MiniMax-M2.7vsM2.1
Across 6 shared benchmarks, MiniMax-M2.7 leads overall: MiniMax-M2.7 wins 5, M2.1 wins 1, with 0 ties and an average score difference of +7.07.
MiniMax-M2.7
MiniMaxAI · 2026-03-18 · Reasoning model
M2.1
MiniMaxAI · 2025-12-23 · AI model
MiniMax-M2.75 wins(83%)(17%)1 winM2.1
Benchmark scores
Grouped by capability, sorted by largest gap within each. 6 shared benchmarks.
General Knowledge
MiniMax-M2.7 2/2| Benchmark | MiniMax-M2.7 | M2.1 | Diff |
|---|---|---|---|
| GPQA Diamond | 8735 / 175Thinking (No Tools) | 8166 / 175thinking | +6 |
| HLE | 2874 / 149Thinking (No Tools) | 2286 / 149thinking | +6 |
Agent Level Benchmark
M2.1 1/1| Benchmark | MiniMax-M2.7 |
|---|
Specs
| Field | MiniMax-M2.7 | M2.1 |
|---|---|---|
| Publisher | MiniMaxAI | MiniMaxAI |
| Release date | 2026-03-18 | 2025-12-23 |
| Model type | Reasoning model | AI model |
| Architecture | MoE | MoE |
| Parameters | 2290.0 | 2300.0 |
| Context length | 200K | 200K |
| Max output | 204800 | 131072 |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | MiniMax-M2.7 | M2.1 |
|---|---|---|
| Text input | $0.3 / 1M tokens | 0.3 美元/100 万tokens |
| Text output | $1.2 / 1M tokens | 1.2 美元/100 万tokens |
| Cache read | $0.06 / 1M tokens | 0.03 美元/100 万tokens |
| Cache write | $0.375 / 1M tokens | 0.375 美元/100 万tokens |
Summary
- MiniMax-M2.7leads in:General Knowledge (2/2), Claw-style Agent Evaluation (1/1), Coding and Software Engineer (1/1), Instruction Following (1/1)
- M2.1leads in:Agent Level Benchmark (1/1)
On average across the 6 shared benchmarks, MiniMax-M2.7 scores 7.07 higher.
Largest single-benchmark gap: SWE-Bench Pro - Public — MiniMax-M2.7 56.20 vs M2.1 32.60 (+23.60).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.