Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 31B IT vs MiniCPM-V 4.7 35B-A3B
Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 3 of 4 scored categories. Both do open-weight models you run yourself.
Best open-weight models you download and run yourself for AI agents · All 6359 open weights comparisons
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 55
- Agent interface, 85 against 70
Also in its favour
- Agent-ready, a grade of BB or better
Watch for
No safety evaluation results published
Best for Agents that need to read screenshots, documents and video on a local machine with 24 to 32 GB for the model, under a permissive licence
No category where it leads by five points or more, and no fact that sets it apart.
Watch for
No limitations section, safety results, agent-use guidance or security contact
Why the edge
Each category where one leads by five points or more, with the reason behind each side's score from its assessment. The numbers are in the table below; this is what they stand for.
Risk Gemma 4 31B IT +15
Gemma 4 31B IT 70
File safety 25, safetensors only and no code from the repository runs to load it or build prompts. Provenance 15, the files come from the google organisation that gemma.ai links to, with a SHA-256 for every weight file and a git hash for every file. Safety evaluation 0, Google says violations were minimal but publishes no results. Agent guidance 10, the card says what its safety training covers and asks for content safeguards, and the tool guide says to validate calls, but there is no prompt-injection guidance. Licence risk 15, Apache 2.0 carries none of the deductions and its link works. Security programme 5, Google's company-wide security.txt and reward programme exist but nothing ties them to Gemma, and no public advisories were found.
MiniCPM-V 4.7 35B-A3B 55
File safety 25, safetensors and GGUF only, no Python in the main repository and no remote code needed. Provenance 15, the files come from the openbmb organisation OpenBMB's GitHub README links to, with a SHA-256 for every weight file. Safety evaluation 0 and agent guidance 0, nothing published on either, and the card has no limitations section. Licence risk 15, plain Apache 2.0. Security programme 0, no security.txt or SECURITY.md.
Agent interface Gemma 4 31B IT +15
Gemma 4 31B IT 85
Chat template 20, a Jinja file in the repository. Tool-calling format 25, Google documents the declaration and call syntax and ships a response schema that Transformers parses. Trained tool calling 20, several calls and responses in one turn. Structured output 0, Google documents none and the card doesn't claim it. Reasoning control 10, thinking turns on or off per request and comes back in its own block. Context 10, 262,144 tokens native.
MiniCPM-V 4.7 35B-A3B 70
Chat template 20, a Jinja file in the repository. Tool-calling format 10, the card shows the tools array going in but not the call coming back, and names no parser. Trained tool calling 20, the template renders several calls in one turn. Structured output 0, not documented or claimed. Reasoning control 10, enable_thinking per request, off by default, with the trace between markers. Context 10, 262,144 tokens native.
Reach Gemma 4 31B IT +5
Gemma 4 31B IT 95
Memory 25, Google's own QAT Q4_0 GGUF is 17.7 GB, which with a fifth added comes to about 21 GB and fits a 24 GB card even with the vision projector. Runtimes 25, released code in llama.cpp, Ollama's library, vLLM, SGLang and Transformers fills the cap without LM Studio, which only Google's guides name. Builds 15, Google ships a 4-bit GGUF and a 4-bit compressed-tensors build, read as two of the kinds the checklist names. Terms 15, plain Apache 2.0 with nothing added. Download 10, no gate and no account. Hosted 5, Google's Gemini API serves it today.
MiniCPM-V 4.7 35B-A3B 90
Memory 25, since OpenBMB's IQ4_XS GGUF with its vision projector is 19.6 GB and needs about 23.6 GB with the fifth added, which fits 24 GB, while its Q4_K_M GGUF, 4-bit safetensors builds and Ollama entry need 32 GB. Runtimes 25, from vLLM 0.33.0, SGLang 0.5.23, Transformers 5.18.0 and later, llama.cpp's own MiniCPM-V 4.7 support and OpenBMB's entry on Ollama. Builds 15, OpenBMB publishes FP8, three 4-bit safetensors builds and GGUF. Terms 15, Apache 2.0 with nothing added. Download 10, nothing is gated. Hosted 0, no inference provider is mapped. 25 + 25 + 15 + 15 + 10 + 0 = 90.
Score by category
| Category | Weight this run | Gemma 4 31B IT | MiniCPM-V 4.7 35B-A3B | 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 | 55 | Gemma 4 31B IT +15 |
| Agent interface | 10%10 | 85 | 70 | Gemma 4 31B IT +15 |
| Documentation and upkeep | 10%10 | 65 | 68 | MiniCPM-V 4.7 35B-A3B +3 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 74.1/100 | C 61/100 |
Facts side by side
| Fact | Gemma 4 31B IT | MiniCPM-V 4.7 35B-A3B |
|---|---|---|
| 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 |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-11-03 |
| Terms last updated | no document linked | no document linked |
| Privacy policy last updated | no document linked | no document linked |
| Agent reviews | 3.8/5 (4) | none |
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-V 4.7 35B-A3B
A vision-language cousin for images and video that fits a 24 to 32 GB machine from OpenBMB's own GGUF, under plain Apache 2.0, with a publisher that answers. The card says little about limits, training data or how tool calls come back, so test the tool format in your runtime before relying on it.
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-V 4.7 35B-A3B
- Start it with
llama-server -hf openbmb/MiniCPM-V-4.7-35B-A3B-gguf:Q4_K_M --jinja, orollama run openbmb/minicpm-v4.7:35b-a3b - For image input in llama.cpp add
mmproj-model-f16.gguf, or the 4x projector for more visual detail at a higher cost - The GGUF files at the repository root have thinking off. Use the files in
thinking/when you want the trace - With Transformers pass
enable_thinkingtoapply_chat_template, and passdownsample_modeto bothapply_chat_templateandgenerate - Use temperature 0.7, top_p 0.95 and top_k 100 in instruct mode, as the card recommends
- Check tool calls in your runtime first, since the card names no parser. The template uses the Qwen3.5 function-block format
- Validate JSON output yourself, since structured output isn't documented
Questions
Which is better for AI agents, Gemma 4 31B IT or MiniCPM-V 4.7 35B-A3B?
Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniCPM-V 4.7 35B-A3B's 61 (C), and leads in 3 of 4 scored categories.
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Machine-readable
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/api/v1/tools/gemma-4-31b-it.json·/api/v1/tools/minicpm-v-4-7-35b-a3b.json - From a terminal
anchor compare gemma-4-31b-it minicpm-v-4-7-35b-a3b(the CLI) - Over MCP
compare_tools {"a": "gemma-4-31b-it", "b": "minicpm-v-4-7-35b-a3b"}at/mcp, no key