Model comparison
GLM-4.6 vs o1-pro
GLM-4.6 is the stronger model overall, scoring 40.9 to 31.2 on the Noometry Index.
Last verified . 0 shared benchmarks.
Summary
- The widest gap is in knowledge, where GLM-4.6 leads 39.6 to 29.3.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $150 / $600 for o1-pro.
- 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 | o1-pro | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 40.9 | 31.2 |
| Released | 2025-09-30 | 2025-03-19 |
| Weights | Open | Proprietary |
| Context window | 205K | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.60 | $150 |
| Output $ / M tokens | $2.20 | $600 |
| Results tracked | 29 | 3 |
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Category by category
Coding Not comparable
GLM-4.6: 39.9 (#137), o1-pro: —
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| SWE-bench Verified (bash only) | 55.4% | — |
| LMArena WebDev | 1339 | — |
| SciCode | 38.4% | — |
| LMArena Coding | 1449 | — |
| ALE-Bench | 340.82 | — |
Agentic & Tool Use Not comparable
GLM-4.6: 32.1 (#60), o1-pro: —
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| Berkeley Function Calling Leaderboard | 72.4% | — |
Reasoning GLM-4.6 leads
GLM-4.6: 23.1 (#168), o1-pro: 20.2 (#223)
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | — |
| ARC-AGI-1 | — | 23.3% |
| CritPt | 1.1% | — |
| EnigmaEval | — | 6.1% |
| LMArena Hard Prompts | 1440 | — |
Math Not comparable
GLM-4.6: 36.4 (#115), o1-pro: —
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Math | 1431 | — |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GLM-4.6 leads
GLM-4.6: 39.6 (#118), o1-pro: 29.3 (#227)
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| Humanity's Last Exam | — | 8.1% |
| Vectara Hallucination Rate | 9.5% | — |
| LMArena Expert | 1432 | — |
Multilingual Not comparable
GLM-4.6: 53.6 (#62), o1-pro: —
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Non-English | 1426 | — |
| LMArena Chinese | 1500 | — |
| LMArena French | 1459 | — |
| LMArena German | 1447 | — |
| LMArena Japanese | 1394 | — |
| LMArena Korean | 1401 | — |
| LMArena Russian | 1418 | — |
| LMArena Spanish | 1436 | — |
Instruction Following Not comparable
GLM-4.6: 74.5 (#93), o1-pro: —
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Instruction Following | 1410 | — |
Long Context Not comparable
GLM-4.6: 43.4 (#91), o1-pro: —
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Longer Query | 1423 | — |
Writing & Preference Not comparable
GLM-4.6: 61.3 (#86), o1-pro: —
| Benchmark | GLM-4.6 | o1-pro |
|---|---|---|
| LMArena Text | 1440 | — |
| LMArena Creative Writing | 1411 | — |
| EQ-Bench Creative Writing | 1411 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is GLM-4.6 better than o1-pro?
GLM-4.6 is the stronger model overall, scoring 40.9 to 31.2 on the Noometry Index.
Which is cheaper, GLM-4.6 or o1-pro?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; o1-pro lists at $150 and $600.
Which has the bigger context window?
GLM-4.6 does, with 205K tokens against 200K.
How many benchmarks do GLM-4.6 and o1-pro share?
0 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and o1-pro has 3.