--- title: "Explorations | Micdrop" description: "Local models that were measured, timed and left out of the stack, with the reason for each." url: "https://micdrop.dev/docs/ai-integration/local-models/explorations" --- * [Getting Started](/docs/getting-started) * [Client (Browser)](/docs/client) * [Installation](/docs/client/installation) * [React Hooks](/docs/client/react-hooks) * [Start/Stop Call](/docs/client/start-stop-call) * [Pause/Resume Call](/docs/client/pause-resume-call) * [Mute/Unmute Call](/docs/client/mute-unmute-call) * [Call State](/docs/client/call-state) * [Display Conversation Messages](/docs/client/display-conversation-messages) * [Handling Tool Calls](/docs/client/handling-tool-calls) * [Device Management](/docs/client/devices-management) * [Voice Activity Detection (VAD)](/docs/client/vad) * [Error Handling](/docs/client/error-handling) * Utility Classes * [Mic](/docs/client/utility-classes/mic) * [MicdropClient](/docs/client/utility-classes/micdrop-client) * [MicRecorder](/docs/client/utility-classes/mic-recorder) * [Speaker](/docs/client/utility-classes/speaker) * [Server (Node.js)](/docs/server) * [Installation](/docs/server/installation) * [With Fastify](/docs/server/with-fastify) * [With NestJS](/docs/server/with-nestjs) * [Auth and Parameters](/docs/server/auth-and-parameters) * [First Message](/docs/server/first-message) * [Save Messages](/docs/server/save-messages) * [Resume a Conversation](/docs/server/resume-conversation) * [Recording Audio](/docs/server/recording-audio) * [Error Handling](/docs/server/error-handling) * [Tools](/docs/server/tools) * [Extract Value from Answer](/docs/server/extract) * [Auto End Call](/docs/server/auto-end-call) * [Semantic Turn Detection](/docs/server/semantic-turn-detection) * [Noise Filtering](/docs/server/noise-filtering) * [Micdrop Protocol](/docs/server/protocol) * [AI Integrations](/docs/ai-integration) * Provided Integrations * [AI SDK](/docs/ai-integration/provided-integrations/ai-sdk) * [Cartesia](/docs/ai-integration/provided-integrations/cartesia) * [ElevenLabs](/docs/ai-integration/provided-integrations/elevenlabs) * [Gladia](/docs/ai-integration/provided-integrations/gladia) * [Gradium](/docs/ai-integration/provided-integrations/gradium) * [Kokoro](/docs/ai-integration/provided-integrations/kokoro) * [Mistral](/docs/ai-integration/provided-integrations/mistral) * [OpenAI](/docs/ai-integration/provided-integrations/openai) * [Piper](/docs/ai-integration/provided-integrations/piper) * [Pocket TTS](/docs/ai-integration/provided-integrations/pocket-tts) * [Whisper](/docs/ai-integration/provided-integrations/whisper) * Custom Integrations * [Agent (LLM)](/docs/ai-integration/custom-integrations/custom-agent) * [Speech-to-Text (STT)](/docs/ai-integration/custom-integrations/custom-stt) * [Text-to-Speech (TTS)](/docs/ai-integration/custom-integrations/custom-tts) * Fallback Strategies * [FallbackAgent](/docs/ai-integration/fallback-strategies/agent-fallback) * [FallbackSTT](/docs/ai-integration/fallback-strategies/stt-fallback) * [FallbackTTS](/docs/ai-integration/fallback-strategies/tts-fallback) * [Local Models](/docs/ai-integration/local-models) * [Choosing the Models](/docs/ai-integration/local-models/choosing-models) * [Latency and Memory](/docs/ai-integration/local-models/performance) * [Explorations](/docs/ai-integration/local-models/explorations) * [IA Vocale Souveraine ๐Ÿ‡ซ๐Ÿ‡ท๐Ÿ‡ช๐Ÿ‡บ](/docs/ai-integration/sovereign-voice-ai) [Micdrop](/) โ€บ [Documentation](/docs/getting-started) # Explorations Every model on this page was installed, run and timed on a MacBook Pro M2 with 24 GB of memory, then left out of the recommended stack. The measurements are written down so nobody has to run them twice, and the verdicts belong to this class of machine rather than to the models themselves. A server with a large GPU, or a faster runtime on the same weights, can bring any of them back