MiniMax M2.7 Official Release: SWE-Pro, VIBE-Pro, and Agent Workflow Benchmarks

One-sentence takeaway

The official results show M2.7 covering a broad range of real engineering, end-to-end project, and Office/Agent tasks: 56.22% on SWE-Pro, 55.6% on VIBE-Pro, 57.0% on Terminal Bench 2, 1495 on GDPval-AA, and 62.7% on MM Claw, although the complete harness remains undisclosed.

Test environment

• Model: MiniMax M2.7; official API/Agent and internal Agent harness.

• Engineering benchmarks: SWE-Pro, VIBE-Pro, Terminal Bench 2, NL2Repo, SWE Multilingual, and Multi SWE Bench.

• Office/Agent benchmarks: GDPval-AA, Toolathlon, MM Claw (40+ skills/complex work), and MLE Bench Lite (22 ML competitions).

• Configuration: Each benchmark was run by an official/internal harness; the article does not provide the complete inputs, sampling settings, repeat counts, costs, or confidence intervals for each test.

Inputs/configuration

The official cases include debugging workflows involving production logs/monitoring/deployment timelines/traces/databases/codebases, delivery of Web/Android/iOS/simulated projects, multi-round Word/Excel/PPT editing, research reports, and a TSMC revenue model. Some results came from internal complex skills, Agent Teams, and dynamic tool search.

Results data

• SWE-Pro: 56.22%; SWE Multilingual: 76.5; Multi SWE Bench: 52.7.

• VIBE-Pro: 55.6%; Terminal Bench 2: 57.0%; NL2Repo: 39.8%.

• GDPval-AA: ELO 1495, described by the official source as the highest among open-source models; Toolathlon: 46.3%.

• MM Claw: 62.7%, described by the official source as close to Sonnet 4.6; across 40+ complex skills, each exceeding 2,000 tokens, skill adherence was 97%.

• MLE Bench Lite: average medal rate of 66.6% across three 24-hour trials; the best run achieved 9 gold/5 silver/1 bronze.

Conclusion

M2.7 is suitable for Agents that need real-world engineering-system understanding, end-to-end delivery, and tool/skill orchestration. When using the public API, however, treat the official internal harness as an upper-bound reference and remeasure performance with the base model or your own scaffold.

Limitations

• These are vendor-published benchmarks; the complete prompts, task splits, harnesses, costs, failure samples, and statistical variance are undisclosed.

• Many figures depend on MiniMax's internal Agent Teams, memory, skills, and dynamic tool search, and cannot be attributed directly to the base model.

• GDPval-AA, MM Claw, SWE-Pro, and other metrics have different properties and should not be combined into one overall score.

• “reduced recovery time under three minutes” refers to multiple internal cases reported by the official source, not controlled average performance.

Reproduction steps

1. Clearly choose the API, open weights, vLLM/SGLang, or a custom Agent harness, and lock the version.

2. Select coding issues, end-to-end projects, Terminal, office-document, tool-calling, and long-running experiment tasks, and provide the same tools and permissions.

3. Record prompts, skill/tool definitions, call traces, patches, tests, costs, latency, and human intervention.

4. Report results separately by benchmark and harness, distinguishing base-model performance from gains due to Agent orchestration.