Head to head · Open-weight models you run yourself · October 2026 research run
Gemma 4 26B A4B IT vs MiniMax-M2.7
Gemma 4 26B A4B IT scores 72.2 (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.
Best open-weight models you download and run yourself for AI agents · All 2870 open weights comparisons
Gemma 4 26B A4B ITGoogle DeepMind · Open-weight model
BB72.2/100#4 of 100 open-weight models
MiniMax-M2.7MiniMax · Open-weight model
C56.1/100#76 of 100 open-weight models
Which one, for what
Best for Agents on a 24 GB or larger GPU or Mac that want a fast, permissively licensed tool-calling model with images and long context
Ahead on
- Reach, 90 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 figures published
Best 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 59
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 this run | Gemma 4 26B A4B IT | MiniMax-M2.7 | Edge |
|---|---|---|---|---|
| Capability | 35%35 | 59 | 71 | MiniMax-M2.7 +12 |
| Reach | 25%25 | 90 | 60 | Gemma 4 26B A4B IT +30 |
| Risk | 20%20 | 70 | 30 | Gemma 4 26B A4B IT +40 |
| Agent interface | 10%10 | 85 | 75 | Gemma 4 26B A4B IT +10 |
| Documentation and upkeep | 10%10 | 65 | 27 | Gemma 4 26B A4B IT +38 |
| Negative events | ≤15 | 0 | 0 | |
| Total | BB 72.2/100 | C 56.1/100 |
Facts side by side
| Fact | Gemma 4 26B A4B 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 | no | 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 26B A4B IT
A fast mixture-of-experts cousin for general agent work with tools, optional thinking and image input, under Apache 2.0. Google's 4-bit build needs about 17 GB, so it runs on a 24 GB GPU or Mac in any common runtime. The main risks are no published safety results, no documented JSON output and unanswered template bug reports.
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 26B A4B IT
- On the reference machine run
llama-server -hf google/gemma-4-26B-A4B-it-qat-q4_0-gguf --hf-file gemma-4-26B_q4_0-it.gguf --jinja -c 65536, and add the mmproj file for images - With Ollama run
ollama run gemma4:26band raise num_ctx or OLLAMA_CONTEXT_LENGTH, since Google's guide says it defaults to 4K below 24 GiB of VRAM and agents want at least 64K - 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 - 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
- Validate tool names and arguments before running them, and check any JSON yourself
MiniMax-M2.7
- 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
- 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
- In SGLang use --tool-call-parser minimax-m2 and --reasoning-parser minimax-append-think, with --trust-remote-code
- MiniMax's guides budget 220 GB for the weights and 240 GB per 1M tokens of KV cache, with 196K the most per sequence
- 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
- 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
- Pin Transformers 4.57.1 as MiniMax's guide does; a report that it fails on Transformers v5 has no answer
- 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 26B A4B IT or MiniMax-M2.7?
Gemma 4 26B A4B IT scores 72.2 (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.
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Machine-readable
- This page as Markdown
/compare/gemma-4-26b-a4b-it-vs-minimax-m2-7.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/gemma-4-26b-a4b-it.json·/api/v1/tools/minimax-m2-7.json - From a terminal
anchor compare gemma-4-26b-a4b-it minimax-m2-7(the CLI) - Over MCP
compare_tools {"a": "gemma-4-26b-a4b-it", "b": "minimax-m2-7"}at/mcp, no key