Remote OpenClaw Blog
Best MiniMax Models for OpenClaw — MiniMax-01 and Text Models
7 min read ·
The best MiniMax model for OpenClaw as of April 2026 is MiniMax M2.7, a self-evolving agent model that scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 while costing only $0.30 per million input tokens. MiniMax gives OpenClaw operators one of the strongest cost-to-performance ratios in the current market, with an OpenAI-compatible API that makes configuration straightforward.
Part of The Complete Guide to OpenClaw — the full reference covering setup, security, memory, and operations.
MiniMax Model Overview
MiniMax is a Chinese AI company that builds large language models with a focus on long-context processing and agent capabilities. The company gained attention in January 2025 when it open-sourced MiniMax-Text-01, a 456B-parameter MoE model with a 4-million-token inference context window — the largest publicly available context window at the time.
Since then, MiniMax has released M2.5 (February 2026) and M2.7 (March 2026), both of which are purpose-built for agentic workflows. M2.7 is particularly notable because it is the first model to actively participate in its own development, running over 100 autonomous rounds of scaffold optimization and achieving a 30% performance improvement through self-evolution.
For OpenClaw operators, MiniMax's combination of low pricing, OpenAI-compatible endpoints, and strong agent performance makes it a compelling alternative to more expensive frontier models.
Model Specs and Comparison
MiniMax M2.7 activates only 10 billion parameters per token, making it the smallest model in the Tier-1 performance class for agentic tasks.
| Model | Parameters (Total / Active) | Context Window | Input Cost / 1M Tokens | Output Cost / 1M Tokens | Key Benchmark |
|---|---|---|---|---|---|
| MiniMax M2.7 | ~400B / 10B | 200K | $0.30 | $1.20 | SWE-Pro 56.22%, Terminal Bench 2 57.0% |
| MiniMax M2.5 | ~350B / ~10B | 196K | $0.12 | $0.95 | SWE Multilingual 72.1% |
| MiniMax-Text-01 | 456B / 45.9B | 4M (inference) | $0.20 | $1.10 | 4M token context processing |
M2.7 approaches Sonnet 4.6 on the MMClaw evaluation, which is MiniMax's internal benchmark for OpenClaw-specific agent tasks. On the public SWE-Pro benchmark, it matches GPT-5.3-Codex at 56.22%, and it scores 76.5 on SWE Multilingual and 52.7 on Multi SWE Bench.
MiniMax-Text-01 uses a hybrid architecture combining Lightning Attention with Softmax Attention and MoE. Lightning Attention reduces computational complexity from O(n^2d) to O(d^2n), which is how the model handles 4-million-token contexts without prohibitive costs. The architecture places a softmax attention layer after every 7 lightning attention layers to preserve fine-grained interactions.
API Setup for OpenClaw
OpenClaw connects to MiniMax through the MiniMax provider, which supports both API key and OAuth authentication.
To get started, register at platform.minimax.io, generate an API key from your dashboard, and configure OpenClaw:
# Option 1: Setup wizard
openclaw configure
# Select MiniMax when prompted
# Option 2: Environment variable
export MINIMAX_API_KEY="your-api-key-here"
MiniMax provides an OpenAI-compatible endpoint at https://api.minimax.io/v1, which means OpenClaw can route requests using standard OpenAI SDK patterns. Model references are case-sensitive:
# API key path
openclaw --model minimax/MiniMax-M2.7
# High-speed variant
openclaw --model minimax/MiniMax-M2.7-highspeed
# OAuth path (if using OAuth)
openclaw --model minimax-portal/MiniMax-M2.7
MiniMax M2.7 is also available as a cloud model through Ollama, referenced as minimax-m2.7:cloud in the OpenClaw Ollama setup.
Use Cases for OpenClaw Operators
MiniMax models fit specific OpenClaw workflows better than others. Here is where each model excels.
Marketplace
Free skills and AI personas for OpenClaw — browse the marketplace.
Browse the Marketplace →MiniMax M2.7 — Agentic Coding and Complex Tasks
M2.7 is built for multi-step agent workflows. On complex skills with over 2,000 tokens of instructions, it maintains a 97% skill adherence rate. Use it when your OpenClaw setup involves:
- repository-scale coding and debugging,
- long-horizon task chains where the agent needs to maintain state,
- automated development workflows where M2.5-generated code previously accounted for 80% of new commits in MiniMax's own engineering pipeline.
MiniMax M2.5 — Budget Production Workloads
At $0.12 per million input tokens, M2.5 is one of the cheapest production-grade models available for OpenClaw. It handles routine tasks — content generation, data extraction, email drafting, summarization — at a fraction of frontier model costs. If your OpenClaw usage is high-volume but not benchmark-critical, M2.5 is often the better value pick than M2.7.
MiniMax-Text-01 — Extreme Long-Context Processing
If your OpenClaw workflow involves processing very large documents — full codebases, legal contracts, research paper collections, or book-length content — MiniMax-Text-01's 4-million-token context is unmatched. No other model available through OpenClaw offers that kind of context capacity at $0.20 per million input tokens.