into range. ## The Mistral models A French project asks for a Mistral first, and the family has little to offer at the sizes a personal computer can run. Mistral Small 4 is a mixture of experts of 119 billion parameters, Mistral Medium and Large are heavier still, and the only open weights that fit are Mistral 7B, whose last revision dates from 2024. `ollama-agent.ts`, run against `mistral:7b-instruct-v0.3-q4_K_M` and `qwen3:4b-instruct` on the same prompt and the same machine, gives: The test Mistral 7B v0.3 Qwen3 4B Instruct First token, model already loaded 2.8 to 3.1 s 0.8 to 1.3 s Asking the time, with a `get_time` tool No call, invented `[heure]:[minute]` Tool called, time spoken A meaningless `"euh"` Answered anyway `CancelLastUserMessage` `"Merci, au revoir !"` Started a new offer of help Said goodbye Ollama does list `tools` among the capabilities of that model, and the model still mishandles the format on the long system prompts Micdrop builds. It writes broken French too, such as `"Comment pouvez-je vous aider"`. Three seconds to the first token is already more than the budget of a whole spoken turn, so pick Qwen3 4B Instruct and reach for the Mistral API when you want a Mistral. ## Voxtral Mini 3B for transcription Voxtral Mini transcribes, translates and answers questions about audio. The community publishes an ONNX export, so it does run in Node through Transformers.js exactly like Whisper does. `examples/advanced/server/src/tests/voxtral-stt.ts` runs it on French sentences spoken by Piper, with weights in `q4` on CPU: Model Per utterance Word errors Voxtral Mini 3B ~16600 ms 17 % Whisper `base` ~470 ms 34 % Whisper `french` ~1200 ms 11 % Voxtral transcribes more accurately than Whisper `base`, and it costs thirty-five times more to run, because transcribing with it means generating tokens with a three billion parameter model instead of running an encoder and a small decoder. Whisper `french` does better on both measures for a fourteenth of the time. The weights come to 3 GB in `q4`, and Transformers.js downloads them on its own and aborts on files that size, so fetch them with `hf download` first. ## Voxtral TTS 4B for the voice Voxtral TTS arrived in March 2026, speaks nine languages including French, and clones a voice from three seconds of audio. It has no ONNX export, so it runs through `mlx-audio` in Python on Apple Silicon rather than in Node. With `mlx-community/Voxtral-4B-TTS-2603-mlx-4bit` in streaming mode, it loads in 2.4 seconds and returns its first chunk of audio after 1.0 to 1.5 seconds, then produces about two thirds of a second of speech per second of computation. That speed rules it out, since the audio arrives more slowly than it plays and a sentence stalls halfway through. Piper answers in 390 ms on the same machine and generates far faster than it plays, so every sentence plays through in one go. The voice itself is a clear step above anything else running locally, and a machine that generates two or three times faster would make it the best local voice available. Voxtral TTS is also under a `CC BY-NC` license, so a commercial call goes through the Mistral API rather than through these weights. ## Kyutai Kyutai publishes streaming speech to text models that cover French and English well, and they would remove the fixed cost of Whisper always reading thirty seconds of audio. They have no ONNX export, so running them means a Python or Rust server next to Node rather than an npm install. The Voxtral voice runs outside Node for the same reason. [Previousโ† Latency and Memory](/docs/ai-integration/local-models/performance)[NextIA Vocale Souveraine ๐Ÿ‡ซ๐Ÿ‡ท๐Ÿ‡ช๐Ÿ‡บ โ†’](/docs/ai-integration/sovereign-voice-ai) On this page * [The Mistral models](#the-mistral-models) * [Voxtral Mini 3B for transcription](#voxtral-mini-3b-for-transcription) * [Voxtral TTS 4B for the voice](#voxtral-tts-4b-for-the-voice) * [Kyutai](#kyutai)