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,
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.