Model comparison
GLM-5.3-Flash vs o4-mini
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 41.6 on the Noometry Index.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and o4-mini in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 24.6.
- The biggest single-benchmark swing is ARC-AGI-2: 65.8% for GLM-5.3-Flash and 6.1% for o4-mini.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.10 / $4.40 for o4-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 | o4-mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 41.6 |
| Released | 2026-08-20 | 2025-04-16 |
| 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 | 60 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), o4-mini: 40.9 (#127)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| LMArena Coding | 1508 | 1368 |
| ALE-Bench | 303.55 | 826.17 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| SWE-bench Verified (bash only) | — | 45% |
| Aider Polyglot | — | 72% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| GSO | — | 3.6% |
| WeirdML | — | 52.6% |
| CadEval | — | 62% |
| AlgoTune | — | 1.72 |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), o4-mini: 32.6 (#61)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 53.2% |
| GDPval | — | 25.3% |
| GDP.pdf | 14% | — |
| METR Time Horizons | — | 63.9% |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), o4-mini: 24.6 (#162)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| ARC-AGI-2 | 65.8% | 6.1% |
| ARC-AGI-1 | 91% | 58.7% |
| CritPt | 15.4% | 0.6% |
| Chess Puzzles | 14% | 26% |
| LMArena Hard Prompts | 1491 | 1351 |
| Mystery Game Puzzles | 8% | 5% |
| Epoch Capabilities Index | 151.88 | 145.64 |
| SimpleBench | — | 38.7% |
| Kagi LLM Benchmark | — | 67.6% |
| EnigmaEval | — | 9.2% |
| DTBench | — | 77.6% |
| LMCA | — | 26.5% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 61.8 |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), o4-mini: 40.8 (#89)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 36.1% |
| FrontierMath Tier 4 | 17.1% | 4.9% |
| OTIS Mock AIME 2024-2025 | 93.9% | 81.7% |
| LMArena Math | 1500 | 1389 |
| ProofBench | 21% | — |
| Omni-MATH | — | 72% |
| MATH Level 5 | — | 97.8% |
| FrontierMath (Feb 2025 set) | — | 24.8% |
| FrontierMath Tier 4 (v1) | — | 6.3% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), o4-mini: 43.6 (#91)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| GPQA Diamond | 90.2% | 79.6% |
| LMArena Expert | 1513 | 1343 |
| Humanity's Last Exam | — | 18.1% |
| SimpleQA Verified | — | 19.6% |
| MMLU-Pro | — | 82% |
| Confabulations | — | 15.8% |
| Vectara Hallucination Rate | — | 18.6% |
| GPQA (HELM) | — | 73.5% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), o4-mini: 40.2 (#49)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| LMArena Vision | 1296 | 1194 |
| GeoBench | — | 64% |
| VPCT | — | 57.5% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), o4-mini: 47.0 (#154)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| LMArena Non-English | 1462 | 1337 |
| LMArena Chinese | 1527 | 1354 |
| LMArena French | 1496 | 1364 |
| LMArena German | 1470 | 1336 |
| LMArena Japanese | 1429 | 1308 |
| LMArena Korean | 1446 | 1312 |
| LMArena Russian | 1469 | 1334 |
| LMArena Spanish | 1471 | 1347 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), o4-mini: 75.2 (#68)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| LMArena Instruction Following | 1478 | 1321 |
| IFEval | — | 92.8% |
Long Context Too close to call
GLM-5.3-Flash: 45.4 (#39), o4-mini: 45.5 (#33)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| LMArena Longer Query | 1482 | 1315 |
| Fiction.LiveBench | — | 77.8% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), o4-mini: 54.0 (#152)
| Benchmark | GLM-5.3-Flash | o4-mini |
|---|---|---|
| LMArena Text | 1471 | 1353 |
| LMArena Creative Writing | 1442 | 1294 |
| LMArena Multi-Turn | 1467 | 1350 |
| Short-Story Creative Writing | — | 75% |
| WildBench | — | 85.4% |
Frequently asked questions
Is GLM-5.3-Flash better than o4-mini?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 41.6 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or o4-mini?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; o4-mini lists at $1.10 and $4.40.
Is GLM-5.3-Flash or o4-mini better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 40.9 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 o4-mini share?
29 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and o4-mini has 60.