MiniMaxAI/MiniMax-M2.1
MiniMax-M2.1 is a SOTA open-source coding and agentic model with 230B total parameters and only 10B active per token (23:1 sparsity ratio). Released December 2025, it achieves 74% on SWE-bench Verified — competitive with Claude Sonnet 4.5 — at a fraction of the cost. It excels in multilingual code development (Python, Java, Go, Rust, C++, TypeScript, Kotlin), long-horizon agentic workflows, and office automation.
api_example.sh
Technical Specifications
Model Architecture & Performance
Pricing
Pay-per-use, no commitments
API Reference
Complete parameter documentation
| Parameter | Type | Default | Description |
|---|---|---|---|
| stream | boolean | true | Enable streaming responses for real-time output. |
| temperature | number | 1 | Recommended at 1.0 for best performance. |
| max_tokens | number | 8192 | Maximum number of tokens the model can generate. |
| top_p | number | 0.95 | Controls nucleus sampling. |
| top_k | number | 40 | Limits token sampling to top-k tokens. |
Explore the full request and response schema in our external API documentation
Performance
Strengths & considerations
| Strengths | Considerations |
|---|---|
| 74% SWE-bench Verified (competitive with Claude Sonnet 4.5) 230B MoE with only 10B active — extreme efficiency 200K context window Best-in-class polyglot coding (Java, Go, Rust, etc.) FP8 native quantization Open weights for local deployment | Less reliable than frontier closed models for deep debugging Sparse activation may miss niche language idioms Very large model size requires multi-GPU setup |
Use cases
Recommended applications for this model
Enterprise
Platform Integration
Docker Support
Official Docker images for containerized deployments
Kubernetes Ready
Production-grade KBS manifests and Helm charts
SDK Libraries
Official SDKs for Python, Javascript, Go, and Java
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