Model detail
MiniMax-M2
MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning, tool use, and multi-step task execution while maintaining low latency and deployment efficiency. The model excels in code generation, multi-file editing, compile-run-fix loops, and test-validated repair, showing strong results on SWE-Bench Verified, Multi-SWE-Bench, and Terminal-Bench. It also performs competitively in agentic evaluations such as BrowseComp and GAIA, effectively handling long-horizon planning, retrieval, and recovery from execution errors. Benchmarked by [Artificial Analysis](https://artificialanalysis.ai/models/minimax-m2), MiniMax-M2 ranks among the top open-source models for composite intelligence, spanning mathematics, science, and instruction-following. Its small activation footprint enables fast inference, high concurrency, and improved unit economics, making it well-suited for large-scale agents, developer assistants, and reasoning-driven applications that require responsiveness and cost efficiency. To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our [docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks).
Model specs
- Model ID
-
MiniMax-M2
- Context length
- 204.8K
- Max output
- 131.072K
- I/O modalities
- Released
- 2025-10
02
API endpoints
One MixRoute gateway, OpenAI-compatible
-
OpenAI-compatible
/v1/chat/completionsPOST
03
Pricing
Flat rate, unit: /1M Tokens
Input
$0.3000 /1M Tokens
Completion
$1.2000 /1M Tokens
Cache read
$0.0300 /1M Tokens
Cache creation
$0.3750 /1M Tokens
05
Selection summary
Quickly judge whether it fits your workload
What is this model good for?
Compact, high-efficiency large language model optimized for end-to-end coding
MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows.
What should you check before using it?
204,800 context, 131,072 output tokens
Model type: text. Verify supported features and quotas with the provider documentation. Knowledge cutoff: 2025-06. Released 2025-10-23.
Why use it through MixRoute?
Swap the base URL, keep the MiniMax-M2 client
MixRoute serves MiniMax-M2 on the OpenAI-compatible endpoint /v1/chat/completions; point your existing client at https://api.mixroute.ai/v1 with the model ID MiniMax-M2 and re-run your integration tests.
07
Capabilities & compatibility
Unconfirmed capabilities stay "Unknown," no guesses
Tool calling
MiniMax-M2 supports function and tool calls.
tools definition; the response returns tool_calls to execute and feed results back.
Structured output
MiniMax-M2 can produce JSON-formatted output on demand.
response_format: { type: "json_schema" } forces valid JSON output.
Streaming
MiniMax-M2 supports streamed responses.
stream: true streams tokens incrementally, so chat UIs render as they arrive.
Prompt caching
MiniMax-M2 supports prompt caching under supported routing conditions.
OpenAI SDK
Call MiniMax-M2 through MixRoute's OpenAI-compatible endpoint.
base_url to https://api.mixroute.ai/v1; everything else follows the OpenAI style.
08
Token cost estimator
Live estimate from this page's pricing, not an actual bill
Share of the same prefix read repeatedly, up to 100%
09
FAQ
MiniMax-M2 sits alongside glm-4.5, gpt-4.1, gpt-4.1-2025-04-14 in the catalog. Compare them on the dimensions that change your bill and your users’ experience: task quality on your own prompts, latency at your concurrency, context and output limits, and cost per completed task rather than per token.
One endpoint, a testable decision
Test this model and alternate routes with the same request format
Start from a real workload, then let quality, total cost, and failure conditions decide whether to send production traffic.