Model comparison
GLM-5.3-Flash vs GPT-4o mini
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 25.5 on the Noometry Index.
Last verified . 25 shared benchmarks.
Summary
- They share 25 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and GPT-4o mini in 0 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.3 to 10.4.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 6.9% for GPT-4o mini.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.15 / $0.60 for GPT-4o mini.
- GLM-5.3-Flash accepts more context: 1M tokens versus 128K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | GPT-4o mini | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 25.5 |
| Released | 2026-08-20 | 2024-07-18 |
| Weights | Open | Proprietary |
| Context window | 1M | 128K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.15 | $0.15 |
| Output $ / M tokens | $0.50 | $0.60 |
| Results tracked | 40 | 60 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), GPT-4o mini: 22.0 (#335)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Coding | 1508 | 1290 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| Aider Polyglot | — | 3.6% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| WeirdML | — | 11.8% |
| BigCodeBench Instruct | — | 46.1% |
| LiveBench Coding | — | 43.1% |
| BigCodeBench Complete | — | 57.4% |
| ALE-Bench | 303.55 | — |
| HumanEval+ | — | 83.5% |
| MBPP+ | — | 72.2% |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), GPT-4o mini: 27.5 (#101)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| APEX-Agents | 52.8% | — |
| BALROG | — | 17.4% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), GPT-4o mini: 8.7 (#347)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| ARC-AGI-2 | 65.8% | 0% |
| Chess Puzzles | 14% | 0% |
| LMArena Hard Prompts | 1491 | 1267 |
| Mystery Game Puzzles | 8% | 12% |
| Epoch Capabilities Index | 151.88 | 126.56 |
| SimpleBench | — | 10.7% |
| Kagi LLM Benchmark | — | 28.8% |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| LiveBench Reasoning | — | 32.8% |
| DTBench | — | 54.4% |
| LiveBench Data Analysis | — | 50% |
| LMCA | — | 10.4% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| LiveBench | — | 41.3% |
| PIQA | — | 88.7% |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), GPT-4o mini: 10.4 (#314)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 0.7% |
| OTIS Mock AIME 2024-2025 | 93.9% | 6.9% |
| LMArena Math | 1500 | 1267 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 28% |
| LiveBench Math | — | 36.3% |
| MATH Level 5 | — | 52.6% |
| GSM8K | — | 91.3% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), GPT-4o mini: 17.7 (#284)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| GPQA Diamond | 90.2% | 37.7% |
| LMArena Expert | 1513 | 1235 |
| SimpleQA Verified | — | 8.3% |
| MMLU-Pro | — | 60.3% |
| Confabulations | — | 37.2% |
| GPQA (HELM) | — | 36.8% |
| BoolQ | — | 88.7% |
| MMLU | — | 81.8% |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), GPT-4o mini: 25.9 (#122)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Vision | 1296 | 1066 |
| Video-MME | — | 64.8% |
| GeoBench | — | 64% |
| VPCT | — | 34% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), GPT-4o mini: 42.0 (#199)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Non-English | 1462 | 1266 |
| LMArena Chinese | 1527 | 1265 |
| LMArena French | 1496 | 1297 |
| LMArena German | 1470 | 1272 |
| LMArena Japanese | 1429 | 1216 |
| LMArena Korean | 1446 | 1195 |
| LMArena Russian | 1469 | 1275 |
| LMArena Spanish | 1471 | 1276 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), GPT-4o mini: 61.9 (#239)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Instruction Following | 1478 | 1258 |
| LiveBench Instruction Following | — | 56.8% |
| IFEval | — | 78.2% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), GPT-4o mini: 39.1 (#186)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Longer Query | 1482 | 1289 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), GPT-4o mini: 39.5 (#248)
| Benchmark | GLM-5.3-Flash | GPT-4o mini |
|---|---|---|
| LMArena Text | 1471 | 1286 |
| LMArena Creative Writing | 1442 | 1268 |
| LMArena Multi-Turn | 1467 | 1285 |
| Short-Story Creative Writing | — | 67.2% |
| EQ-Bench Creative Writing | — | 873 |
| WildBench | — | 79.1% |
| LiveBench Language | — | 28.6% |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-4o mini?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 25.5 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or GPT-4o mini?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-4o mini lists at $0.15 and $0.60.
Is GLM-5.3-Flash or GPT-4o mini better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 22.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 128K.
How many benchmarks do GLM-5.3-Flash and GPT-4o mini share?
25 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-4o mini has 60.