Model comparison
GLM-5.3 vs GPT-4o
GLM-5.3 is the stronger model overall, scoring 54.8 to 28.6 on the Noometry Index.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. GLM-5.3 scores higher in 9 categories and GPT-4o in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 10.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 6.4% for GPT-4o.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $2.50 / $10 for GPT-4o.
- GLM-5.3 accepts more context: 1M tokens versus 128K.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | GPT-4o | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 28.6 |
| Released | 2026-08-14 | 2024-05-13 |
| Weights | Open | Proprietary |
| Context window | 1M | 128K |
| Max output | 131K | 16K |
| Input $ / M tokens | $1.40 | $2.50 |
| Output $ / M tokens | $4.40 | $10 |
| Results tracked | 42 | 72 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), GPT-4o: 24.8 (#328)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| WeirdML | 75.4% | 25.1% |
| LMArena Coding | 1496 | 1297 |
| SWE-bench Verified | — | 31% |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| SWE-bench Verified (bash only) | — | 21.6% |
| Aider Polyglot | — | 45.3% |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| GSO | — | 0% |
| BigCodeBench Instruct | — | 51.1% |
| LiveBench Coding | — | 51.4% |
| BigCodeBench Complete | — | 61.1% |
| CadEval | — | 26% |
| ALE-Bench | 1,317 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 72.2% |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.4 (#38), GPT-4o: 21.0 (#141)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| APEX-Agents | 56.6% | — |
| GDPval | — | 9.9% |
| TheAgentCompany | — | 8.6% |
| Cybench | — | 12.5% |
| BALROG | — | 32.3% |
| LMArena Search | — | 1006 |
| METR Time Horizons | — | 40.8% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), GPT-4o: 9.4 (#343)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| CritPt | 19.1% | 0% |
| Chess Puzzles | 21% | 13% |
| LMArena Hard Prompts | 1489 | 1281 |
| DTBench | 87.7% | 64.5% |
| LMCA | 55.5% | 16.6% |
| Epoch Capabilities Index | 155.61 | 128.97 |
| ARC-AGI-2 | — | 0% |
| SimpleBench | — | 17.8% |
| NYT Connections (extended) | 74.2% | — |
| ARC-AGI-1 | — | 4.5% |
| EnigmaEval | — | 0.8% |
| LiveBench Reasoning | — | 55.8% |
| Mystery Game Puzzles | 33% | — |
| LiveBench Data Analysis | — | 60.9% |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 57.7 |
| LiveBench | — | 55.3% |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), GPT-4o: 10.6 (#312)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 0.4% |
| OTIS Mock AIME 2024-2025 | 91.1% | 6.4% |
| LMArena Math | 1489 | 1285 |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| Omni-MATH | — | 29.3% |
| LiveBench Math | — | 49.5% |
| MATH Level 5 | — | 53.3% |
| FrontierMath (Feb 2025 set) | — | 0.3% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), GPT-4o: 28.8 (#242)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| GPQA Diamond | 90.9% | 49.2% |
| SimpleQA Verified | 41% | 26% |
| LMArena Expert | 1516 | 1250 |
| Humanity's Last Exam | — | 2.7% |
| MMLU-Pro | — | 71.3% |
| Confabulations | — | 15.3% |
| Vectara Hallucination Rate | — | 9.6% |
| GPQA (HELM) | — | 52% |
| MMLU | — | 88.1% |
Multimodal Not comparable
GLM-5.3: —, GPT-4o: 34.5 (#91)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| LMArena Vision | — | 1137 |
| Video-MME | — | 71.9% |
| GeoBench | — | 71% |
| VPCT | — | 40% |
| ScienceQA | — | 88.5% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), GPT-4o: 43.2 (#186)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| LMArena Non-English | 1457 | 1283 |
| LMArena Chinese | 1528 | 1277 |
| LMArena French | 1499 | 1304 |
| LMArena German | 1499 | 1282 |
| LMArena Japanese | 1453 | 1257 |
| LMArena Korean | 1472 | 1234 |
| LMArena Russian | 1463 | 1286 |
| LMArena Spanish | 1460 | 1292 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), GPT-4o: 66.6 (#207)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| LMArena Instruction Following | 1477 | 1278 |
| LiveBench Instruction Following | — | 68.6% |
| IFEval | — | 81.7% |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), GPT-4o: 39.4 (#179)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| LMArena Longer Query | 1482 | 1289 |
| Fiction.LiveBench | — | 66.7% |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), GPT-4o: 52.6 (#166)
| Benchmark | GLM-5.3 | GPT-4o |
|---|---|---|
| LMArena Text | 1471 | 1300 |
| LMArena Creative Writing | 1457 | 1292 |
| LMArena Multi-Turn | 1472 | 1302 |
| Short-Story Creative Writing | — | 81.8% |
| EQ-Bench Creative Writing | 2075 | — |
| WildBench | — | 82.8% |
| LiveBench Language | — | 47.6% |
Frequently asked questions
Is GLM-5.3 better than GPT-4o?
GLM-5.3 is the stronger model overall, scoring 54.8 to 28.6 on the Noometry Index.
Which is cheaper, GLM-5.3 or GPT-4o?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-4o lists at $2.50 and $10.
Is GLM-5.3 or GPT-4o better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 24.8 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 128K.
How many benchmarks do GLM-5.3 and GPT-4o share?
27 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-4o has 72.