Model comparison
GLM-4.6 vs o3-mini
GLM-4.6 is the stronger model overall, scoring 41.4 to 36.7 on the Noometry Index.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-4.6 scores higher in 7 categories and o3-mini in 2 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.6 leads 39.1 to 28.1.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- GLM-4.6 accepts more context: 205K tokens versus 200K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | o3-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.4 | 36.7 |
| Released | 2025-09-30 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 205K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.60 | $1.10 |
| Output $ / M tokens | $2.20 | $4.40 |
| Results tracked | 29 | 51 |
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Category by category
Coding Too close to call
GLM-4.6: 40.1 (#148), o3-mini: 40.8 (#132)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| SciCode | 38.4% | 39.8% |
| LMArena Coding | 1449 | 1378 |
| SWE-bench Verified (bash only) | 55.4% | — |
| Aider Polyglot | — | 60.4% |
| LMArena WebDev | 1340 | — |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use GLM-4.6 leads
GLM-4.6: 32.3 (#66), o3-mini: 29.6 (#84)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| Cybench | — | 22.5% |
Reasoning GLM-4.6 leads
GLM-4.6: 23.7 (#172), o3-mini: 16.3 (#305)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| CritPt | 1.1% | 0.3% |
| LMArena Hard Prompts | 1440 | 1366 |
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| Kagi LLM Benchmark | 47.4% | — |
| ARC-AGI-1 | — | 34.5% |
| Chess Puzzles | — | 17% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| Epoch Capabilities Index | — | 140.34 |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math GLM-4.6 leads
GLM-4.6: 39.1 (#111), o3-mini: 28.1 (#244)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| LMArena Math | 1432 | 1396 |
| FrontierMath (Feb 2025 set) | 3.8% | 12.4% |
| FrontierMath Tier 4 (v1) | 2.1% | 4.2% |
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| OTIS Mock AIME 2024-2025 | — | 76.9% |
| LiveBench Math | — | 77.3% |
| MATH Level 5 | — | 96.5% |
Knowledge GLM-4.6 leads
GLM-4.6: 40.2 (#124), o3-mini: 38.3 (#146)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| LMArena Expert | 1431 | 1364 |
| GPQA Diamond | — | 77% |
| SimpleQA Verified | — | 15.3% |
| Confabulations | — | 17.9% |
| Vectara Hallucination Rate | 9.5% | — |
Multilingual GLM-4.6 leads
GLM-4.6: 53.5 (#66), o3-mini: 45.7 (#164)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| LMArena Non-English | 1426 | 1319 |
| LMArena Chinese | 1499 | 1379 |
| LMArena French | 1459 | 1334 |
| LMArena German | 1447 | 1303 |
| LMArena Japanese | 1393 | 1286 |
| LMArena Korean | 1400 | 1314 |
| LMArena Russian | 1419 | 1304 |
| LMArena Spanish | 1436 | 1321 |
Instruction Following Too close to call
GLM-4.6: 74.3 (#98), o3-mini: 75.1 (#72)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1410 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
Long Context GLM-4.6 leads
GLM-4.6: 43.4 (#94), o3-mini: 33.8 (#256)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| LMArena Longer Query | 1422 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference GLM-4.6 leads
GLM-4.6: 61.1 (#90), o3-mini: 50.3 (#182)
| Benchmark | GLM-4.6 | o3-mini |
|---|---|---|
| LMArena Text | 1440 | 1337 |
| LMArena Creative Writing | 1411 | 1286 |
| LMArena Multi-Turn | 1427 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| EQ-Bench Creative Writing | 1411 | — |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GLM-4.6 better than o3-mini?
GLM-4.6 is the stronger model overall, scoring 41.4 to 36.7 on the Noometry Index.
Which is cheaper, GLM-4.6 or o3-mini?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is GLM-4.6 or o3-mini better for coding?
They score almost the same on coding (40.1 vs 40.8); test both on your own repository before choosing.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 200K.
How many benchmarks do GLM-4.6 and o3-mini share?
21 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and o3-mini has 51.