See key specs and per-benchmark scores for each model/mode. Scroll horizontally for all columns. 当前对比 2 个模型的评测数据与核心参数。
GLM 5.1
智谱AI
Best overall
GLM 5.1 · 65.63
Best single
MiniMax-M2.7 · GPQA Diamond 87.00
Modality coverage
GLM 5.1 · 1 modalities
Head to head
3
Benchmarks
2
Wins
1
Losses
+8.57
Average diff
Compare benchmark results across thinking modes and tool usage.
Data sourced primarily from official releases (GitHub, Hugging Face, papers), then benchmark leaderboards, then third-party evaluators. Learn about our data methodology
Complete scores for each model/mode across selected benchmarks.
3 benchmarks with comparable scores. Each model shows its best score; mode label is displayed below.
| Benchmark | GLM 5.1 | MiniMax-M2.7 |
|---|---|---|
GPQA Diamond 综合评估 | 86.20Thinking Enabled | 87.00Thinking Enabled |
HLE 综合评估 | 52.30Thinking Enabled ｜ Tools | 28.00Thinking Enabled |
SWE-Bench Pro - Public 编程与软件工程 | 58.40Thinking Enabled ｜ Tools | 56.20Thinking Enabled ｜ Tools |
Side-by-side input/output token pricing
Licensing, MoE architecture, and multi-modality support.
| Features & specs | GLM 5.1智谱AI | MiniMax-M2.7MiniMaxAI |
|---|---|---|
Core specsRelease | 2026-03-27 | 2026-03-18 |
Context length | 200K | 200K |
Parameters | 754 | 2290 |
Active parameters | 40 | 100 |
Max output | 128000 | 204800 |
MoE | Yes | Yes |
LicenseCode Open Source | Closed Source | Not provided |
Weights Open Source | Closed Source | Not provided |
Commercial use | 免费商用授权 | 不可以商用 |
Modality supportText Input/Output | / | Not provided |
ResourcesPaper / report | GLM-5.1: Towards Long-Horizon Tasks | MiniMax M2.7: Early Echoes of Self-Evolution |
DataLearner blog | Not provided | MiniMax M2.7 发布：模型开始帮自己训练自己 |
MiniMax-M2.7
MiniMaxAI
