MiniMax-M2.7
Released 18 Mar 2026$0.3 input, $1.2 output per million tokensopenAlso written as minimax/MiniMax-M2.7
3 results on 3 of 13 boards, latest measured Sep 2026. Compare with other models
Results
Each line sits on its own board's scale. Tall tick: the board leader.
Documentation and coding
- MedScribe (Vals AI)model ID minimax/MiniMax-M2.7; temperature=1; top_p=0.95; max_output_tokens=3000079.87Rank 56 of 105Leader Claude Opus 5.5 91.43
- MedCode (Vals AI)model ID minimax/MiniMax-M2.7; temperature=1; top_p=0.95; max_output_tokens=3000034.44Rank 76 of 103Leader Claude Opus 5 63.57
EHR and workflow agents
- PhysicianBenchPass@1 over 3 runs; Pass^3 1.0; shared FHIR tool harness; up to 100 turns; high reasoning when supported8.7Rank 25 of 28 here, 12 models on the boardLeader Claude Opus 5.5 (max) 68.4
Index working
The Clinical Benchmarks Index is this site's own calculation: a weighted average of the board scores times a multiplier for the boards behind it. How it is computed
- MedScribe (Vals AI)79.8779.9×0.25, near its ceilingNear its ceiling: leader 91.4, weight 0.25model ID minimax/MiniMax-M2.7; temperature=1; top_p=0.95; max_output_tokens=30000
- MedCode (Vals AI)34.4434.4×0.911model ID minimax/MiniMax-M2.7; temperature=1; top_p=0.95; max_output_tokens=30000
- PhysicianBench8.78.7×0.79Pass@1 over 3 runs; Pass^3 1.0; shared FHIR tool harness; up to 100 turns; high reasoning when supported
- Weighted average of 3 boards
- 29.8
- Multiplier for 3 of 10 boards
- × 0.707
- Index
- 29.8 × 0.707 = 21.1
Score: the result on the board’s own 0 to 100 scale. Dot: this model; tick: the board’s leader. Weight: how much the board counts in the weighted average; bold where the board is near its ceiling and counts for less. Each result links to its quote and page; the line under it is the setting it was measured with.
Sources
The quote, page and document for each of the 3 results
56MedScribe (Vals AI) MiniMax-M2.7 79.87
Printed as 79.87%Official leaderboard, measured Sep 2026Configuration: model ID minimax/MiniMax-M2.7; temperature=1; top_p=0.95; max_output_tokens=30000Vals AI MedScribe leaderboard official leaderboard, Vals AI, 3 Sep 2026. Vals AI MedScribe leaderboard, View: All Models, Task: Overall, row 56 of 105 (MiniMax-M2.7), Accuracy column; Updated 9/29/2026. Rendered BenchmarkView table; configuration from embedded astro-island BenchmarkView props, benchmarkView.default.tasks.overall["minimax/MiniMax-M2.7"].56 | MiniMax-M2.7 | 79.87%±1.86 | $0.3/$1.2 | 27.25s
Every result from this document76MedCode (Vals AI) MiniMax-M2.7 34.44
Printed as 34.44%Official leaderboard, measured Sep 2026Configuration: model ID minimax/MiniMax-M2.7; temperature=1; top_p=0.95; max_output_tokens=30000Vals AI MedCode leaderboard official leaderboard, Vals AI, 29 Sep 2026. MedCode leaderboard, Overall task, All Models expanded, rank 76 of 103; Accuracy column; board Updated 9/29/2026. Rendered table row; configuration from embedded BenchmarkView props benchmarkView.default.tasks.overall["minimax/MiniMax-M2.7"].76 | MiniMax-M2.7 | 34.44%±1.99 | $0.3/$1.2 | 30.25s
Every result from this document25PhysicianBench Pass@1 over 3 runs 8.7
Printed as 8.7 ± 1.2Official leaderboard, measured May 2026Configuration: Pass@1 over 3 runs; Pass^3 1.0; shared FHIR tool harness; up to 100 turns; high reasoning when supportedPhysicianBench: Evaluating LLM Agents in Real-World EHR Environments (arXiv 2605.02240v1 PDF) paper, Stanford University (HealthRex; Liu, Chen et al.), 4 May 2026. arXiv HTML 2605.02240v1, Section 5.2, Table 2; MiniMax M2.7 row, Pass@1 column (PDF p. 8).Model | Pass@1 | Pass@3 | Pass^3 | #Turns MiniMax M2.7 | 8.7 ± 1.2 | 15.9 | 1.0 | 29.7
Every result from this document
Other MiniMax models: MiniMax-M2.1, MiniMax M3