MiniMax-M2.7vsKimi K2.5
Across 7 shared benchmarks, MiniMax-M2.7 leads overall: MiniMax-M2.7 wins 5, Kimi K2.5 wins 2, with 0 ties and an average score difference of +1.29.
MiniMax-M2.7
MiniMaxAI · 2026-03-18 · Reasoning model
Kimi K2.5
Moonshot AI · 2026-01-27 · Multimodal model
MiniMax-M2.75 wins(71%)(29%)2 winsKimi K2.5
Benchmark scores
Grouped by capability, sorted by largest gap within each. 7 shared benchmarks.
Claw-style Agent Evaluation
MiniMax-M2.7 2/2| Benchmark | MiniMax-M2.7 | Kimi K2.5 | Diff |
|---|---|---|---|
| Claw Bench | 91.705 / 29Thinking (With Tools) | 81.7018 / 29Thinking (With Tools) | +10 |
| Pinch Bench | 87.109 / 37Thinking (With Tools) | 84.8017 / 37Thinking (With Tools) | +2.30 |
General Knowledge
Kimi K2.5 2/2| Benchmark |
|---|
Specs
| Field | MiniMax-M2.7 | Kimi K2.5 |
|---|---|---|
| Publisher | MiniMaxAI | Moonshot AI |
| Release date | 2026-03-18 | 2026-01-27 |
| Model type | Reasoning model | Multimodal model |
| Architecture | MoE | MoE |
| Parameters | 2290.0 | 10000.0 |
| Context length | 200K | 256K |
| Max output | 204800 | 16384 |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | MiniMax-M2.7 | Kimi K2.5 |
|---|---|---|
| Text input | $0.3 / 1M tokens | 0.6 美元/100 万tokens |
| Text output | $1.2 / 1M tokens | 3 美元/100 万tokens |
| Cache read | $0.06 / 1M tokens | 0.1 美元/100 万tokens |
| Cache write | $0.375 / 1M tokens | Not public |
Summary
- MiniMax-M2.7leads in:Claw-style Agent Evaluation (2/2), Coding and Software Engineer (1/1), Long Context (1/1), Productivity Knowledge (1/1)
- Kimi K2.5leads in:General Knowledge (2/2)
On average across the 7 shared benchmarks, MiniMax-M2.7 scores 1.29 higher.
Largest single-benchmark gap: HLE — MiniMax-M2.7 28 vs Kimi K2.5 50.20 (-22.20).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.