Open-weight 230B/10B-active MoE tuned for coding agents and long-horizon tool use at high throughput and low cost.
Output Speed *
Intelligence Index *
Context Window *
Input price
Output price
Performance
Why 10B Active Parameters Change the Agent Loop
Benchmarks
MiniMax-M2 on Agentic and Coding Benchmarks
Output Speed
Intelligence Index
MMLU *
GPQA *
HLE *
LiveCodeBench *
Technical Specifications
What the model supports
Comparison
MiniMax-M2 vs GLM-4.6: Which Open-Weight Coding Agent?
MiniMax-M2 and GLM-4.6 are the two most realistic open-weight choices for coding and agent workloads, and they share near-identical context windows around 204.8K tokens. Both handle tool use and agentic execution well. <cite index="27-1,27-2">On independent testing MiniMax-M2 is faster, generating around 84.6 tokens per second versus GLM-4.6 (Reasoning) at 45.8.</cite> The practical decision is throughput and cost efficiency versus GLM-4.6's more comprehensive planning and documentation output. <cite index="32-1">GLM-4.6 is roughly 1.5x more expensive on input and 1.9x more expensive on output than MiniMax-M2.</cite>
Choose MiniMax-M2 when you run long agent sessions, high-volume automation, or budget-sensitive coding loops and want faster iteration at lower cost per task. <cite index="29-7,29-8">In head-to-head coding tests the M-series delivered the same functional result as GLM at roughly half the cost, though GLM produced more comprehensive planning and documentation.</cite> Choose GLM-4.6 when you value thorough documentation, more deliberate planning, and slightly higher reliability on complex builds, and can absorb its higher price and slower generation. For pure interactive speed and cost, M2 is the stronger default.
Limitations & Trade-offs
Best-Fit Workloads
Where this model earns its place
Autonomous coding agents
M2 is engineered for multi-file edits, compile-run-fix loops, and test-validated repairs across terminals, IDEs and CI. Its 10B active footprint keeps the plan-act-verify loop fast and cheap, so agents can iterate through debugging cycles without token cost ballooning. In practice it self-tests generated code, runs the CLI, and recovers from execution errors — well suited to CI-embedded assistants and IDE agents that must run for long autonomous stretches.
Long-horizon tool-use and browser agents
The model plans and executes complex toolchains across shell, browser, retrieval and code runners, maintaining traceable evidence and recovering gracefully from flaky steps in BrowseComp-style evaluations. Interleaved thinking with persistent traces supports multi-step decisions across many turns. This fits research agents, web-navigation automation, and orchestration pipelines — provided the harness preserves content between calls.
High-throughput, cost-sensitive automation
With above-average output speed and low compute per token, M2 suits batched sampling and high-volume generation where cost per task dominates model selection. Its open-weight MIT-modified license also allows self-hosting for teams that want to control serving. Ideal for large-scale code transformation, bulk repository operations, and pipeline automation where a frontier proprietary model would be economically impractical.
Multilingual software engineering
M2 shows strong performance on Multi-SWE-bench and SWE-bench Multilingual-style tasks, demonstrating practical effectiveness across programming languages in terminals and CI. This makes it a reasonable fit for polyglot codebases and internationalized engineering teams needing consistent behavior beyond Python-centric benchmarks — though for the highest multilingual coding accuracy, newer M-series releases score higher.