Model comparison
GLM-5.3-Flash vs o1-mini
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 33.7 on the Noometry Index.
Last verified . 20 shared benchmarks.
Summary
- They share 20 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and o1-mini in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 34.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 46.9% for o1-mini.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | o1-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 33.7 |
| Released | 2026-08-20 | 2024-09-12 |
| Weights | Open | Proprietary |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.15 | — |
| Output $ / M tokens | $0.50 | — |
| Results tracked | 36 | 39 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.7 (#26), o1-mini: 35.1 (#218)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| LMArena Coding | 1506 | 1362 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| Aider Polyglot | — | 32.9% |
| LMArena WebDev | 1614 | — |
| SciCode | 51.6% | — |
| WeirdML | — | 36.3% |
| LiveBench Coding | — | 48% |
| ALE-Bench | 303.55 | — |
| HumanEval+ | — | 89% |
| MBPP+ | — | 78.8% |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.3 (#41), o1-mini: 24.3 (#112)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Cybench | — | 10% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 31.7 (#69), o1-mini: 8.9 (#340)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| LMArena Hard Prompts | 1490 | 1333 |
| Epoch Capabilities Index | 151.9 | 135.82 |
| ARC-AGI-2 | — | 0.8% |
| SimpleBench | — | 18.1% |
| ARC-AGI-1 | — | 14% |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| LiveBench Reasoning | — | 72.3% |
| Mystery Game Puzzles | 8% | — |
| LiveBench Data Analysis | — | 57.9% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| LiveBench | — | 57.8% |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#38), o1-mini: 33.7 (#168)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 46.9% |
| LMArena Math | 1499 | 1358 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| LiveBench Math | — | 62% |
| MATH Level 5 | — | 89.2% |
| FrontierMath (Feb 2025 set) | — | 1.7% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#33), o1-mini: 34.8 (#180)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| GPQA Diamond | 90.2% | 62.4% |
| LMArena Expert | 1512 | 1316 |
| Confabulations | — | 18.6% |
Multimodal Not comparable
GLM-5.3-Flash: 42.6 (#25), o1-mini: —
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| LMArena Vision | 1304 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.2 (#24), o1-mini: 43.8 (#177)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| LMArena Non-English | 1462 | 1289 |
| LMArena Chinese | 1529 | 1314 |
| LMArena French | 1495 | 1292 |
| LMArena German | 1457 | 1278 |
| LMArena Japanese | 1420 | 1245 |
| LMArena Korean | 1443 | 1223 |
| LMArena Russian | 1472 | 1283 |
| LMArena Spanish | 1471 | 1302 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.8 (#17), o1-mini: 67.0 (#200)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| LMArena Instruction Following | 1480 | 1304 |
| LiveBench Instruction Following | — | 65.4% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.3 (#41), o1-mini: 40.1 (#156)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| LMArena Longer Query | 1479 | 1321 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.4 (#49), o1-mini: 48.6 (#196)
| Benchmark | GLM-5.3-Flash | o1-mini |
|---|---|---|
| LMArena Text | 1471 | 1317 |
| LMArena Creative Writing | 1440 | 1244 |
| LMArena Multi-Turn | 1471 | 1314 |
| Short-Story Creative Writing | — | 64.9% |
| LiveBench Language | — | 40.9% |
Frequently asked questions
Is GLM-5.3-Flash better than o1-mini?
GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 33.7 on the Noometry Index.
Is GLM-5.3-Flash or o1-mini better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 35.1 in the Noometry coding category.
How many benchmarks do GLM-5.3-Flash and o1-mini share?
20 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and o1-mini has 39.