Independent benchmarks question MiniMax M2.7 self-improvement claims

The discrepancy between vendor claims and independent benchmarks highlights the challenges of evaluating multi-turn, agentic models with traditional single-turn evals.

* M2.7 scored an average of 8.46 across 13 evaluations, virtually tying with the older M1 model (8.47) and well below GPT-5.4 (9.26).

* On targeted self-improvement tests, M2.7 tied for 1st in recursive optimization but fell short in iterative code improvement and debugging reasoning chains.

* The evaluator used external frontier judges (Claude Sonnet 4.6, GPT-5.4, Gemini 3.1 Pro) to reduce noise from same-family judging.

* Acknowledging the limitations of single-turn testing for an agentic model, the author is seeking community input on designing appropriate multi-turn evaluation harnesses.