GLM 5.1vsMiniMax-M2.7
Across 4 shared benchmarks, GLM 5.1 leads overall: GLM 5.1 wins 3, MiniMax-M2.7 wins 1, with 0 ties and an average score difference of +8.10.
GLM 5.1
智谱AI · 2026-03-27 · Reasoning model
MiniMax-M2.7
MiniMaxAI · 2026-03-18 · Reasoning model
GLM 5.13 wins(75%)(25%)1 winMiniMax-M2.7
Benchmark scores
Grouped by capability, sorted by largest gap within each. 4 shared benchmarks.
General Knowledge
GLM 5.1 2/3| Benchmark | GLM 5.1 | MiniMax-M2.7 | Diff |
|---|---|---|---|
| HLE | 52.3019 / 172Thinking (With Tools) | 2896 / 172Thinking (No Tools) | +24.30 |
| LiveBench | 70.1837 / 115Normal (No Tools) | 63.4956 / 115Deep Thinking (No Tools) | +6.69 |
| GPQA Diamond | 86.2047 / 187Thinking (No Tools) | 8742 / 187Thinking (No Tools) | -0.80 |
Coding and Software Engineer
GLM 5.1 1/1| Benchmark | GLM 5.1 | MiniMax-M2.7 | Diff |
|---|---|---|---|
| SWE-Bench Pro - Public | 58.4015 / 54Thinking (With Tools) | 56.2024 / 54Thinking (With Tools) | +2.20 |
Specs
| Field | GLM 5.1 | MiniMax-M2.7 |
|---|---|---|
| Publisher | 智谱AI | MiniMaxAI |
| Release date | 2026-03-27 | 2026-03-18 |
| Model type | Reasoning model | Reasoning model |
| Architecture | MoE | MoE |
| Parameters | 75.4B | 229B |
| Context length | 200K | 200K |
| Max output | 125K | 200K |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | GLM 5.1 | MiniMax-M2.7 |
|---|---|---|
| Text input | $1.4 / 1M tokens | $0.3 / 1M tokens |
| Text output | $4.4 / 1M tokens | $1.2 / 1M tokens |
| Cache read | $4.4 / 1M tokens | $0.06 / 1M tokens |
| Cache write | $0.26 / 1M tokens | $0.375 / 1M tokens |
Summary
- GLM 5.1leads in:General Knowledge (2/3), Coding and Software Engineer (1/1)
On average across the 4 shared benchmarks, GLM 5.1 scores 8.10 higher.
Largest single-benchmark gap: HLE — GLM 5.1 52.30 vs MiniMax-M2.7 28 (+24.30).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.