Patchboard
Catalog

Voice AI models and benchmarks

Every model Patchboard has measured, and the 72 of 72 catalog entries a request can pin today. Accuracy, latency and price as each board published them — nothing on this page is estimated to fill a column.

Alibaba 1Anthropic 2AssemblyAI 1Baseten 4Bland 1Cartesia 3Cerebras 2Deepgram 4ElevenLabs 2Fish Audio 1Gemini 2Gladia 1Google 1Gradium 2Hume 1Inworld 3Maya 1MiniMax 1Modulate 1OpenAI 13Palabra 1Qwen 1Rime 1Smallest 1Smallest AI 1Soniox 3Speechify 1Together 1xAI 4xAI Grok 1
#ModelProviderWER · batchWER · streamFinalize p50Cost / min
1PulseSmallest AI5.1%8.0%178ms~$0.0050
2Nova-3Deepgram9.8%12.9%106ms$0.0048
3FluxDeepgramnot measured6.6%406ms$0.0065
4Ink-2Cartesia11.0%9.9%102ms$0.0090
5Qwen3-ASRAlibaba2.8%4.0%424ms$0.0054
6Chirp 3Google3.9%7.4%581ms$0.0160
7Scribe v2 RealtimeElevenLabsnot measured3.4%233ms$0.0065
8Realtime STT-1Inworld3.3%3.6%139ms$0.0025
9Universal-3.5 ProAssemblyAI2.0%2.0%66ms$0.0075
10Velma 2Modulate4.4%5.4%1.11s$0.0010
11stt-rt-v5Soniox7.5%7.3%78ms$0.0020
12GPT-4o TranscribeOpenAI2.3%5.8%572ms$0.0060
13GPT Live TranscribeOpenAInot measured4.5%1.12s$0.0170
14GPT-4o-mini TranscribeOpenAI2.7%6.4%460ms$0.0030
15Grok STTxAI4.8%10.9%305ms$0.0033
16Gradium ASRGradium8.4%11.7%334ms$0.0104
17Solaria-1Gladia5.0%11.4%596ms$0.0125
Measured per language

22 studies across 11 languages

Naturalness and error rate are measured per language, not inferred from English — the model that wins one study routinely loses another.

Arabic speech-to-text
#ModelCER · batch
1GPT-4o Transcribe2.7%
2Universal-3.5 Pro3.3%
3Qwen3-ASR3.5%
4Qwen3-ASR Realtimenot measured
5Scribe v2 Realtimenot measured
6Grok STTnot measured
7GPT-4o-mini Transcribe4.7%
8S35.0%
9stt-rt-v55.1%
10Nova-37.1%
11Ink-Whisper10.2%
Arabic text-to-speech
#ModelNaturalness · elo
1grok-tts1736.00
2sonic-3.51644.00
3tts-rt-v21612.00
4eleven_v3_conversational1564.00
5inworld-tts-21454.00
Published stacks

What benchmarks.patchboard.online picks for each job

The measurement that decided it. A stack whose legs are identical to another's is one row: the publisher distinguishes four jobs and its boards distinguish two stacks. A stack with a leg Router cannot currently call is not shown at all.

Real-time phone agent
decided on 66ms · 192ms · 116ms
Speech-to-text
Universal-3.5 Pro
assemblyai:universal-3-5-pro
LLM
gemma-4-31b
cerebras:gemma-4-31b
Text-to-speech
inworld-tts-2
inworld:inworld-tts-2
Tool-heavy agent
decided on 2.0% WER · 2.7% tool silence · 116ms
Speech-to-text
Universal-3.5 Pro
assemblyai:universal-3-5-pro
LLM
Claude Haiku 4.5
anthropic:claude-haiku-4-5
Text-to-speech
inworld-tts-2
inworld:inworld-tts-2
Natural conversation
decided on 2.0% WER · 1.6% dead-air · MOS 1590
Speech-to-text
Universal-3.5 Pro
assemblyai:universal-3-5-pro
LLM
Claude Haiku 4.5
anthropic:claude-haiku-4-5
Text-to-speech
eleven_v3_conversational
elevenlabs:eleven_v3_conversational
How to read this

Reading the boards correctly

Every model id is copyable.

An id offered by the current Router catalog can be sent as model; other ids identify benchmark evidence without claiming that the route is currently available. A direct benchmark label means the provider published no Router model id for that row.

Not measured is not zero.

A dash in a column of word error rates reads as a perfect score, so an unmeasured cell says so in words. A leading ~ is the board's own mark for an estimate.

Nothing here re-ranks a board.

The # column indexes the order you sorted into. It is not a verdict.