Open-weight model uniting frontier coding, 1M-token context, and native image/video input for long-horizon agents.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Where MiniMax M3 Earns Its Place: Long-Horizon Coding and Agents
Benchmarks
MiniMax M3 Benchmarks: Coding, Tool Use, and Autonomous Browsing
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
MiniMax M3 vs MiniMax M2.7: What Changes for Long-Context Agents?
MiniMax M2.7 is M3's direct predecessor and remains the practical alternative for teams already integrated with the M-series. Both target coding and agent workloads, but the architecture differs sharply. M2.7 ships a roughly 200K-token context window and is text-focused with a highspeed variant; M3 introduces MSA, extending context to 1M tokens, adds native image and video input, and reports higher engineering-benchmark scores. On SWE-Bench Pro, M3's 59.0% edges past M2.7's 56.2%. The core decision is whether you need M3's much larger context and multimodality, or whether M2.7's smaller, stable, well-integrated context is sufficient.
Choose MiniMax M3 when you need whole-codebase or long-transcript context, native image/video understanding, or long-horizon autonomous agent runs where MSA's low long-context compute cost matters. Choose MiniMax M2.7 when your workflows fit comfortably within ~200K tokens, you are already integrated with its endpoints and highspeed variant, or you want the more established, text-only option without migrating SDK, billing, or endpoint configuration. M3 does not dominate every row — frontier closed models still lead some coding tasks — so validate against your own workloads before switching.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Long-horizon coding agents
M3 is oriented toward multi-step, tool-driven development rather than single completions, with autonomous demonstrations running 12–24 hours — including a CUDA kernel optimization that improved hardware utilization across nearly 2,000 tool calls without human intervention. Combined with 1M-token context and reported 59.0% SWE-Bench Pro, this fits repository-wide refactoring, bugfixing, and implementation agents. Validate on your codebase, since vendor benchmarks are pending independent confirmation.
Whole-codebase and long-document RAG
The MSA architecture makes the 1M-token context economically usable, cutting per-token compute at long context to roughly 1/20 of the previous generation with much faster prefill and decode. This supports feeding entire codebases, large log sets, or multi-document retrieval packs in a single request without the prohibitive long-context cost of full attention. Ideal for code-understanding assistants and research pipelines that reason across large corpora.
Autonomous browsing and tool-use agents
M3 reports strong agentic tool-use results, including 74.2% on MCP Atlas and 83.5 on BrowseComp for autonomous web research, and it can operate a desktop computer. This suits research agents, browser automation, and office-workflow assistants that plan, invoke tools, and iterate. Confirm tool-calling support on your chosen endpoint, as some third-party routes restrict function calling.
Grounded multimodal analysis
Because M3 is natively multimodal from training step zero, it handles screenshots, documents, charts, and video frames alongside text in one workflow — independent profiling rates its multimodal and grounded category among its stronger areas. This fits UI analysis, chart-to-code, document review, and visual QA tasks. Remember output is text only, so pair M3 with a generative media model if you need image, audio, or video output.