Model comparison
GLM-5.3-Flash vs o1
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 40.9 on the Noometry Index.
Last verified . 24 shared benchmarks.
Summary
- They share 24 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and o1 in 1 category; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 27.9.
- The biggest single-benchmark swing is ARC-AGI-1: 91% for GLM-5.3-Flash and 30.7% for o1.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $15 / $60 for o1.
- GLM-5.3-Flash accepts more context: 1M tokens versus 200K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | o1 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 40.9 |
| Released | 2026-08-20 | 2024-09-12 |
| Weights | Open | Proprietary |
| Context window | 1M | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.15 | $15 |
| Output $ / M tokens | $0.50 | $60 |
| Results tracked | 40 | 52 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), o1: 46.1 (#70)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| LMArena Coding | 1508 | 1367 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| Aider Polyglot | — | 61.7% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| WeirdML | — | 47.6% |
| LiveBench Coding | — | 69.7% |
| CadEval | — | 56% |
| ALE-Bench | 303.55 | — |
| HumanEval+ | — | 89% |
| MBPP+ | — | 80.2% |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), o1: 24.6 (#117)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Cybench | — | 10% |
| GDP.pdf | 14% | — |
| METR Time Horizons | — | 51.1% |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), o1: 27.9 (#111)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| ARC-AGI-1 | 91% | 30.7% |
| Chess Puzzles | 14% | 15% |
| LMArena Hard Prompts | 1491 | 1371 |
| Epoch Capabilities Index | 151.88 | 141.91 |
| ARC-AGI-2 | 65.8% | — |
| SimpleBench | — | 41.7% |
| CritPt | 15.4% | — |
| EnigmaEval | — | 5.7% |
| LiveBench Reasoning | — | 91.6% |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 74.7% |
| LiveBench Data Analysis | — | 65.5% |
| LMCA | — | 22.3% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| LiveBench | — | 75.7% |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), o1: 36.1 (#175)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 14.7% |
| OTIS Mock AIME 2024-2025 | 93.9% | 73.3% |
| LMArena Math | 1500 | 1388 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| LiveBench Math | — | 80.3% |
| MATH Level 5 | — | 94.7% |
| FrontierMath (Feb 2025 set) | — | 9.3% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), o1: 41.5 (#110)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| GPQA Diamond | 90.2% | 76.8% |
| LMArena Expert | 1513 | 1361 |
| Humanity's Last Exam | — | 8% |
| SimpleQA Verified | — | 41.1% |
| Confabulations | — | 11.7% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), o1: 34.2 (#93)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| LMArena Vision | 1296 | 1168 |
| GeoBench | — | 80% |
| VPCT | — | 37% |
| SpatialViz-Bench | — | 41.4% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), o1: 48.6 (#142)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| LMArena Non-English | 1462 | 1358 |
| LMArena Chinese | 1527 | 1394 |
| LMArena French | 1496 | 1344 |
| LMArena German | 1470 | 1337 |
| LMArena Japanese | 1429 | 1346 |
| LMArena Korean | 1446 | 1396 |
| LMArena Russian | 1469 | 1356 |
| LMArena Spanish | 1471 | 1345 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), o1: 74.8 (#86)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1367 |
| LiveBench Instruction Following | — | 81.5% |
Long Context o1 leads
GLM-5.3-Flash: 45.4 (#39), o1: 50.3 (#9)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| LMArena Longer Query | 1482 | 1378 |
| Fiction.LiveBench | — | 83.3% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), o1: 55.6 (#144)
| Benchmark | GLM-5.3-Flash | o1 |
|---|---|---|
| LMArena Text | 1471 | 1366 |
| LMArena Creative Writing | 1442 | 1348 |
| LMArena Multi-Turn | 1467 | 1369 |
| Short-Story Creative Writing | — | 70.2% |
| LiveBench Language | — | 65.4% |
Frequently asked questions
Is GLM-5.3-Flash better than o1?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 40.9 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or o1?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; o1 lists at $15 and $60.
Is GLM-5.3-Flash or o1 better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 46.1 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 200K.
How many benchmarks do GLM-5.3-Flash and o1 share?
24 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and o1 has 52.