Model comparison
GLM-5.3-Flash vs o3-mini
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 36.7 on the Noometry Index.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and o3-mini in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 16.3.
- The biggest single-benchmark swing is ARC-AGI-2: 65.8% for GLM-5.3-Flash and 3% for o3-mini.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.10 / $4.40 for o3-mini.
- 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 | o3-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 36.7 |
| Released | 2026-08-20 | 2024-12-20 |
| Weights | Open | Proprietary |
| Context window | 1M | 200K |
| Max output | 131K | 100K |
| Input $ / M tokens | $0.15 | $1.10 |
| Output $ / M tokens | $0.50 | $4.40 |
| Results tracked | 40 | 51 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), o3-mini: 40.8 (#132)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| SciCode | 51.6% | 39.8% |
| LMArena Coding | 1508 | 1378 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| Aider Polyglot | — | 60.4% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| GSO | — | 1.3% |
| WeirdML | — | 43.7% |
| LiveBench Coding | — | 82.7% |
| CadEval | — | 54% |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), o3-mini: 29.6 (#84)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Cybench | — | 22.5% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), o3-mini: 16.3 (#305)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| ARC-AGI-2 | 65.8% | 3% |
| ARC-AGI-1 | 91% | 34.5% |
| CritPt | 15.4% | 0.3% |
| Chess Puzzles | 14% | 17% |
| LMArena Hard Prompts | 1491 | 1366 |
| Mystery Game Puzzles | 8% | 7% |
| Epoch Capabilities Index | 151.88 | 140.34 |
| SimpleBench | — | 22.8% |
| LiveBench Reasoning | — | 89.6% |
| DTBench | — | 68.8% |
| LiveBench Data Analysis | — | 70.6% |
| LMCA | — | 19% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 59.6 |
| LiveBench | — | 75.9% |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), o3-mini: 28.1 (#244)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 18.6% |
| FrontierMath Tier 4 | 17.1% | 0% |
| OTIS Mock AIME 2024-2025 | 93.9% | 76.9% |
| LMArena Math | 1500 | 1396 |
| ProofBench | 21% | — |
| LiveBench Math | — | 77.3% |
| MATH Level 5 | — | 96.5% |
| FrontierMath (Feb 2025 set) | — | 12.4% |
| FrontierMath Tier 4 (v1) | — | 4.2% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), o3-mini: 38.3 (#146)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| GPQA Diamond | 90.2% | 77% |
| LMArena Expert | 1513 | 1364 |
| SimpleQA Verified | — | 15.3% |
| Confabulations | — | 17.9% |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), o3-mini: —
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), o3-mini: 45.7 (#164)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| LMArena Non-English | 1462 | 1319 |
| LMArena Chinese | 1527 | 1379 |
| LMArena French | 1496 | 1334 |
| LMArena German | 1470 | 1303 |
| LMArena Japanese | 1429 | 1286 |
| LMArena Korean | 1446 | 1314 |
| LMArena Russian | 1469 | 1304 |
| LMArena Spanish | 1471 | 1321 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), o3-mini: 75.1 (#72)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| LMArena Instruction Following | 1478 | 1337 |
| LiveBench Instruction Following | — | 84.4% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), o3-mini: 33.8 (#256)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| LMArena Longer Query | 1482 | 1343 |
| Fiction.LiveBench | — | 50% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), o3-mini: 50.3 (#182)
| Benchmark | GLM-5.3-Flash | o3-mini |
|---|---|---|
| LMArena Text | 1471 | 1337 |
| LMArena Creative Writing | 1442 | 1286 |
| LMArena Multi-Turn | 1467 | 1320 |
| Short-Story Creative Writing | — | 61.7% |
| LiveBench Language | — | 50.7% |
Frequently asked questions
Is GLM-5.3-Flash better than o3-mini?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 36.7 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or o3-mini?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; o3-mini lists at $1.10 and $4.40.
Is GLM-5.3-Flash or o3-mini better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 40.8 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 o3-mini share?
28 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and o3-mini has 51.