Model comparison
GLM-5.3-Flash vs GPT-4.5
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 37.2 on the Noometry Index.
Last verified . 22 shared benchmarks.
Summary
- They share 22 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and GPT-4.5 in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 13.9.
- The biggest single-benchmark swing is ARC-AGI-1: 91% for GLM-5.3-Flash and 10.3% for GPT-4.5.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | GPT-4.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 37.2 |
| Released | 2026-08-20 | 2025-02-27 |
| Weights | Open | Proprietary |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.15 | — |
| Output $ / M tokens | $0.50 | — |
| Results tracked | 40 | 42 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), GPT-4.5: 42.2 (#109)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Coding | 1508 | 1396 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| Aider Polyglot | — | 44.9% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| WeirdML | — | 39.4% |
| LiveBench Coding | — | 75.2% |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), GPT-4.5: 27.9 (#97)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Cybench | — | 17.5% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), GPT-4.5: 13.9 (#330)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| ARC-AGI-2 | 65.8% | 0.8% |
| ARC-AGI-1 | 91% | 10.3% |
| LMArena Hard Prompts | 1491 | 1403 |
| Epoch Capabilities Index | 151.88 | 136.74 |
| SimpleBench | — | 34.5% |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| EnigmaEval | — | 3.2% |
| LiveBench Reasoning | — | 71.1% |
| Mystery Game Puzzles | 8% | — |
| LiveBench Data Analysis | — | 64.3% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 61.7 |
| LiveBench | — | 69% |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), GPT-4.5: 32.6 (#211)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 37.8% |
| LMArena Math | 1500 | 1412 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| LiveBench Math | — | 69.3% |
| MATH Level 5 | — | 78.6% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), GPT-4.5: 32.5 (#211)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| GPQA Diamond | 90.2% | 68.7% |
| LMArena Expert | 1513 | 1394 |
| Humanity's Last Exam | — | 5.4% |
| Confabulations | — | 13.6% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), GPT-4.5: 37.6 (#71)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Vision | 1296 | 1195 |
| VPCT | — | 45% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), GPT-4.5: 52.5 (#83)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Non-English | 1462 | 1413 |
| LMArena Chinese | 1527 | 1421 |
| LMArena French | 1496 | 1418 |
| LMArena German | 1470 | 1457 |
| LMArena Japanese | 1429 | 1416 |
| LMArena Korean | 1446 | 1392 |
| LMArena Russian | 1469 | 1419 |
| LMArena Spanish | 1471 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), GPT-4.5: 72.6 (#134)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Instruction Following | 1478 | 1404 |
| LiveBench Instruction Following | — | 72.3% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), GPT-4.5: 40.4 (#155)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Longer Query | 1482 | 1406 |
| Fiction.LiveBench | — | 63.9% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), GPT-4.5: 56.9 (#134)
| Benchmark | GLM-5.3-Flash | GPT-4.5 |
|---|---|---|
| LMArena Text | 1471 | 1417 |
| LMArena Creative Writing | 1442 | 1394 |
| LMArena Multi-Turn | 1467 | 1444 |
| Short-Story Creative Writing | — | 75.6% |
| EQ-Bench Creative Writing | — | 1258 |
| LiveBench Language | — | 61.5% |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-4.5?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 37.2 on the Noometry Index.
Is GLM-5.3-Flash or GPT-4.5 better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 42.2 in the Noometry coding category.
How many benchmarks do GLM-5.3-Flash and GPT-4.5 share?
22 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-4.5 has 42.