MiniMax-M2.7vsGLM-5
Across 6 shared benchmarks, MiniMax-M2.7 leads overall: MiniMax-M2.7 wins 3, GLM-5 wins 2, with 1 ties and an average score difference of -0.12.
MiniMax-M2.7
MiniMaxAI · 2026-03-18 · Reasoning model
GLM-5
智谱AI · 2026-02-11 · Chat model
MiniMax-M2.73 wins(50%)Ties1(33%)2 winsGLM-5
Benchmark scores
Grouped by capability, sorted by largest gap within each. 6 shared benchmarks.
Claw-style Agent Evaluation
MiniMax-M2.7 1/2| Benchmark | MiniMax-M2.7 | GLM-5 | Diff |
|---|---|---|---|
| Pinch Bench | 87.1010 / 38Thinking (With Tools) | 86.4013 / 38Thinking (With Tools) | +0.70 |
| Claw Bench | 91.705 / 29Thinking (With Tools) | 91.705 / 29Thinking (With Tools) | — |
Agent Level Benchmark
MiniMax-M2.7 1/1| Benchmark | MiniMax-M2.7 | GLM-5 | Diff |
|---|---|---|---|
| τ³-Banking | 9.90129 / 164Thinking (With Tools) | 9.79130 / 164Thinking (With Tools) | +0.11 |
AI Agent - Tool Usage
GLM-5 1/1| Benchmark | MiniMax-M2.7 | GLM-5 | Diff |
|---|---|---|---|
| Terminal Bench 2.0 | 5725 / 48Thinking (With Tools) | 61.1018 / 48Thinking (With Tools) | -4.10 |
General Knowledge
GLM-5 1/1| Benchmark | MiniMax-M2.7 | GLM-5 | Diff |
|---|---|---|---|
| CritPt | 0.60168 / 200Thinking (No Tools) | 2127 / 200Thinking (No Tools) | -1.40 |
Productivity Knowledge
MiniMax-M2.7 1/1| Benchmark | MiniMax-M2.7 | GLM-5 | Diff |
|---|---|---|---|
| GDPval-AA | 507 / 15Thinking (No Tools) | 468 / 15Thinking (No Tools) | +4 |
Specs
| Field | MiniMax-M2.7 | GLM-5 |
|---|---|---|
| Publisher | MiniMaxAI | 智谱AI |
| Release date | 2026-03-18 | 2026-02-11 |
| Model type | Reasoning model | Chat model |
| Architecture | MoE | MoE |
| Parameters | 229B | 744B |
| Context length | 200K | 200K |
| Max output | 200K | 128K |
API pricing
Prices use DataLearner records when available; missing fields are not inferred.
| Item | MiniMax-M2.7 | GLM-5 |
|---|---|---|
| Text input | $0.3 / 1M tokens | $1 / 1M tokens |
| Text output | $1.2 / 1M tokens | $3.2 / 1M tokens |
| Cache read | $0.06 / 1M tokens | Not public |
| Cache write | $0.375 / 1M tokens | $0.2 / 1M tokens |
Summary
- MiniMax-M2.7leads in:Claw-style Agent Evaluation (1/2), Agent Level Benchmark (1/1), Productivity Knowledge (1/1)
- GLM-5leads in:AI Agent - Tool Usage (1/1), General Knowledge (1/1)
On average across the 6 shared benchmarks, GLM-5 scores 0.12 higher.
Largest single-benchmark gap: Terminal Bench 2.0 — MiniMax-M2.7 57 vs GLM-5 61.10 (-4.10).
Page generated from structured model, pricing and benchmark records. No real-time LLM is used to write the prose.