--- title: "MiniCPM5-2B | Micdrop" description: "MiniCPM5-2B tested as the agent of a local call, twice as fast per turn as Qwen3 4B but English only in practice." url: "https://micdrop.dev/docs/ai-integration/local-models/explorations/minicpm" --- * [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) * [Turn Detection](/docs/client/turn-detection) * [Reducing Latency](/docs/client/latency) * [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) * [Client (React Native)](/docs/react-native) * [Installation](/docs/react-native/installation) * [Hooks and Call State](/docs/react-native/hooks) * [Audio Output and Devices](/docs/react-native/audio-output) * [Voice Activity Detection (VAD)](/docs/react-native/vad) * [Turn Detection](/docs/react-native/turn-detection) * [Using Another Audio Library](/docs/react-native/custom-audio) * [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) * [Dictation and Text-Only Calls](/docs/server/dictation) * [Partial Messages](/docs/server/partial-messages) * [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) * [Qwen3-TTS](/docs/ai-integration/provided-integrations/qwen-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) * [Local LLM](/docs/ai-integration/local-models/agent) * [Local STT](/docs/ai-integration/local-models/speech-to-text) * [Local TTS](/docs/ai-integration/local-models/text-to-speech) * [Latency and Memory](/docs/ai-integration/local-models/performance) * [Explorations](/docs/ai-integration/local-models/explorations) * [MiniCPM5-2B](/docs/ai-integration/local-models/explorations/minicpm) * [Mistral 7B](/docs/ai-integration/local-models/explorations/mistral-7b) * [Voxtral Mini 3B](/docs/ai-integration/local-models/explorations/voxtral-stt) * [Voxtral TTS 4B](/docs/ai-integration/local-models/explorations/voxtral-tts) * [AuK and AuK-Flash](/docs/ai-integration/local-models/explorations/auk) * [IA Vocale Souveraine 🇫🇷🇪🇺](/docs/ai-integration/sovereign-voice-ai) * [Migration](/docs/migration) * [Upgrade to v3](/docs/migration/v3) [Micdrop](/) › [Documentation](/docs/getting-started) › [AI Integrations](/docs/ai-integration) › [Local Models](/docs/ai-integration/local-models) › [Explorations](/docs/ai-integration/local-models/explorations) # MiniCPM5-2B Tested as the agent, the part that writes the answers and decides when to call a tool. It is the only model in this section that is recommended, for an English call on a machine with little memory. MiniCPM5-2B is a 2 billion parameter model from OpenBMB, published as a GGUF, so Ollama pulls it from Hugging Face with `ollama pull hf.co/openbmb/MiniCPM5-2B-GGUF:Q4_K_M`. Like the other agents in this section, it was compared with the recommended model, [Qwen3 4B Instruct](/docs/ai-integration/local-models/agent), using `ollama-agent.ts` with its `OLLAMA_MODEL` and `OLLAMA_LANG` variables, on the same machine and with the same prompt: The test MiniCPM5-2B Qwen3 4B, recommended Weights in `Q4_K_M` 1.6 GB 2.5 GB First token, warm, tools declared ~60 ms ~100 ms A whole turn in English 340 to 820 ms 820 to 1420 ms Asking the time, with a `get_time` tool Tool called Tool called A meaningless `"uh"`, with `ignore_user_noise` Answered anyway, 1 call in 9 runs `CancelLastUserMessage` every run `"Thanks, goodbye!"`, with `end_call` No call, offered more help No call, said goodbye Numbers written in full, as the prompt asks `"9:14:07 AM"` `"nine fifteen forty-three in the morning"` The model reasons before answering unless reasoning is turned off. Ollama maps `providerOptions: { openai: { reasoningEffort: 'none' } }` to its own `think` flag. A short greeting takes 700 to 1500 ms with reasoning on, and 180 ms with it off. The model answers a plain `"uh"` instead of calling `ignore_user_noise`: it called the tool once in 9 English runs, while Qwen3 4B called it every time. Noise filtering needs a fallback. It also writes the time as `"9:14:07 AM"` although the prompt asks for numbers in full. In French it writes `"Il est currently neuf heures"`, invents words such as `"aujourd'nous"` and `"n'hélas"`, and opens every turn with `"Bonjour"`. When the prompt asks for French, the first line of the default Micdrop prompt, _“If you’re first to speak, greet the user and ask how you can help”_, also stops its tool calls. Over 5 identical turns asking the time, `get_time` was called 0 times with that line in the prompt and 5 times with the line removed. The same prompt in English calls the tool 5 times either way. The weights are trained on English and Chinese. Use MiniCPM5-2B for an English call on a machine with little memory. It is twice as fast per turn as Qwen3 4B and uses 1 GB less. Qwen3 4B Instruct stays the recommended model, because it handles the automatic prompts and other languages. [Previous← Explorations](/docs/ai-integration/local-models/explorations)[NextMistral 7B →](/docs/ai-integration/local-models/explorations/mistral-7b)