Model comparison
GLM-5.2 vs MiniMax-M2.1
GLM-5.2 is the stronger model overall, scoring 51.1 to 38.9 on the Noometry Index. MiniMax-M2.1 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 . 20 shared benchmarks.
MiniMaxSummary
- They share 20 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and MiniMax-M2.1 in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.3 to 16.6.
- The biggest single-benchmark swing is NYT Connections (extended): 74.3% for GLM-5.2 and 11.2% for MiniMax-M2.1.
- MiniMax-M2.1 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-M2.1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | MiniMax |
| Noometry Index | 51.1 | 38.9 |
| Released | 2026-06-13 | 2025-12-23 |
| 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 | 22 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 51.3 (#41), MiniMax-M2.1: 40.4 (#143)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| LMArena WebDev | 1603 | 1384 |
| LMArena Coding | 1485 | 1421 |
| ALE-Bench | 1,047 | 623.83 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| SciCode | 50.5% | — |
| WeirdML | 70.1% | — |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 32.4 (#63), MiniMax-M2.1: 27.9 (#98)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| Terminal-Bench | — | 36.6% |
| APEX-Agents | 45.2% | — |
| τ²-bench Banking | 37.1% | — |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.3 (#52), MiniMax-M2.1: 16.6 (#302)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| NYT Connections (extended) | 74.3% | 11.2% |
| LMArena Hard Prompts | 1480 | 1411 |
| ARC-AGI-2 | 22.8% | — |
| SimpleBench | 58.8% | — |
| Kagi LLM Benchmark | 62.6% | — |
| ARC-AGI-1 | 77% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| Epoch Capabilities Index | 151.78 | — |
Math GLM-5.2 leads
GLM-5.2: 55.7 (#43), MiniMax-M2.1: 38.3 (#138)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| LMArena Math | 1482 | 1397 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| OTIS Mock AIME 2024-2025 | 86.4% | — |
| ProofBench | 35% | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), MiniMax-M2.1: 38.3 (#147)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| LMArena Expert | 1486 | 1431 |
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| Vectara Hallucination Rate | — | 11.8% |
Multilingual GLM-5.2 leads
GLM-5.2: 55.8 (#26), MiniMax-M2.1: 50.0 (#128)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| LMArena Non-English | 1459 | 1378 |
| LMArena Chinese | 1519 | 1430 |
| LMArena French | 1479 | 1404 |
| LMArena German | 1468 | 1381 |
| LMArena Japanese | 1451 | 1287 |
| LMArena Korean | 1445 | 1298 |
| LMArena Russian | 1466 | 1387 |
| LMArena Spanish | 1477 | 1397 |
Instruction Following GLM-5.2 leads
GLM-5.2: 76.9 (#34), MiniMax-M2.1: 73.8 (#112)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| LMArena Instruction Following | 1465 | 1400 |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), MiniMax-M2.1: 43.2 (#101)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| LMArena Longer Query | 1479 | 1416 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), MiniMax-M2.1: 58.3 (#120)
| Benchmark | GLM-5.2 | MiniMax-M2.1 |
|---|---|---|
| LMArena Text | 1470 | 1392 |
| LMArena Creative Writing | 1462 | 1361 |
| LMArena Multi-Turn | 1469 | 1396 |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
Frequently asked questions
Is GLM-5.2 better than MiniMax-M2.1?
GLM-5.2 is the stronger model overall, scoring 51.1 to 38.9 on the Noometry Index. MiniMax-M2.1 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-M2.1?
MiniMax-M2.1 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-M2.1 better for coding?
GLM-5.2 scores higher on coding benchmarks: 51.3 versus 40.4 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-M2.1 share?
20 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and MiniMax-M2.1 has 22.