Model comparison
GLM-4.6V vs o1
GLM-4.6V and o1 score almost the same on the Noometry Index (41.3 vs 40.9), so choose on price, context window or the category you care about most.
Last verified . 12 shared benchmarks.
Summary
- They share 12 benchmarks with published results for both. GLM-4.6V scores higher in 3 categories and o1 in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in long context, where o1 leads 50.3 to 41.3.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $15 / $60 for o1.
- o1 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 | o1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 41.3 | 40.9 |
| Released | 2025-12-08 | 2024-09-12 |
| Weights | Open | Proprietary |
| Context window | 128K | 200K |
| Max output | 33K | 100K |
| Input $ / M tokens | $0.30 | $15 |
| Output $ / M tokens | $0.90 | $60 |
| Results tracked | 12 | 52 |
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Category by category
Coding o1 leads
GLM-4.6V: 40.9 (#128), o1: 46.1 (#70)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| LMArena Coding | 1390 | 1367 |
| Aider Polyglot | — | 61.7% |
| WeirdML | — | 47.6% |
| LiveBench Coding | — | 69.7% |
| CadEval | — | 56% |
| HumanEval+ | — | 89% |
| MBPP+ | — | 80.2% |
Agentic & Tool Use Not comparable
GLM-4.6V: —, o1: 24.6 (#117)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| Cybench | — | 10% |
| METR Time Horizons | — | 51.1% |
Reasoning Too close to call
GLM-4.6V: 27.6 (#115), o1: 27.9 (#111)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| LMArena Hard Prompts | 1368 | 1371 |
| SimpleBench | — | 41.7% |
| ARC-AGI-1 | — | 30.7% |
| Chess Puzzles | — | 15% |
| EnigmaEval | — | 5.7% |
| LiveBench Reasoning | — | 91.6% |
| DTBench | — | 74.7% |
| LiveBench Data Analysis | — | 65.5% |
| LMCA | — | 22.3% |
| Epoch Capabilities Index | — | 141.91 |
| LiveBench | — | 75.7% |
Math Not comparable
GLM-4.6V: —, o1: 36.1 (#175)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 14.7% |
| OTIS Mock AIME 2024-2025 | — | 73.3% |
| LiveBench Math | — | 80.3% |
| LMArena Math | — | 1388 |
| MATH Level 5 | — | 94.7% |
| FrontierMath (Feb 2025 set) | — | 9.3% |
Knowledge o1 leads
GLM-4.6V: 38.0 (#149), o1: 41.5 (#110)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| LMArena Expert | 1371 | 1361 |
| GPQA Diamond | — | 76.8% |
| Humanity's Last Exam | — | 8% |
| SimpleQA Verified | — | 41.1% |
| Confabulations | — | 11.7% |
Multimodal Too close to call
GLM-4.6V: 34.8 (#90), o1: 34.2 (#93)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| LMArena Vision | 1164 | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |
Multilingual Too close to call
GLM-4.6V: 48.6 (#141), o1: 48.6 (#142)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| LMArena Non-English | 1359 | 1358 |
| LMArena Chinese | 1425 | 1394 |
| LMArena Russian | 1340 | 1356 |
| LMArena French | — | 1344 |
| LMArena German | — | 1337 |
| LMArena Japanese | — | 1346 |
| LMArena Korean | — | 1396 |
| LMArena Spanish | — | 1345 |
Instruction Following o1 leads
GLM-4.6V: 71.4 (#151), o1: 74.8 (#86)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| LMArena Instruction Following | 1352 | 1367 |
| LiveBench Instruction Following | — | 81.5% |
Long Context o1 leads
GLM-4.6V: 41.3 (#143), o1: 50.3 (#9)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| LMArena Longer Query | 1358 | 1378 |
| Fiction.LiveBench | — | 83.3% |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.6 (#137), o1: 55.6 (#144)
| Benchmark | GLM-4.6V | o1 |
|---|---|---|
| LMArena Text | 1377 | 1366 |
| LMArena Creative Writing | 1347 | 1348 |
| LMArena Multi-Turn | 1360 | 1369 |
| Short-Story Creative Writing | — | 70.2% |
| LiveBench Language | — | 65.4% |
Frequently asked questions
Is GLM-4.6V better than o1?
GLM-4.6V and o1 score almost the same on the Noometry Index (41.3 vs 40.9), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-4.6V or o1?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; o1 lists at $15 and $60.
Is GLM-4.6V or o1 better for coding?
o1 scores higher on coding benchmarks: 46.1 versus 40.9 in the Noometry coding category.
Which has the bigger context window?
o1 does, with 200K tokens against 128K.
How many benchmarks do GLM-4.6V and o1 share?
12 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and o1 has 52.