POSTopenai
/v1/chat/completionsMiniMax M3
MiniMax-M3MiniMax M3 is a frontier multimodal MiniMax model with a 1M-token context window, built for long-horizon agent workflows, coding, and tool use. It uses MiniMax Sparse Attention to reduce the cost of large-context workloads compared with earlier generations. Use the MiniMax API through TokenHub to review the current MiniMax M3 price, model ID, supported endpoints, context limits, and benchmark results before deploying it for production software tasks or collaborative agent workflows.
Total Context
512Ktokens
Max Output
128Ktokens
Released
Jun 1, 2026
Modalities
MiniMax M3 Price
| Input Price | Output Price | Cache Read |
|---|---|---|
| $0.6/M | $2.4/M | $0.12/M |
How do I use MiniMax M3 through the API?
POSTanthropic
/v1/messagesPOSTgemini
/v1beta/models/{model}:generateContentMiniMax M3 Benchmark
MiniMax-M3
44.4
/100
Artificial Analysis Intelligence Index
Artificial Analysis broad capability aggregate
Index score
43.4
/100
Artificial Analysis Coding Index
Artificial Analysis software task aggregate
Index score
Knowledge & Reasoning
GPQA
Advanced science problem solving
92.9%
HLE
Broad expert-level exam set
37.1%
Coding & Engineering
SciCode
Scientific coding challenges
45.4%
Terminal-Bench Hard
Hard terminal task execution
42.4%
Instruction Following & Agent Tasks
IFBench
Prompt constraint adherence
82.9%
AA-LCR
Long-context reasoning
74%
τ²-Bench
Agent workflow tasks
88.9%
Metrics sourced from Artificial Analysis
MiniMax M3 API FAQs
Answers to common questions about the MiniMax M3 API, pricing, coding and agent workflows, long-context limits, and model comparisons on TokenHub.
What is MiniMax M3?+
MiniMax M3 is a MiniMax multimodal model for long-horizon agent work, coding, and tool use. Its model profile emphasizes large-context workloads and production-oriented software tasks where a model needs to work across substantial project information.
Is MiniMax M3 suitable for coding and agent workflows?+
MiniMax M3 is worth evaluating for coding and agent workflows that need long context, repeated tool use, or collaboration across multiple task steps. Before adopting it in production, test it with representative repository tasks, tool calls, and completion criteria from your own workflow.
How do I use the MiniMax M3 API through TokenHub?+
Create a TokenHub API key, copy the exact MiniMax M3 model ID shown on this page, and choose a supported endpoint in the API section. Use the generated request example as the starting point for your OpenAI-compatible client or other supported integration.
How should I evaluate MiniMax M3 price?+
Review the current input, output, and cache-read prices on this page, then estimate cost using your typical prompt size, response length, request volume, and cache-hit rate. Long-context and agent workflows can have a very different cost profile from a short text-only request.
What should I check before using MiniMax M3 for a long-context task?+
Check the current context window, maximum output, input modalities, supported endpoints, and pricing shown in the model specifications. A large context limit does not remove the need to structure documents, control tool loops, and test how the model performs with your real project data.
MiniMax M3 vs Kimi K3: which model should I choose?+
Compare MiniMax M3 and Kimi K3 against the workload you need to ship. Review the current API price, context and output limits, modalities, supported endpoints, and benchmark data, then run a small evaluation using your own coding, reasoning, or agent tasks before committing to either model.
Where can I find a MiniMax API key and the current MiniMax M3 model ID?+
Create an API key in the TokenHub workspace, then copy the exact MiniMax M3 model ID displayed on this page. Confirm the supported endpoint and current specifications here before sending production traffic, because availability and model details can change.
Media and Discussions
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