Minimax M2.7 for OpenClaw: The $0.60/M Alternative to Opus
MiniMax shipped M2.7 in early April 2026. 229 billion parameters, open weights, SWE-Pro 56.22%, Terminal Bench 2 at 57.0, and a GDPval-AA ELO of 1495 — reported as the highest among open models at release. Hosted API or self-host, your call. The r/openclaw reaction landed somewhere between “actually good” and “doesn't suck,” which is high praise for a model launch. This post is the setup guide plus the honest parts — starting with the license, because that is the single biggest reason M2.7 is not a drop-in replacement for every team.
The numbers that matter
Four figures carry the weight of this release. Open weights at a size nobody has to apologize for, a real SWE-Pro score, a Terminal Bench 2 result above most closed models, and a public price point that makes Opus look expensive on high-volume agent workloads.
229B
Open weights. Dense architecture, not MoE.
56.22%
Real software-engineering benchmark. Within arm's reach of Opus.
57.0
Agent-style tool use in a live shell environment.
~$0.60/M
April 2026 hosted price. Pricing moves — verify before you commit.
One more headline figure worth mentioning: MiniMax reported a GDPval-AA ELO of 1495 for M2.7, which they position as the top among open-weights models at release. Take that claim with the usual caveats — ELO measures relative win rates and shifts as competitors update — but the direction is clear. This is a serious frontier-adjacent open model, not a polite also-ran.
All prices in this post are indicative and subject to change. Verify on MiniMax's pricing page before you wire it into a production budget.
The license caveat — read this before anything else
This is the part most launch coverage buries, so put it up front. M2.7's weights are open in the sense that you can download them. The license is not open in the MIT / Apache sense. It is source-available with a commercial-revenue threshold — the typical MiniMax release pattern.
What this means in practice
- Solo developers, hobbyists, and low-revenue side projects can use M2.7 without friction.
- Agents that generate direct commercial revenue above the threshold may require a commercial license agreement with MiniMax.
- Internal tooling at larger companies sits in a gray zone. Run it past legal before you make M2.7 a load-bearing part of your stack.
- The threshold and exact wording can shift between releases. Do not rely on a write-up (including this one) as the source of truth — read the actual LICENSE file on the model card.
The practical read: M2.7 is an excellent choice if the license works for your project. It is not a drop-in Claude replacement for every company. Pretending otherwise helps nobody and wastes engineering time downstream. If your use case is ambiguous, GLM-5.1 or a purely hosted commercial model will save you a legal review cycle.
When to pick M2.7
Four concrete criteria. If more than two of these fit, M2.7 is worth a serious evaluation for the agent you're building.
You have agentic coding workloads
M2.7 is benchmark-tuned for software engineering and shell-based agent tasks. If your agents write, read, or refactor code all day, this is exactly the workload the model was built for.
You want open weights for self-hosting
Air-gapped environments, strict data residency, or latency-sensitive inference on your own GPUs — M2.7 gives you the weights. Most commercial frontier models do not.
You are cost-sensitive at volume
At an indicative ~$0.60/M input tokens, the math on high-frequency agent calls looks very different from Opus pricing. The license has to work for your use case, but when it does, the savings are real.
You want independence from US providers
OpenAI, Anthropic, and Google are good but concentrated. M2.7 gives you a credible fourth option outside that triangle — useful for diversification, jurisdictional concerns, or simply avoiding single-vendor lock-in.
Setup with OpenClaw — hosted option
The fastest path. MiniMax ships an OpenAI-compatible API, which OpenClaw's provider system handles natively. Add the provider, drop in a SOUL.md, run the agent.
openclaw provider add minimax \
--api-key $MINIMAX_API_KEY \
--base-url https://api.minimax.chat/v1
cp configs/minimax-m2.7/SOUL.md ~/.openclaw/agents/coding-assistant/SOUL.md
openclaw agent --agent coding-assistant --message "Refactor this function"That's the whole setup. Your existing SOUL.md, tools, and memory continue to work — only the model layer changes. If you already have a coding-assistant agent running on Claude or GPT, you can wire up M2.7 as a parallel provider and A/B them against the same task prompts to see which behaves better for your workload.
Setup with OpenClaw — self-hosted option
If you have the hardware and the license fits, running M2.7 locally gives you full control. 229B dense parameters is not a small model — plan your infrastructure accordingly.
4x A100 80GB
Or equivalent Hopper-class. Tighter quantization may reduce this at quality cost.
vLLM / SGLang
Both expose OpenAI-compatible endpoints OpenClaw can talk to directly.
localhost:8000/v1
Point --base-url at your local server. Everything else stays identical to the hosted flow.
# Launch vLLM against the downloaded weights
vllm serve minimax/m2.7 \
--tensor-parallel-size 4 \
--host 0.0.0.0 --port 8000
# Point OpenClaw at the local endpoint
openclaw provider add minimax-local \
--api-key none \
--base-url http://localhost:8000/v1
openclaw agent --agent coding-assistant --message "Write tests for utils.ts"Realistically, most OpenClaw users will prefer the hosted API. Running 229B on-prem is a meaningful ops commitment — worth it if you already have GPU capacity or strict data residency requirements, overkill if you're a solo developer with a side project.
mmx-cli is a separate thing
Worth calling out because it caused confusion in the launch threads. MiniMax ships mmx-cli as a standalone command-line tool for text, image, video, speech, and music generation. It's a nice tool. It is not how you use M2.7 for agents.
