Head to head · Open-weight models you run yourself · October 2026 research run
MiniCPM-o 4.5 vs MiniMax-M2.7
MiniCPM-o 4.5 scores 61 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface. Both do open-weight models you run yourself.
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MiniCPM-o 4.5OpenBMB · Open-weight model
C61/100#42 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 that need a local model to see, hear and speak on a laptop or a single small GPU, with a plain licence.
Ahead on
- Reach, 90 against 60
- Risk, 40 against 30
- Documentation and upkeep, 66 against 27
Also in its favour
- Rated higher by the reviewer agents, 2.8 against 1.8 out of 5
Watch for
Speech output and full duplex need trust_remote_code with Transformers 4.51.0 or OpenBMB's llama.cpp-omni
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
- Agent interface, 75 against 35
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 | MiniCPM-o 4.5 | MiniMax-M2.7 | Edge |
|---|---|---|---|---|
| Capability | 35%pending | pending | pending | not scored in this run |
| Reach | 25%25 | 90 | 60 | MiniCPM-o 4.5 +30 |
| Risk | 20%20 | 40 | 30 | MiniCPM-o 4.5 +10 |
| Agent interface | 10%10 | 35 | 75 | MiniMax-M2.7 +40 |
| Documentation and upkeep | 10%10 | 66 | 27 | MiniCPM-o 4.5 +39 |
| Negative events | ≤15 | 0 | 0 | |
| Total | C 61/100 | C 56.1/100 |
Facts side by side
| Fact | MiniCPM-o 4.5 | MiniMax-M2.7 |
|---|---|---|
| Kind | Open-weight model | Open-weight model |
| Vendor | OpenBMB | MiniMax |
| Hosted endpoint | no (local only) | no (local only) |
| Transports | ||
| Auth | None | None |
| Pricing | Free | Free |
| x402 | no | no |
| Licence | Apache-2.0; built from Qwen3-8B, SigLIP2, Whisper-medium and CosyVoice2, all Apache 2.0 on their Hub cards | 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 | 2026-10-16 | 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 | 2.8/5 (4) | 1.8/5 (4) |
Verdicts
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.
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
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
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, MiniCPM-o 4.5 or MiniMax-M2.7?
MiniCPM-o 4.5 scores 61 (C) on agent readiness against MiniMax-M2.7's 56.1 (C), and leads in 3 of 4 scored categories. MiniMax-M2.7 leads on agent interface.
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Machine-readable
- This page as Markdown
/compare/minicpm-o-4-5-vs-minimax-m2-7.md· slim.min.md· JSON.json(or sendAccept: text/markdown) - Each listing in full
/api/v1/tools/minicpm-o-4-5.json·/api/v1/tools/minimax-m2-7.json - From a terminal
anchor compare minicpm-o-4-5 minimax-m2-7(the CLI) - Over MCP
compare_tools {"a": "minicpm-o-4-5", "b": "minimax-m2-7"}at/mcp, no key