Model comparison
GLM-5.3 vs GPT-5
GLM-5.3 is the stronger model overall, scoring 54.8 to 50.9 on the Noometry Index.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and GPT-5 in 1 category; 9 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 45.4.
- The biggest single-benchmark swing is ProofBench: 49% for GLM-5.3 and 18% for GPT-5.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GLM-5.3 accepts more context: 1M tokens versus 400K.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | GPT-5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 50.9 |
| Released | 2026-08-14 | 2025-08-07 |
| Weights | Open | Proprietary |
| Context window | 1M | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $1.40 | $1.25 |
| Output $ / M tokens | $4.40 | $10 |
| Results tracked | 42 | 69 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), GPT-5: 50.3 (#47)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| LMArena WebDev | 1622 | 1418 |
| SciCode | 59% | 42.9% |
| WeirdML | 75.4% | 60.7% |
| LMArena Coding | 1496 | 1436 |
| ALE-Bench | 1,317 | 1,162 |
| SWE-bench Verified | — | 73.6% |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| SWE-bench Verified (bash only) | — | 65% |
| Aider Polyglot | — | 88% |
| CursorBench | 42.6% | — |
| FrontierSWE | 30.2% | — |
| GSO | — | 6.9% |
| AlgoTune | — | 1.67 |
Agentic & Tool Use GLM-5.3 leads
GLM-5.3: 36.4 (#38), GPT-5: 33.1 (#56)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| Terminal-Bench | — | 49.6% |
| APEX-Agents | 56.6% | — |
| GDPval | — | 34.8% |
| Remote Labor Index | — | 1.7% |
| DeepResearch Bench | — | 49.6% |
| BALROG | — | 32.8% |
| LMArena Search | — | 1133 |
| METR Time Horizons | — | 69.6% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), GPT-5: 38.3 (#64)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| CritPt | 19.1% | 12.6% |
| Chess Puzzles | 21% | 37% |
| LMArena Hard Prompts | 1489 | 1416 |
| Mystery Game Puzzles | 33% | 23% |
| DTBench | 87.7% | 90.7% |
| LMCA | 55.5% | 40% |
| Epoch Capabilities Index | 155.61 | 150 |
| ARC-AGI-2 | — | 9.9% |
| SimpleBench | — | 56.7% |
| Kagi LLM Benchmark | — | 72.7% |
| NYT Connections (extended) | 74.2% | — |
| ARC-AGI-1 | — | 65.7% |
| EnigmaEval | — | 10.5% |
| EBR-Bench | — | 12.7% |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 61.4 |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), GPT-5: 55.0 (#44)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 55.4% |
| FrontierMath Tier 4 | 29.3% | 22% |
| OTIS Mock AIME 2024-2025 | 91.1% | 91.4% |
| ProofBench | 49% | 18% |
| LMArena Math | 1489 | 1407 |
| Omni-MATH | — | 64.7% |
| MATH Level 5 | — | 98.1% |
| FrontierMath (Feb 2025 set) | — | 32.4% |
| FrontierMath Tier 4 (v1) | — | 12.5% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), GPT-5: 56.6 (#43)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| GPQA Diamond | 90.9% | 86.2% |
| SimpleQA Verified | 41% | 50.1% |
| LMArena Expert | 1516 | 1419 |
| Humanity's Last Exam | — | 25.3% |
| MMLU-Pro | — | 86.3% |
| Confabulations | — | 10.3% |
| Vectara Hallucination Rate | — | 14.7% |
| GPQA (HELM) | — | 79.2% |
Multimodal Not comparable
GLM-5.3: —, GPT-5: 46.8 (#13)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| LMArena Vision | — | 1232 |
| GeoBench | — | 81% |
| VPCT | — | 66% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), GPT-5: 51.4 (#110)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| LMArena Non-English | 1457 | 1397 |
| LMArena Chinese | 1528 | 1422 |
| LMArena French | 1499 | 1410 |
| LMArena German | 1499 | 1416 |
| LMArena Japanese | 1453 | 1409 |
| LMArena Korean | 1472 | 1360 |
| LMArena Russian | 1463 | 1406 |
| LMArena Spanish | 1460 | 1399 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), GPT-5: 73.8 (#113)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| LMArena Instruction Following | 1477 | 1388 |
| IFEval | — | 87.5% |
Long Context GPT-5 leads
GLM-5.3: 45.4 (#41), GPT-5: 69.5 (#2)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| LMArena Longer Query | 1482 | 1399 |
| Fiction.LiveBench | — | 97.2% |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), GPT-5: 63.4 (#65)
| Benchmark | GLM-5.3 | GPT-5 |
|---|---|---|
| LMArena Text | 1471 | 1406 |
| LMArena Creative Writing | 1457 | 1365 |
| EQ-Bench Creative Writing | 2075 | 1627 |
| LMArena Multi-Turn | 1472 | 1426 |
| Short-Story Creative Writing | — | 86% |
| WildBench | — | 85.7% |
Frequently asked questions
Is GLM-5.3 better than GPT-5?
GLM-5.3 is the stronger model overall, scoring 54.8 to 50.9 on the Noometry Index.
Which is cheaper, GLM-5.3 or GPT-5?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-5 lists at $1.25 and $10.
Is GLM-5.3 or GPT-5 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 50.3 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 400K.
How many benchmarks do GLM-5.3 and GPT-5 share?
34 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-5 has 69.