For OpenClaw agent workflows — SOUL.md, tools, memory, scheduled runs — you want the M2.7 model via the hosted API or self-hosted weights. For quick multi-modal creative tasks, install mmx-cli separately and treat it as its own thing. Don't try to make mmx-cli your agent runtime; that's what OpenClaw is for.
Gotchas from the first week of real usage
Aggregated from r/openclaw threads and first-hand setup experience. None of these are dealbreakers, but knowing them upfront saves a couple of debugging sessions.
1. License clarity before production
Say it twice because it matters twice. The weights download is one click; a commercial license review is several weeks. Don't let the first one lead you into a surprise version of the second.
2. Model ID variants
The API exposes minimax-m2.7, minimax-m2.7-turbo, and minimax-m2.7-mini (naming approximate per launch docs). Confirm which variant your endpoint is routing to — benchmark numbers in this post refer to the flagship. A 'turbo' or 'mini' variant will score differently.
3. Tool-call schema drift
The function-calling format is close to OpenAI's but not identical. Agents that hardcode assumptions about the exact tool-call JSON shape may need small adapter tweaks. Validate your tool schemas end-to-end on M2.7 before trusting them in production.
4. Streaming and token caps
Response streaming works. Maximum output token limits are different from Anthropic's — do not assume your 8K-output-cap assumptions carry over. Long-form code generation in particular should have explicit max_tokens set.
5. Occasional Chinese leakage
If your system prompt isn't forceful, M2.7 can occasionally slip into Chinese-language responses mid-turn, especially on ambiguous prompts. Adding an explicit 'respond only in English' line to SOUL.md resolves this in practice.
How it compares to GLM-5.1
These two models keep getting compared in r/openclaw threads and the comparison is fair — both are serious open-weights options from outside the US provider stack. They solve slightly different problems.
| Minimax M2.7 | GLM-5.1 | |
|---|---|---|
| Parameters | 229B dense | 744B MoE (40B active) |
| SWE-Bench Pro | 56.22 | 58.4 |
| License | Source-available + revenue threshold | Open weights + hosted $3-10/mo plan |
| Setup path | OpenAI-compatible API or vLLM self-host | Ollama :cloud tag (one command) |
| Best for | Self-hosting flexibility, cost at volume | Drop-in Claude replacement for most users |
Verdict: GLM-5.1 is the easier first move for most OpenClaw users. The Ollama :cloud tag, the $3-10/month pricing, and the clean open-weights license make it a low-friction swap from Claude. M2.7 wins specifically when you need the weights on your own infrastructure, when volume pushes hosted API bills above the self-host break-even, or when the MiniMax license genuinely works for your project. Both are good answers to different questions.
Build an M2.7 agent today
The config bundle in awesome-openclaw-agents includes a working SOUL.md for M2.7 plus example tool configs for common coding-agent roles. Clone it, adjust the rules, point OpenClaw at MiniMax, done.
Minimax M2.7 config bundle
SOUL.md, provider config, and example tool setups on GitHub
Build your agent in CrewClaw
Visual builder, pick M2.7 as the model, download a complete deploy package
Already read the GLM-5.1 piece? This post is the self-host-friendly counterpart. If you want the head-to-head reasoning on why GLM-5.1 might still be your first pick, the full writeup is here:
Frequently Asked Questions
Is MiniMax M2.7 open source?
The weights are published openly, but the license is source-available with a commercial-revenue threshold — MiniMax's typical release pattern. Solo developers and low-revenue projects are generally fine. Teams with meaningful commercial revenue need to verify their specific use case against the license terms before deploying M2.7 in production.
Can I run M2.7 locally?
Yes, with enough GPU memory. For the full 229B parameter model, a realistic baseline is 4x A100 80GB (or equivalent Hopper-class hardware). Use vLLM or SGLang as the inference engine and point OpenClaw at the local endpoint with --base-url http://localhost:8000/v1. Most users will still prefer the hosted API unless they already have the hardware on hand.
How does M2.7 compare to GLM-5.1?
GLM-5.1 is a 744B MoE (40B active) hosted via Ollama's :cloud tag with a $3-10/month Coding Plan — easiest drop-in Claude replacement for most users. MiniMax M2.7 is 229B open weights with SWE-Pro 56.22%; it wins when you specifically need the flexibility of self-hosting, cost control at volume, or an open-weights model outside the US provider stack.
What is mmx-cli?
mmx-cli is MiniMax's separate command-line tool for text, image, video, speech, and music generation. It is not how you use M2.7 for agent workflows — that goes through the OpenAI-compatible API or self-hosted weights. If you also want multi-modal creative work from MiniMax, install mmx-cli separately; the two tools solve different problems.
Does OpenClaw natively support M2.7?
Yes. OpenClaw's provider system works with any OpenAI-compatible endpoint, which is how MiniMax ships the API. Add the provider with openclaw provider add, drop in the SOUL.md from the Minimax M2.7 config bundle in awesome-openclaw-agents, and point your agent at the minimax-m2.7 model. Self-hosted setups use the same path with a local base URL.
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