MiniMax-M2.7vsMiniMax M2.5
Across 11 shared benchmarks, MiniMax-M2.7 leads overall: MiniMax-M2.7 wins 7, MiniMax M2.5 wins 4, with 0 ties and an average score difference of +2.20.
MiniMax-M2.7
MiniMaxAI · 2026-03-18 · Reasoning model
MiniMax M2.5
MiniMaxAI · 2026-02-12 · Reasoning model
MiniMax-M2.77 wins(64%)(36%)4 winsMiniMax M2.5
Benchmark scores
Grouped by capability, sorted by largest gap within each. 11 shared benchmarks.
General Knowledge
MiniMax-M2.7 2/3| Benchmark | MiniMax-M2.7 | MiniMax M2.5 | Diff |
|---|---|---|---|
| HLE | 29.60208 / 563Thinking (No Tools) · Text only | 20.50281 / 563Thinking (No Tools) · Text only | +9.10 |
| LiveBench | 63.4957 / 117Deep Thinking (No Tools) | 60.1470 / 117Deep Thinking (No Tools) | +3.35 |
| CritPt | 0.60168 / 200Thinking (No Tools) | 1.10147 / 200Thinking (No Tools) | -0.50 |
Agent Level Benchmark
Even 2/2| Benchmark | MiniMax-M2.7 | MiniMax M2.5 | Diff |
|---|---|---|---|
| τ²-Bench - Telecom | 8589 / 264Thinking (With Tools) | 97.8012 / 264Thinking (With Tools) | -12.80 |
| Terminal Bench Hard | 3955 / 244Thinking (With Tools) | 34.8075 / 244Thinking (With Tools) | +4.20 |
Claw-style Agent Evaluation
MiniMax M2.5 2/2| Benchmark | MiniMax-M2.7 | MiniMax M2.5 | Diff |
|---|---|---|---|
| Pinch Bench | 87.1010 / 38Thinking (With Tools) | 87.807 / 38Thinking (With Tools) | -0.70 |
| Claw Bench | 91.705 / 29Thinking (With Tools) | 92.104 / 29Thinking (With Tools) | -0.40 |
AI Agent - Tool Usage
MiniMax-M2.7 1/1| Benchmark | MiniMax-M2.7 | MiniMax M2.5 | Diff |
|---|---|---|---|
| Terminal Bench 2.0 | 5725 / 48Thinking (With Tools) | 51.7032 / 48Thinking (With Tools) | +5.30 |
Coding and Software Engineer
MiniMax-M2.7 1/1| Benchmark | MiniMax-M2.7 | MiniMax M2.5 | Diff |
|---|---|---|---|
| SWE-Bench Pro - Public | 56.2029 / 62Thinking (With Tools) | 55.4031 / 62Thinking (With Tools) | +0.80 |
General Evaluation
MiniMax-M2.7 1/1| Benchmark | MiniMax-M2.7 | MiniMax M2.5 | Diff |
|---|---|---|---|
| GPQA Diamond | 87133 / 462Thinking (No Tools) | 85.20157 / 462Thinking (No Tools) | +1.80 |
Productivity Knowledge
MiniMax-M2.7 1/1| Benchmark | MiniMax-M2.7 | MiniMax M2.5 | Diff |
|---|---|---|---|
| GDPval-AA | 507 / 15Thinking (No Tools) | 3611 / 15Thinking (No Tools) | +14 |
Specs
| Field | MiniMax-M2.7 | MiniMax M2.5 |
|---|---|---|
| Publisher | MiniMaxAI | MiniMaxAI |
| Release date | 2026-03-18 | 2026-02-12 |
| Model type | Reasoning model | Reasoning model |
| Architecture | MoE | MoE |
| Parameters | 229B | 229B |
| Context length | 200K | 128K |
| Max output | 200K | Not available |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | MiniMax-M2.7 | MiniMax M2.5 |
|---|---|---|
| Text input | $0.3 / 1M tokens | $0.3 / 1M tokens |
| Text output | $1.2 / 1M tokens | $2.4 / 1M tokens |
| Cache read | $0.06 / 1M tokens | Not public |
| Cache write | $0.375 / 1M tokens | Not public |
Summary
- MiniMax-M2.7leads in:General Knowledge (2/3), AI Agent - Tool Usage (1/1), Coding and Software Engineer (1/1), General Evaluation (1/1), Productivity Knowledge (1/1)
- MiniMax M2.5leads in:Claw-style Agent Evaluation (2/2)
- Tied in:Agent Level Benchmark
On average across the 11 shared benchmarks, MiniMax-M2.7 scores 2.20 higher.
Largest single-benchmark gap: GDPval-AA — MiniMax-M2.7 50 vs MiniMax M2.5 36 (+14).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.