# Gemma 4 12B IT vs MiniMax-M2.7

> Gemma 4 12B IT scores 71.3 (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-4-12b-it-vs-minimax-m2-7
- Markdown: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.md (~6,200 tokens)
- Slim: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.min.md (~530 tokens, same facts, less prose, for token-sensitive contexts)
- JSON: https://www.anchorterminal.com/compare/gemma-4-12b-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 12B IT scores 71.3 (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 12B IT: grade BB, 71.3/100, rank #5 of 100 open-weight models. Markdown https://www.anchorterminal.com/tools/gemma-4-12b-it.md · JSON https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-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 12B IT (BB)

Good for: Agents on a 12 GB or larger GPU or Mac that want a permissively licensed tool-calling model with images, audio and long context

Ahead on:
- Reach, 95 against 60
- Risk, 70 against 30
- Agent interface, 85 against 75
- Documentation and upkeep, 75 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 figures 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 50

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 12B IT | MiniMax-M2.7 | Edge |
| --- | --- | --- | --- | --- |
| Capability | 35% (35 this run) | 50 | 71 | MiniMax-M2.7 +21 |
| Reach | 25% (25 this run) | 95 | 60 | Gemma 4 12B IT +35 |
| Risk | 20% (20 this run) | 70 | 30 | Gemma 4 12B IT +40 |
| Agent interface | 10% (10 this run) | 85 | 75 | Gemma 4 12B IT +10 |
| Documentation and upkeep | 10% (10 this run) | 75 | 27 | Gemma 4 12B IT +48 |
| Negative events | ≤15 | 0 | 0 | |
| **Total** | | **71.3 · BB** | **56.1 · C** | |

## Facts side by side

| Fact | Gemma 4 12B 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 12B IT.** A mid-size cousin for agent work with tools, optional thinking and image and audio input, under Apache 2.0. Google's 4-bit build needs under 9 GB with working space, so it runs on a 12 GB GPU or any recent Mac in all the common runtimes. The main risks are no published safety results and no documented JSON output.

**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 12B IT

1. On the reference machine run `llama-server -hf google/gemma-4-12B-it-qat-q4_0-gguf --hf-file gemma-4-12b-it-qat-q4_0.gguf --jinja -c 131072`, and add `--mmproj` with mmproj-gemma-4-12b-it-qat-q4_0.gguf for images and audio
2. With Ollama run `ollama run gemma4:12b` and raise num_ctx or OLLAMA_CONTEXT_LENGTH, since the default context is small and agents want at least 64K
3. In vLLM or SGLang pass `--tool-call-parser gemma4 --reasoning-parser gemma4` (vLLM also `--enable-auto-tool-choice`), or tool calls come back as text; Google's w4a16 build is the 4-bit route there
4. Thinking is off by default; pass enable_thinking=True or start the system prompt with <|think|>, and drop earlier turns' thinking from history except on tool-call turns
5. Put images before the text and audio after it, keep audio to 30 seconds and video to 60 frames, and validate tool names and arguments before running them
6. Transformers needs 5.10.0 or later for gemma4_unified, and mlx-lm has no gemma4_unified code, so on a Mac use mlx-vlm, LM Studio or Ollama

### 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 12B IT or MiniMax-M2.7?

Gemma 4 12B IT scores 71.3 (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-4-12b-it-vs-minimax-m2-7.json, and with the fewest tokens: https://www.anchorterminal.com/compare/gemma-4-12b-it-vs-minimax-m2-7.min.md
- Over MCP at https://www.anchorterminal.com/mcp (no key): `compare_tools {"a": "gemma-4-12b-it", "b": "minimax-m2-7"}`. From a terminal: `anchor compare gemma-4-12b-it minimax-m2-7`
- Each listing in full: https://www.anchorterminal.com/api/v1/tools/gemma-4-12b-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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