Model comparison
GLM-5.2 vs o1-mini
GLM-5.2 is the stronger model overall, scoring 50.9 to 33.7 on the Noometry Index.
Last verified . 24 shared benchmarks.
Summary
- They share 24 benchmarks with published results for both. GLM-5.2 scores higher in 9 categories and o1-mini in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.2 leads 42.1 to 8.9.
- The biggest single-benchmark swing is ARC-AGI-1: 77% for GLM-5.2 and 14% for o1-mini.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.2 | o1-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 50.9 | 33.7 |
| Released | 2026-06-13 | 2024-09-12 |
| Weights | Open | Proprietary |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $1.40 | — |
| Output $ / M tokens | $4.40 | — |
| Results tracked | 50 | 39 |
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Category by category
Coding GLM-5.2 leads
GLM-5.2: 50.9 (#37), o1-mini: 35.1 (#218)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| WeirdML | 70.1% | 36.3% |
| LMArena Coding | 1484 | 1362 |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| Aider Polyglot | — | 32.9% |
| LMArena WebDev | 1604 | — |
| SciCode | 50.5% | — |
| LiveBench Coding | — | 48% |
| ALE-Bench | 1,047 | — |
| HumanEval+ | — | 89% |
| MBPP+ | — | 78.8% |
Agentic & Tool Use GLM-5.2 leads
GLM-5.2: 31.6 (#63), o1-mini: 24.3 (#112)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| τ²-bench Banking | 37.1% | — |
| Cybench | — | 10% |
| PostTrainBench | 31.7% | — |
| GBAEval | 0% | — |
| Vending-Bench 2 | 8,314 | — |
Reasoning GLM-5.2 leads
GLM-5.2: 42.1 (#46), o1-mini: 8.9 (#340)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| ARC-AGI-2 | 22.8% | 0.8% |
| SimpleBench | 58.8% | 18.1% |
| ARC-AGI-1 | 77% | 14% |
| LMArena Hard Prompts | 1480 | 1333 |
| Epoch Capabilities Index | 151.76 | 135.82 |
| Kagi LLM Benchmark | 62.6% | — |
| NYT Connections (extended) | 74.3% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| LiveBench Reasoning | — | 72.3% |
| Mystery Game Puzzles | 19% | — |
| DTBench | 93.6% | — |
| LiveBench Data Analysis | — | 57.9% |
| LMCA | 45.8% | — |
| Surface Evolver Bench | 55.6% | — |
| LiveBench | — | 57.8% |
Math GLM-5.2 leads
GLM-5.2: 55.4 (#35), o1-mini: 33.7 (#168)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 86.4% | 46.9% |
| LMArena Math | 1482 | 1358 |
| FrontierMath (Tiers 1-3) | 59.2% | — |
| FrontierMath Tier 4 | 29.3% | — |
| MathArena Final-Answer Competitions | 67.6% | — |
| ProofBench | 35% | — |
| LiveBench Math | — | 62% |
| MATH Level 5 | — | 89.2% |
| FrontierMath (Feb 2025 set) | — | 1.7% |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#36), o1-mini: 34.8 (#180)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| GPQA Diamond | 91.9% | 62.4% |
| LMArena Expert | 1484 | 1316 |
| SimpleQA Verified | 34.2% | — |
| Confabulations | — | 18.6% |
Multilingual GLM-5.2 leads
GLM-5.2: 56.0 (#25), o1-mini: 43.8 (#177)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| LMArena Non-English | 1459 | 1289 |
| LMArena Chinese | 1518 | 1314 |
| LMArena French | 1476 | 1292 |
| LMArena German | 1470 | 1278 |
| LMArena Japanese | 1451 | 1245 |
| LMArena Korean | 1445 | 1223 |
| LMArena Russian | 1467 | 1283 |
| LMArena Spanish | 1477 | 1302 |
Instruction Following GLM-5.2 leads
GLM-5.2: 77.1 (#32), o1-mini: 67.0 (#200)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| LMArena Instruction Following | 1465 | 1304 |
| LiveBench Instruction Following | — | 65.4% |
Long Context GLM-5.2 leads
GLM-5.2: 45.3 (#43), o1-mini: 40.1 (#156)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| LMArena Longer Query | 1479 | 1321 |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.7 (#20), o1-mini: 48.6 (#196)
| Benchmark | GLM-5.2 | o1-mini |
|---|---|---|
| LMArena Text | 1470 | 1317 |
| LMArena Creative Writing | 1465 | 1244 |
| LMArena Multi-Turn | 1468 | 1314 |
| Short-Story Creative Writing | — | 64.9% |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
| LiveBench Language | — | 40.9% |
Frequently asked questions
Is GLM-5.2 better than o1-mini?
GLM-5.2 is the stronger model overall, scoring 50.9 to 33.7 on the Noometry Index.
Is GLM-5.2 or o1-mini better for coding?
GLM-5.2 scores higher on coding benchmarks: 50.9 versus 35.1 in the Noometry coding category.
How many benchmarks do GLM-5.2 and o1-mini share?
24 benchmarks have published results for both models. GLM-5.2 has 50 scored results on Noometry and o1-mini has 39.