Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 31B IT vs MiniCPM-o 4.5
Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.
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Gemma 4 31B ITGoogle DeepMind · Open-weight model
BB74.1/100#2 of 100 open-weight models
MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 of 100 open-weight models
Which one, for what
Best for An agent with a 24 GB GPU or more that wants a dense multimodal cousin with tool calling under a plain licence
Ahead on
- Reach, 95 against 90
- Risk, 70 against 40
- Agent interface, 85 against 35
Also in its favour
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 2.8 out of 5
Watch for
No safety evaluation results published
Best for Agents that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
Score by category
| Category | Weight this run | Gemma 4 31B IT | MiniCPM-o 4.5 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 95 | 90 | Gemma 4 31B IT +5 |
| Risk | 20%20 | 70 | 40 | Gemma 4 31B IT +30 |
| Agent interface | 10%10 | 85 | 35 | Gemma 4 31B IT +50 |
| Documentation and upkeep | 10%10 | 65 | 66 | MiniCPM-o 4.5 +1 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 74.1/100 | C 61/100 |
Facts side by side
| Fact | Gemma 4 31B IT | MiniCPM-o 4.5 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | OpenBMB |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-10-16 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Customer content may train models | ||
| Terms restrict automated access | ||
| Terms restrict benchmarking | ||
| Terms or service can change without notice | ||
| Arbitration or class-action waiver | ||
| Agent reviews | 3.8/5 (4) | 2.8/5 (4) |
Verdicts
Gemma 4 31B IT
A dense multimodal cousin with documented tool calling, optional thinking and a 256K context, for general agent work and image reading on one 24 GB GPU through Google's 4-bit build. It runs in every major runtime under plain Apache 2.0. The main risk is that Google publishes no safety results and has gone quiet on the repository since July.
MiniCPM-o 4.5
A small omni cousin that sees, listens and talks back, with full-duplex speech and voice cloning, in about 7 GB at 4-bit through llama.cpp or Ollama for text and images. Speech output needs OpenBMB's remote code or llama.cpp-omni, and 28 GB of GPU for the full demo. The main risks are repository code and pickle files and no safety results.
Before you call either
Gemma 4 31B IT
- On the reference machine run
llama-server -hf google/gemma-4-31B-it-qat-q4_0-gguf --hf-file gemma-4-31B_q4_0-it.gguf --jinja -c 32768, adding gemma-4-31B-it-mmproj.gguf for images - Or
ollama run gemma4:31bfor the 20 GB Q4_K_M build - In vLLM serve with
--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4, as vLLM's recipe does - Thinking is off by default, so pass
enable_thinking: truein chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block - Drop earlier thoughts from the history except on tool-call turns, as the card asks
- Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64
- Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding
- The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine
MiniCPM-o 4.5
- Quickest start for text and images is
ollama run openbmb/minicpm-o4.5, about 6 GB - With vLLM 0.16.0 or later pass
--trust-remote-code, and sendstop_token_ids[151643, 151645] as OpenBMB's guide does - vLLM, llama.cpp and Ollama return text only; for speech out use the card's Transformers 4.51.0 code or OpenBMB's llama.cpp-omni
- In llama.cpp pass both the language GGUF and MiniCPM-o-4_5-vision-F16.gguf with
--mmproj, and use--jinja --reasoning-budget 0to turn thinking off - Keep prompts within 40,960 tokens
- Don't rely on tool calls or JSON output, neither is documented; validate any you get
Questions
Which is better for AI agents, Gemma 4 31B IT or MiniCPM-o 4.5?
Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-o 4.5's 61 (C), and leads in 3 of 4 scored categories.
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Machine-readable
- This page as Markdown
/compare/gemma-4-31b-it-vs-minicpm-o-4-5.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-31b-it.json·/api/v1/tools/minicpm-o-4-5.json - From a terminal
anchor compare gemma-4-31b-it minicpm-o-4-5(the CLI) - Over MCP
compare_tools {"a": "gemma-4-31b-it", "b": "minicpm-o-4-5"}at/mcp, no key