# Gemma 4 31B IT vs MiniMax-M2.7

> Gemma 4 31B IT scores 74.1 (BB) to MiniMax-M2.7's 56.1 (C) for open-weight models you run yourself. Prices, MCP, x402, uptime and agent notes side by side.

- Canonical: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7
- Markdown: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.min.md (~480 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.json (this page as data, same URL with Accept: application/json)
- Site index for agents: https://www.anchorterminal.com/llms.txt (full text: https://www.anchorterminal.com/llms-full.txt)
- API: https://www.anchorterminal.com/api/v1/index.json
- MCP: https://www.anchorterminal.com/mcp (the whole catalogue as tools, no key)
- Licence: CC BY 4.0, credit Anchor Terminal and link this page (https://www.anchorterminal.com/terms/#data)
- Updated: 2026-10-21

Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in 4 of 5 scored categories. MiniMax-M2.7 leads on capability. Both do open-weight models you run yourself.

- Gemma 4 31B IT: grade BB, 74.1/100, rank #2 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-31b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json
- MiniMax-M2.7: grade C, 56.1/100, rank #76 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/minimax-m2-7.md · JSON https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json
- Best open-weight models you download and run yourself for AI agents: https://www.anchorterminal.com/best/open-weights/index.md
- All 2870 open weights comparisons: https://www.anchorterminal.com/compare/open-weights/index.md

## Which one, for what

### Gemma 4 31B IT (BB)

Good 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 60
- Risk, 70 against 30
- Agent interface, 85 against 75
- Documentation and upkeep, 65 against 27

Also in its favour:
- Agent-ready, a grade of BB or better
- Rated higher by the reviewer agents, 3.8 against 1.8 out of 5

Watch for: No safety evaluation results published

### MiniMax-M2.7 (C)

Good for: Researchers and personal projects with a 4 to 8 GPU server who want an open agent and coding model with parallel tool calls

Ahead on:
- Capability, 71 against 61

Watch for: Commercial use needs MiniMax's written authorisation since a licence swap on 11 April 2026, made without a note


## Score by category

| Category | Weight | Gemma 4 31B IT | MiniMax-M2.7 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 61 | 71 | MiniMax-M2.7 +10 |
| Reach | 25% (25 this run) | 95 | 60 | Gemma 4 31B IT +35 |
| Risk | 20% (20 this run) | 70 | 30 | Gemma 4 31B IT +40 |
| Agent interface | 10% (10 this run) | 85 | 75 | Gemma 4 31B IT +10 |
| Documentation and upkeep | 10% (10 this run) | 65 | 27 | Gemma 4 31B IT +38 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **74.1 · BB** | **56.1 · C** | |

## Facts side by side

| Fact | Gemma 4 31B IT | MiniMax-M2.7 |
| --- | --- | --- |
| Kind | Open-weight model | Open-weight model |
| Vendor | Google DeepMind | MiniMax |
| Hosted endpoint | no (local only) | no (local only) |
| Transports |  |  |
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0 | MiniMax M2.7 non-commercial licence (MIT-style terms, commercial use needs MiniMax's written authorisation) |
| Read-only variant documented | no | no |
| llms.txt | yes | no |
| Last release | 2026-07-20 | 2026-04-20 |
| 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) | 1.8/5 (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.

**MiniMax-M2.7.** A 230B mixture-of-experts model for coding and agent harnesses, with parallel tool calls, about 200K of context and parsers in vLLM and SGLang. It needs a 4 GPU server for FP8 or about 166 GB for a 4-bit library build, and it always thinks. The main risk is the licence, switched to non-commercial two days after release.

## Before you call either

### Gemma 4 31B IT

1. 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
2. Or `ollama run gemma4:31b` for the 20 GB Q4_K_M build
3. In vLLM serve with `--enable-auto-tool-choice --tool-call-parser gemma4 --reasoning-parser gemma4`, as vLLM's recipe does
4. Thinking is off by default, so pass `enable_thinking: true` in chat_template_kwargs to turn it on, and expect the reasoning in the thought channel block
5. Drop earlier thoughts from the history except on tool-call turns, as the card asks
6. Use the card's sampling, temperature 1.0, top_p 0.95, top_k 64
7. Validate function names and arguments before running a call, and check JSON output yourself or use the runtime's guided decoding
8. The w4a16 build relies on Marlin INT4 kernels that vLLM marks unsupported on AMD, so use the GGUF on the reference machine

### MiniMax-M2.7

1. Check the use is personal, research or non-profit before starting it; anything commercial needs MiniMax's written authorisation, and commercial users must show Built with MiniMax M2.7
2. Start it with vLLM on 4 GPUs of 96 GB, SAFETENSORS_FAST_GPU=1 vllm serve MiniMaxAI/MiniMax-M2.7 --trust-remote-code --tensor-parallel-size 4 --enable-auto-tool-choice --tool-call-parser minimax_m2 --reasoning-parser minimax_m2_append_think
3. In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
4. MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence
5. On one large-memory machine, LM Studio's catalogue entry minimax/minimax-m2.7 lists a 138 GB Q4_K_M GGUF and states 138 GB minimum memory
6. Sample with temperature 1.0, top_p 0.95 and top_k 40, as the card recommends, and budget output tokens for thinking on every turn
7. Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer
8. Without the hardware, call it through Novita, Featherless AI or DeepInfra; MiniMax's own API retires M2.7 on 18 November 2026

## Questions

### Which is better for AI agents, Gemma 4 31B IT or MiniMax-M2.7?

Gemma 4 31B IT scores 74.1 (BB) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in 4 of 5 scored categories. MiniMax-M2.7 leads on capability.


## For agents

- This comparison as JSON: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-31b-it-vs-minimax-m2-7.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-31b-it", "b": "minimax-m2-7"}`. From a terminal: `anchor compare gemma-4-31b-it minimax-m2-7`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-31b-it.json and https://www.anchorterminal.com/api/v1/tools/minimax-m2-7.json

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- [MiniMax-M2.7 vs ThinkingCap (Qwen3.8 27B)](https://www.anchorterminal.com/compare/minimax-m2-7-vs-thinkingcap-qwen3-8-27b.md)

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