minimax-m2

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MiniMax M2 is a high-efficiency large language model built for coding and agentic workflows.

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minimax-m2:cloud

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MiniMax M2 is a high-efficiency large language model built for coding and agentic workflows.

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Highlights

Superior Intelligence. According to benchmarks from Artificial Analysis, MiniMax-M2 demonstrates highly competitive general intelligence across mathematics, science, instruction following, coding, and agentic tool use. Its composite score ranks #1 among open-source models globally.

Advanced Coding. Engineered for end-to-end developer workflows, MiniMax-M2 excels at multi-file edits, coding-run-fix loops, and test-validated repairs. Strong performance on Terminal-Bench and (Multi-)SWE-Bench–style tasks demonstrates practical effectiveness in terminals, IDEs, and CI across languages.

Agent Performance. MiniMax-M2 plans and executes complex, long-horizon toolchains across shell, browser, retrieval, and code runners. In BrowseComp-style evaluations, it consistently locates hard-to-surface sources, maintains evidence traceable, and gracefully recovers from flaky steps.

Efficient Design. With 10 billion activated parameters (230 billion in total), MiniMax-M2 delivers lower latency, lower cost, and higher throughput for interactive agents and batched sampling—perfectly aligned with the shift toward highly deployable models that still shine on coding and agentic tasks.

Coding & Agentic Benchmarks

These comprehensive evaluations test real-world end-to-end coding and agentic tool use: editing real repos, executing commands, browsing the web, and delivering functional solutions. Performance on this suite correlates with day-to-day developer experience in terminals, IDEs, and CI.

Benchmark MiniMax-M2 Claude Sonnet 4 Claude Sonnet 4.5 Gemini 2.5 Pro GPT-5 (thinking) GLM-4.6 Kimi K2 0905 DeepSeek-V3.2
SWE-bench Verified 69.4 72.7 * 77.2 * 63.8 * 74.9 * 68 * 69.2 * 67.8 *
Multi-SWE-Bench 36.2 35.7 * 44.3 / / 30 33.5 30.6
SWE-bench Multilingual 56.5 56.9 * 68 / / 53.8 55.9 * 57.9 *
Terminal-Bench 46.3 36.4 * 50 * 25.3 * 43.8 * 40.5 * 44.5 * 37.7 *
ArtifactsBench 66.8 57.3* 61.5 57.7* 73* 59.8 54.2 55.8
BrowseComp 44 12.2 19.6 9.9 54.9* 45.1* 14.1 40.1*
BrowseComp-zh 48.5 29.1 40.8 32.2 65 49.5 28.8 47.9*
GAIA (text only) 75.7 68.3 71.2 60.2 76.4 71.9 60.2 63.5
xbench-DeepSearch 72 64.6 66 56 77.8 70 61 71
HLE (w/ tools) 31.8 20.3 24.5 28.4 * 35.2 * 30.4 * 26.9 * 27.2 *
τ²-Bench 77.2 65.5* 84.7* 59.2 80.1* 75.9* 70.3 66.7
FinSearchComp-global 65.5 42 60.8 42.6* 63.9* 29.2 29.5* 26.2
AgentCompany 36 37 41 39.3* / 35 30 34

Notes: Data points marked with an asterisk (*) are taken directly from the model’s official tech report or blog. All other metrics were obtained using the evaluation methods described below. - SWE-bench Verified: We use the same scaffold as R2E-Gym (Jain et al. 2025) on top of OpenHands to test with agents on SWE tasks. All scores are validated on our internal infrastructure with 128k context length, 100 max steps, and no test-time scaling. All git-related content is removed to ensure agent sees only the code at the issue point. - Multi-SWE-Bench & SWE-bench Multilingual: All scores are averaged across 8 runs using the claude-code CLI (300 max steps) as the evaluation scaffold. - Terminal-Bench: All scores are evaluated with the official claude-code from the original Terminal-Bench repository(commit 94bf692), averaged over 8 runs to report the mean pass rate. - ArtifactsBench: All Scores are computed by averaging three runs with the official implementation of ArtifactsBench, using the stable Gemini-2.5-Pro as the judge model. - BrowseComp & BrowseComp-zh & GAIA (text only) & xbench-DeepSearch: All scores reported use the same agent framework as WebExplorer (Liu et al. 2025), with minor tools description adjustment. We use the 103-sample text-only GAIA validation subset following WebExplorer (Liu et al. 2025). - HLE (w/ tools): All reported scores are obtained using search tools and a Python tool. The search tools employ the same agent framework as WebExplorer (Liu et al. 2025), and the Python tool runs in a Jupyter environment. We use the text-only HLE subset. - τ²-Bench: All scores reported use “extended thinking with tool use”, and employ GPT-4.1 as the user simulator. - FinSearchComp-global: Official results are reported for GPT-5-Thinking, Gemini 2.5 Pro, and Kimi-K2. Other models are evaluated using the open-source FinSearchComp (Hu et al. 2025) framework using both search and Python tools, launched simultaneously for consistency. - AgentCompany: All scores reported use OpenHands 0.42 agent framework.

Intelligence Benchmarks

We align with Artificial Analysis (AA), which aggregates challenging benchmarks using a consistent methodology to reflect a model’s broader intelligence profile across math, science, instruction following, coding, and agentic tool use.

Metric (AA) MiniMax-M2 Claude Sonnet 4 Claude Sonnet 4.5 Gemini 2.5 Pro GPT-5 (thinking) GLM-4.6 Kimi K2 0905 DeepSeek-V3.2
AIME25 78 74 88 88 94 86 57 88
MMLU-Pro 82 84 88 86 87 83 82 85
GPQA-Diamond 78 78 83 84 85 78 77 80
HLE (w/o tools) 12.5 9.6 17.3 21.1 26.5 13.3 6.3 13.8
LiveCodeBench (LCB) 83 66 71 80 85 70 61 79
SciCode 36 40 45 43 43 38 31 38
IFBench 72 55 57 49 73 43 42 54
AA-LCR 61 65 66 66 76 54 52 69
τ²-Bench-Telecom 87 65 78 54 85 71 73 34
Terminal-Bench-Hard 24 30 33 25 31 23 23 29
AA Intelligence 61 57 63 60 69 56 50 57

AA: All scores of MiniMax-M2 aligned with Artificial Analysis Intelligence Benchmarking Methodology (https://artificialanalysis.ai/methodology/intelligence-benchmarking). All scores of other models reported from https://artificialanalysis.ai/.