listen, transcribe, reason, synthesize, and then hand audio off to agent B so B can transcribe it again, the system keeps paying serialization tax at every hop. A speech-native model lets speech be the medium rather than a temporary encoding, and a serving layer like vLLM-Omni gives that setup a shape you can actually operate.
1237
1238## Takeaway
1239
1240Pipelines still make sense when you want to swap best-of-breed components independently, or when your text model materially outperforms your native speech model. But if the question is whether open multimodal stacks are ready for serious realtime workflows, this benchmark pushed me from "not quite" to "yes, probably." Qwen3-Omni is what makes direct speech-in / speech-out agents look viable. vLLM-Omni is what makes them look deployable. And once those two things are true, direct agent to agent workflows stop sounding like a research demo and start looking like the default architecture.
1241`},{slug:`introducing-sprag`,title:`Introducing Sprag: Unified Multimodal AI Inference`,category:`Announcement`,excerpt:`Today we're launching Sprag â a single API for text, image, audio, and video inference. One endpoint, every modality, built for speed.`,heroImage:`/brand/sprag-lockup.svg`,author:{name:`Ian Eaves`,initials:`IE`,role:`Founder`,avatar:`/team/ian-eaves.webp`},date:`2026-03-27`,description:`Sprag is a unified multimodal AI inference API for text, image, audio, and video workloads.`,content:`
1242## Why we built Sprag
1243
1244Running AI inference across multiple modalities today means juggling different providers, SDKs, billing systems, and latency profiles. You need one service for text generation, another for image understanding, a third for audio transcription, and yet another for video analysis. Each comes with its own authentication, rate limits, and failure modes. It's fragile, expensive, and slow to iterate on.
1245
1246We built Sprag to collapse that complexity into a single managed API.
1247
1248Under the hood, Sprag is powered by [vLLM-Omni](https://github.com/the-sprag/vllm-omni) â our extension of the popular vLLM inference engine. What makes vLLM-Omni novel is that it serves text, image, audio, and video modalities through a **single process** with a **unified KV-cache** shared across all modalities. Traditional approaches stitch together separate model pipelines â one process for text, another for vision, another for audio â each with its own memory footprint and scheduling overhead. vLLM-Omni eliminates that overhead entirely by running everything in one engine with shared attention state.
1249
1250The result: lower latency, higher throughput, and a dramatically simpler operational footprint. Sprag wraps this engine into a managed cloud API so you never have to think about GPU scheduling, model loading, or infrastru

1250cture scaling.
1251
1252## What Sprag does
1253
1254- **Multimodal by default.** Text generation, image understanding, audio transcription and synthesis, and video analysis â all through one consistent OpenAI-compatible API.
1255- **Low latency at scale.** Requests are routed to optimised vLLM-Omni instances with shared KV-cache, eliminating cold starts and redundant memory allocation across modalities.
1256- **Simple integration.** A single API key, a single endpoint pattern, and clear documentation. Go from zero to your first inference call in under five minutes.
1257- **Usage-based pricing.** Pay only for what you use. No reserved capacity, no minimums.
1258
1259## Supported models at launch
1260
1261We're launching with the following models, all served through the same API:
1262
1263| Model | Capabilities |
1264|---|---|
1265| \`Qwen/Qwen3-Omni-30B-A3B-Instruct\` | Multimodal â text, audio, image, and video input; text and audio output |
1266| \`Qwen/Qwen-Image-Edit-2511\` | Image editing â instruction-based image manipulation |
1267| \`Qwen/Qwen3-TTS-12Hz-1.7B-CustomVoice\` | Text-to-speech â high-quality synthesis with preset voices |
1268| \`Qwen/Qwen3-TTS-12Hz-1.7B-VoiceDesign\` | Text-to-speech â custom voice design from description |
1269
1270All models are available immediately. We'll be adding more as we validate performance and quality on our infrastructure.
1271
1272## Making your first call
1273
1274The Sprag API follows the OpenAI chat completions format. Here's a simple text generation request:
1275
1276\`\`\`bash
1277curl https://api.sprag.ai/v1/chat/completions \\
1278  -H "Content-Type: application/json" \\
1279  -H "Authorization: Bearer YOUR_API_KEY" \\
1280  -d '{
1281    "model": "Qwen/Qwen3-Omni-30B-A3B-Instruct",
1282    "modalities": ["text"],
1283    "messages": [
1284      {"role": "user", "content": "Hello, how are you?"}
1285    ]
1286  }'
1287\`\`\`
1288
1289## Get started
1290
1291Head over to our [docs](/docs) to grab your API key and make your first request. You can also try things out in the [models](/models) page â no sign-up required for a quick test.
1292
1293We're just getting started and would love your feedback. Reach out on our [contact page](/contact) or drop us a message â we read everything.
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