GLM 5.1vsMiniMax-M2.7
Across 3 shared benchmarks, GLM 5.1 leads overall: GLM 5.1 wins 2, MiniMax-M2.7 wins 1, with 0 ties and an average score difference of +8.57.
GLM 5.1
智谱AI · 2026-03-27 · Reasoning model
MiniMax-M2.7
MiniMaxAI · 2026-03-18 · Reasoning model
GLM 5.12 wins(67%)(33%)1 winMiniMax-M2.7
Benchmark scores
Grouped by capability, sorted by largest gap within each. 3 shared benchmarks.
General Knowledge
Even 2/2| Benchmark | GLM 5.1 | MiniMax-M2.7 | Diff |
|---|---|---|---|
| HLE | 52.3012 / 157Thinking (With Tools) | 2882 / 157Thinking (No Tools) | +24.30 |
| GPQA Diamond | 86.2042 / 178Thinking (No Tools) | 8738 / 178Thinking (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 | 58.409 / 43Thinking (With Tools) | 56.2016 / 43Thinking (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:Coding and Software Engineer (1/1)
- Tied in:General Knowledge
On average across the 3 shared benchmarks, GLM 5.1 scores 8.57 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.