Model comparison
GLM-4.6V vs o3-mini
GLM-4.6V is the stronger model overall, scoring 40.9 to 36.7 on the Noometry Index.
Last verified . 11 shared benchmarks.
Summary
- They share 11 benchmarks with published results for both. GLM-4.6V scores higher in 4 categories and o3-mini in 3 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.6V leads 26.5 to 16.3.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- o3-mini accepts more context: 200K tokens versus 128K.
- GLM-4.6V has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6V | o3-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 40.9 | 36.7 |
| Released | 2025-12-08 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 33K | 100K |
| Input $ / M tokens | $0.30 | $1.10 |
| Output $ / M tokens | $0.90 | $4.40 |
| Results tracked | 12 | 51 |
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Category by category
Coding o3-mini leads
GLM-4.6V: 40.2 (#128), o3-mini: 41.4 (#116)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| LMArena Coding | 1390 | 1378 |
| Aider Polyglot | — | 60.4% |
| SciCode | — | 39.8% |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
Agentic & Tool Use Not comparable
GLM-4.6V: —, o3-mini: 29.3 (#79)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| Cybench | — | 22.5% |
Reasoning GLM-4.6V leads
GLM-4.6V: 26.5 (#117), o3-mini: 16.3 (#295)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1366 |
| ARC-AGI-2 | — | 3% |
| SimpleBench | — | 22.8% |
| ARC-AGI-1 | — | 34.5% |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 17% |
| LiveBench Reasoning | — | 89.6% |
| Mystery Game Puzzles | — | 7% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| Epoch Capabilities Index | — | 140.35 |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math Not comparable
GLM-4.6V: —, o3-mini: 27.6 (#236)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 18.6% |
| FrontierMath Tier 4 | — | 0% |
| OTIS Mock AIME 2024-2025 | — | 76.9% |
| LiveBench Math | — | 77.3% |
| LMArena Math | — | 1396 |
| MATH Level 5 | — | 96.5% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge Too close to call
GLM-4.6V: 37.4 (#148), o3-mini: 38.3 (#135)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| LMArena Expert | 1372 | 1364 |
| GPQA Diamond | — | 77% |
| SimpleQA Verified | — | 15.3% |
| Confabulations | — | 17.9% |
Multimodal Not comparable
GLM-4.6V: 34.2 (#83), o3-mini: —
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual GLM-4.6V leads
GLM-4.6V: 48.8 (#135), o3-mini: 45.9 (#159)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| LMArena Non-English | 1359 | 1319 |
| LMArena Chinese | 1426 | 1379 |
| LMArena Russian | 1341 | 1304 |
| LMArena French | — | 1333 |
| LMArena German | — | 1303 |
| LMArena Japanese | — | 1286 |
| LMArena Korean | — | 1314 |
| LMArena Spanish | — | 1321 |
Instruction Following o3-mini leads
GLM-4.6V: 71.6 (#145), o3-mini: 75.3 (#67)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1351 | 1338 |
| LiveBench Instruction Following | — | 84.4% |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#138), o3-mini: 33.8 (#251)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| LMArena Longer Query | 1358 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.8 (#132), o3-mini: 50.4 (#177)
| Benchmark | GLM-4.6V | o3-mini |
|---|---|---|
| LMArena Text | 1377 | 1337 |
| LMArena Creative Writing | 1347 | 1286 |
| LMArena Multi-Turn | 1359 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GLM-4.6V better than o3-mini?
GLM-4.6V is the stronger model overall, scoring 40.9 to 36.7 on the Noometry Index.
Which is cheaper, GLM-4.6V or o3-mini?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is GLM-4.6V or o3-mini better for coding?
o3-mini scores higher on coding benchmarks: 41.4 versus 40.2 in the Noometry coding category.
Which has the bigger context window?
o3-mini does, with 200K tokens against 128K.
How many benchmarks do GLM-4.6V and o3-mini share?
11 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and o3-mini has 51.