Model comparison
GLM-5.2 vs o3-pro
GLM-5.2 is the stronger model overall, scoring 51.1 to 42.9 on the Noometry Index.
Last verified . 7 shared benchmarks.
Summary
- They share 7 benchmarks with published results for both. GLM-5.2 scores higher in 3 categories and o3-pro in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.2 leads 57.1 to 29.5.
- The biggest single-benchmark swing is ARC-AGI-2: 22.8% for GLM-5.2 and 4.9% for o3-pro.
- GLM-5.2 is cheaper at $1.40 / $4.40 per million input/output tokens, against $20 / $80 for o3-pro.
- GLM-5.2 accepts more context: 1M tokens versus 200K.
- GLM-5.2 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.2 | o3-pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.1 | 42.9 |
| Released | 2026-06-13 | 2025-06-10 |
| Weights | Open | Proprietary |
| Context window | 1M | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $1.40 | $20 |
| Output $ / M tokens | $4.40 | $80 |
| Results tracked | 51 | 12 |
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Category by category
Coding o3-pro leads
GLM-5.2: 51.3 (#41), o3-pro: 55.5 (#24)
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| WeirdML | 70.1% | 58.2% |
| SWE-bench Verified | 78.7% | — |
| DeepSWE | 43.8% | — |
| FrontierCode | 24.5% | — |
| Aider Polyglot | — | 84.9% |
| LMArena WebDev | 1603 | — |
| SciCode | 50.5% | — |
| LMArena Coding | 1485 | — |
| ALE-Bench | 1,047 | — |
Agentic & Tool Use Not comparable
GLM-5.2: 32.4 (#63), o3-pro: —
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| 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), o3-pro: 23.8 (#171)
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| ARC-AGI-2 | 22.8% | 4.9% |
| Kagi LLM Benchmark | 62.6% | 72.1% |
| ARC-AGI-1 | 77% | 59.3% |
| DTBench | 93.6% | 86.9% |
| LMCA | 45.8% | 38.5% |
| Epoch Capabilities Index | 151.78 | 147.42 |
| SimpleBench | 58.8% | — |
| NYT Connections (extended) | 74.3% | — |
| CritPt | 20.9% | — |
| Chess Puzzles | 21% | — |
| EBR-Bench | 9.5% | — |
| LMArena Hard Prompts | 1480 | — |
| Mystery Game Puzzles | 19% | — |
| Surface Evolver Bench | 55.6% | — |
Math Not comparable
GLM-5.2: 55.7 (#43), o3-pro: —
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| 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% | — |
| LMArena Math | 1482 | — |
Knowledge GLM-5.2 leads
GLM-5.2: 57.1 (#40), o3-pro: 29.5 (#238)
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| GPQA Diamond | 91.9% | — |
| SimpleQA Verified | 34.2% | — |
| Confabulations | — | 14.2% |
| Vectara Hallucination Rate | — | 23.3% |
| LMArena Expert | 1486 | — |
Multilingual Not comparable
GLM-5.2: 55.8 (#26), o3-pro: —
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| LMArena Non-English | 1459 | — |
| LMArena Chinese | 1519 | — |
| LMArena French | 1479 | — |
| LMArena German | 1468 | — |
| LMArena Japanese | 1451 | — |
| LMArena Korean | 1445 | — |
| LMArena Russian | 1466 | — |
| LMArena Spanish | 1477 | — |
Instruction Following Not comparable
GLM-5.2: 76.9 (#34), o3-pro: —
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| LMArena Instruction Following | 1465 | — |
Long Context o3-pro leads
GLM-5.2: 45.3 (#43), o3-pro: 72.2 (#1)
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| Fiction.LiveBench | — | 97.2% |
| LMArena Longer Query | 1479 | — |
Writing & Preference GLM-5.2 leads
GLM-5.2: 70.4 (#21), o3-pro: 57.1 (#133)
| Benchmark | GLM-5.2 | o3-pro |
|---|---|---|
| LMArena Text | 1470 | — |
| LMArena Creative Writing | 1462 | — |
| Short-Story Creative Writing | — | 84.4% |
| EQ-Bench Creative Writing | 1757 | — |
| EQ-Bench 4 | 1222 | — |
| LMArena Multi-Turn | 1469 | — |
Frequently asked questions
Is GLM-5.2 better than o3-pro?
GLM-5.2 is the stronger model overall, scoring 51.1 to 42.9 on the Noometry Index.
Which is cheaper, GLM-5.2 or o3-pro?
GLM-5.2 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; o3-pro lists at $20 and $80.
Is GLM-5.2 or o3-pro better for coding?
o3-pro scores higher on coding benchmarks: 55.5 versus 51.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.2 does, with 1M tokens against 200K.
How many benchmarks do GLM-5.2 and o3-pro share?
7 benchmarks have published results for both models. GLM-5.2 has 51 scored results on Noometry and o3-pro has 12.