Last refreshed 2026-10-20. Next refresh: weekly.
Why use Voxtral Mini Transcribe 3 on Vercel AI Gateway?
Vercel AI Gateway offers Voxtral Mini Transcribe 3. Vercel AI Gateway is a unified AI proxy providing a single OpenAI-compatible API endpoint to 275+ models from 25+ providers including Anthropic, OpenAI, Google, Meta, Mistral, DeepSeek, xAI, Alibaba, Amazon, ByteDance, Cohere, MiniMax, MoonshotAI, KwaiPilot, Black Forest Labs, Recraft, Voyage AI, NVIDIA, and more.
Compare Voxtral Mini Transcribe 3 across 2 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport AI_GATEWAY_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],mistral/voxtral-mini-transcribe-3Request example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["AI_GATEWAY_API_KEY"],
base_url="https://ai-gateway.vercel.sh/v1"
)
response = client.chat.completions.create(
model="mistral/voxtral-mini-transcribe-3",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "mistral/voxtral-mini-transcribe-3", not the LLMReference slug "voxtral-mini-transcribe-3".
- creator/model-name e.g. kwaipilot/kat-coder-pro-v2
- The examples expect AI_GATEWAY_API_KEY; rename it only if your application config maps the new variable.
Compare Voxtral Mini Transcribe 3 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Mistral AI Studio | — | — |
| Vercel AI Gateway | — | — |
Capabilities
About Voxtral Mini Transcribe 3
Voxtral Mini Transcribe 3 is Mistral AI's efficient batch speech-to-text model from the Voxtral 3 family, released October 20, 2026 as a Public Preview on the Mistral API. It has 4B parameters and transcribes more than 50 languages, with speaker diarization, word timestamps and context biasing (key terms or free text that improve recall on domain vocabulary without fine-tuning). Mistral ranks it third on its FLEURS multilingual transcription and English diarization benchmarks, ahead of Gemini 3.5 Transcribe. It has a 32k context and costs $0.0035 per audio minute; weights are not published.