Cost Comparison
MiniMax M2.7 costs $0.30 per million input tokens and $1.20 per million output tokens, making it one of the cheapest Tier-1 agent models available.
| Model | Input / 1M Tokens | Output / 1M Tokens | Relative Cost (vs Claude Sonnet) |
|---|---|---|---|
| MiniMax M2.7 | $0.30 | $1.20 | ~10x cheaper on input |
| MiniMax M2.5 | $0.12 | $0.95 | ~25x cheaper on input |
| MiniMax-Text-01 | $0.20 | $1.10 | ~15x cheaper on input |
| GLM-4.7 | $0.39 | $1.75 | ~8x cheaper on input |
| Claude Sonnet 4.6 | $3.00 | $15.00 | baseline |
The cost advantage compounds quickly for high-volume OpenClaw operators. An agent processing 10 million tokens of input per day would cost $3.00/day on M2.7 versus $30.00/day on Claude Sonnet — a $810/month difference. For operators managing budgets carefully, MiniMax is one of the most practical options available. See the cheapest way to run OpenClaw for a broader cost analysis.
Limitations and Tradeoffs
MiniMax is a strong value play, but there are clear limits operators should understand.
- English-language benchmark gap: M2.7 approaches but does not consistently match the top-tier Western models on English-only benchmarks. If your work is entirely English and benchmark performance is the deciding factor, Claude or GPT-5 still lead.
- Ecosystem size: MiniMax has a smaller developer community and fewer third-party integrations than OpenAI or Anthropic. Debugging and community support are more limited.
- Self-evolution claims: MiniMax's "self-evolving" capability is genuine — the model did participate in its own scaffold optimization — but the practical benefit to end users is in the resulting benchmark scores, not in the model continuing to self-improve after deployment.
- Context window reliability: MiniMax-Text-01's 4M inference context is real, but performance at the extreme end of the context window degrades. Do not assume the model handles 4 million tokens as cleanly as it handles 200K.
- Data residency: MiniMax is a Chinese company. The same data sovereignty considerations that apply to other Chinese AI providers apply here.
When NOT to use MiniMax for OpenClaw: if you need guaranteed top-tier English reasoning, if compliance requirements prohibit Chinese-domiciled providers, or if you need the strongest possible multi-modal capabilities (MiniMax's vision models exist but are not as mature as Google's or OpenAI's).
Related Guides
- MiniMax M2 OpenClaw Guide
- OpenClaw MiniMax Setup
- Best Ollama Models for OpenClaw
- OpenClaw OpenRouter Setup
FAQ
What is the best MiniMax model for OpenClaw in 2026?
MiniMax M2.7 is the best MiniMax model for OpenClaw as of April 2026. It scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 while costing only $0.30 per million input tokens — making it one of the best cost-to-performance ratios available for agent workflows.
How much does MiniMax M2.7 cost per million tokens?
MiniMax M2.7 costs $0.30 per million input tokens and $1.20 per million output tokens. That is approximately 10x cheaper on input compared to Claude Sonnet 4.6 and makes it one of the most affordable Tier-1 agent models for OpenClaw.
Can I use MiniMax with OpenClaw through Ollama?
Yes. MiniMax M2.7 is available as a cloud model through Ollama, referenced as minimax-m2.7:cloud. This is useful when you want Ollama as the unified gateway for both local and cloud models in your OpenClaw setup.
What is MiniMax-Text-01's 4-million-token context useful for?
MiniMax-Text-01's 4M inference context is useful for processing very large documents — entire codebases, long legal contracts, full research paper collections, or book-length content — in a single session. However, performance degrades at the extreme end of the context window, so it works best for tasks that genuinely require very long input rather than as a general-purpose agent model.
How does MiniMax M2.7 compare to GLM-5 for OpenClaw?
MiniMax M2.7 is cheaper ($0.30 vs $1.00 per million input tokens) and activates fewer parameters (10B vs 40B), making it more efficient. GLM-5 scores higher on math benchmarks (92.7% AIME 2026 vs M2.7's coding focus) and has stronger bilingual Chinese-English support. For pure coding agent work, M2.7 is the better value. For bilingual or math-heavy workflows, GLM-5 has the edge.
Frequently Asked Questions
What is the best MiniMax model for OpenClaw in 2026?
MiniMax M2.7 is the best MiniMax model for OpenClaw as of April 2026. It scores 56.22% on SWE-Pro and 57.0% on Terminal Bench 2 while costing only $0.30 per million input tokens — making it one of the best cost-to-performance ratios available for agent workflows.
What is MiniMax-Text-01's 4-million-token context useful for?
MiniMax-Text-01's 4M inference context is useful for processing very large documents — entire codebases, long legal contracts, full research paper collections, or book-length content — in a single session. However, performance degrades at the extreme end of the context window, so it works best for tasks that genuinely require very long input rather than as a general-purpose agent model.
How does MiniMax M2.7 compare to GLM-5 for OpenClaw?
MiniMax M2.7 is cheaper ($0.30 vs $1.00 per million input tokens) and activates fewer parameters (10B vs 40B), making it more efficient. GLM-5 scores higher on math benchmarks (92.7% AIME 2026 vs M2.7's coding focus) and has stronger bilingual Chinese-English support. For pure coding agent work, M2.7 is the better value. For bilingual or math-heavy workflows, GLM-5 has the edge.