Model comparison
GLM-5.2 vs MiniMax-M2.7
GLM-5.2 is the stronger model overall, scoring 51.1 to 37.7 on the Noometry Index. MiniMax-M2.7 costs 4.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
MiniMaxSummary
- They share 26 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and MiniMax-M2.7 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.2 leads 55.7 to 25.9.
- The biggest single-benchmark swing is NYT Connections (extended): 74.3% for GLM-5.2 and 24.7% for MiniMax-M2.7.
- MiniMax-M2.7 is cheaper at $0.30 / $1.20 per million input/output tokens, against $1.40 / $4.40 for GLM-5.2.
- GLM-5.2 accepts more context: 1M tokens versus 205K.
Side by side
| GLM-5.2 | MiniMax-M2.7 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | MiniMax |
| Noometry Index | 51.1 | 37.7 |
| Released | 2026-06-13 | 2026-03-18 |
| Weights | Open | Open |
| Context window | 1M | 205K |
| Max output | 131K | 131K |
| Input $ / M tokens | $1.40 | $0.30 |
| Output $ / M tokens | $4.40 | $1.20 |
| Results tracked | 51 | 30 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), MiniMax-M2.7: 41.8 (#120)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| LMArena WebDev | 1603 | 1398 |
| SciCode | 50.5% | 47% |
| WeirdML | 70.1% | 37% |
| LMArena Coding | 1485 | 1454 |
| ALE-Bench | 1,047 | 599.25 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 32.4 (#63), MiniMax-M2.7: 25.1 (#111)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| GBAEval | 0% | 0% |
| Terminal-Bench | — | 45.1% |
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| ExploitBench | — | 13.3% |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), MiniMax-M2.7: 19.7 (#253)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| NYT Connections (extended) | 74.3% | 24.7% |
| CritPt | 20.9% | 0.6% |
| LMArena Hard Prompts | 1480 | 1422 |
| Epoch Capabilities Index | 151.78 | 145.85 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| Chess Puzzles | 21% | — |
| Thematic Generalization | — | 39.3% |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), MiniMax-M2.7: 25.9 (#263)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| ProofBench | 35% | 3% |
| LMArena Math | 1482 | 1420 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), MiniMax-M2.7: 37.7 (#152)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| LMArena Expert | 1486 | 1444 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| Vectara Hallucination Rate | — | 12.9% |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), MiniMax-M2.7: 50.3 (#123)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| LMArena Non-English | 1459 | 1382 |
| LMArena Chinese | 1519 | 1441 |
| LMArena French | 1479 | 1421 |
| LMArena German | 1468 | 1398 |
| LMArena Japanese | 1451 | 1262 |
| LMArena Korean | 1445 | 1313 |
| LMArena Russian | 1466 | 1383 |
| LMArena Spanish | 1477 | 1403 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), MiniMax-M2.7: 74.1 (#103)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| LMArena Instruction Following | 1465 | 1405 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), MiniMax-M2.7: 43.3 (#99)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| LMArena Longer Query | 1479 | 1419 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), MiniMax-M2.7: 58.9 (#112)
| Benchmark | GLM-5.2 | MiniMax-M2.7 |
|---|---|---|
| LMArena Text | 1470 | 1405 |
| LMArena Creative Writing | 1462 | 1354 |
| LMArena Multi-Turn | 1469 | 1412 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than MiniMax-M2.7?
GLM-5.2 is the stronger model overall, scoring 51.1 to 37.7 on the Noometry Index. MiniMax-M2.7 costs 4.1× less per token, which makes it the better buy when GLM-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-5.2 or MiniMax-M2.7?
MiniMax-M2.7 is cheaper. It lists at $0.30 per million input tokens and $1.20 per million output tokens; GLM-5.2 lists at $1.40 and $4.40.
Is GLM-5.2 or MiniMax-M2.7 better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 41.8 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 205K.
How many benchmarks do GLM-5.2 and MiniMax-M2.7 share?
26 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and MiniMax-M2.7 has 30.