Model comparison
GLM-5.3 vs GPT-5.5
GPT-5.5 is the stronger model overall, scoring 63.4 to 54.8 on the Noometry Index. GLM-5.3 costs 5.2× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Last verified . 40 shared benchmarks.
Summary
- They share 40 benchmarks with published results for both. GLM-5.3 scores higher in 2 categories and GPT-5.5 in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.5 leads 72.8 to 46.1.
- The biggest single-benchmark swing is FrontierMath Tier 4: 29.3% for GLM-5.3 and 72.5% for GPT-5.5.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $5 / $30 for GPT-5.5.
- GPT-5.5 accepts more context: 1.05M tokens versus 1M.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | GPT-5.5 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 63.4 |
| Released | 2026-08-14 | 2026-04-23 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $1.40 | $5 |
| Output $ / M tokens | $4.40 | $30 |
| Results tracked | 42 | 71 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), GPT-5.5: 58.2 (#17)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| DeepSWE | 69% | 67% |
| FrontierCode | 40.1% | 43% |
| LMArena WebDev | 1622 | 1513 |
| SciCode | 59% | 56.1% |
| WeirdML | 75.4% | 84.9% |
| LMArena Coding | 1496 | 1494 |
| ALE-Bench | 1,317 | 1,943 |
| SWE-bench Verified | — | 80.6% |
| CursorBench | 42.6% | — |
| FrontierSWE | 30.2% | — |
| GSO | — | 40.2% |
| MirrorCode | — | 10% |
Agentic & Tool Use GPT-5.5 leads
GLM-5.3: 36.4 (#38), GPT-5.5: 50.7 (#6)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| APEX-Agents | 56.6% | 55.1% |
| Vending-Bench 2 | 8,164 | 7,524 |
| Terminal-Bench | — | 84.7% |
| OSWorld 2.0 | — | 13% |
| Remote Labor Index | — | 6.3% |
| τ²-bench Banking | — | 44.6% |
| DeepResearch Bench | — | 54% |
| PostTrainBench | — | 27.2% |
| ExploitBench | — | 47.4% |
| GBAEval | — | 53.2% |
| GDP.pdf | — | 26% |
| LMArena Search | — | 1242 |
Reasoning GPT-5.5 leads
GLM-5.3: 46.1 (#46), GPT-5.5: 72.8 (#11)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| NYT Connections (extended) | 74.2% | 96.2% |
| CritPt | 19.1% | 27.1% |
| Chess Puzzles | 21% | 54% |
| LMArena Hard Prompts | 1489 | 1489 |
| Mystery Game Puzzles | 33% | 56% |
| DTBench | 87.7% | 96% |
| LMCA | 55.5% | 54.3% |
| Bench to the Future 3 | 0.15 | 0.14 |
| Epoch Capabilities Index | 155.61 | 159.1 |
| ARC-AGI-2 | — | 85% |
| SimpleBench | — | 69% |
| Kagi LLM Benchmark | — | 88.8% |
| ARC-AGI-1 | — | 95% |
| EBR-Bench | — | 34.3% |
| Surface Evolver Bench | — | 88.1% |
| ForecastBench | — | 60.6 |
Math GPT-5.5 leads
GLM-5.3: 62.3 (#33), GPT-5.5: 81.7 (#11)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 85.3% |
| FrontierMath Tier 4 | 29.3% | 72.5% |
| OTIS Mock AIME 2024-2025 | 91.1% | 100% |
| ProofBench | 49% | 50% |
| LMArena Math | 1489 | 1486 |
| MathArena Final-Answer Competitions | — | 94.3% |
| FrontierMath (Feb 2025 set) | — | 51.7% |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | — | 35.4% |
Knowledge GPT-5.5 leads
GLM-5.3: 58.3 (#37), GPT-5.5: 64.4 (#17)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| GPQA Diamond | 90.9% | 94% |
| SimpleQA Verified | 41% | 63% |
| LMArena Expert | 1516 | 1508 |
| Vectara Hallucination Rate | — | 9.3% |
Multimodal Not comparable
GLM-5.3: —, GPT-5.5: 46.9 (#12)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| LMArena Vision | — | 1297 |
| Blueprint-Bench 2 | — | 36.2% |
| Furniture Assembly | — | 44.2% |
| LMArena Document | — | 1486 |
Multilingual Too close to call
GLM-5.3: 55.7 (#28), GPT-5.5: 56.4 (#20)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| LMArena Non-English | 1457 | 1467 |
| LMArena Chinese | 1528 | 1533 |
| LMArena French | 1499 | 1486 |
| LMArena German | 1499 | 1480 |
| LMArena Japanese | 1453 | 1498 |
| LMArena Korean | 1472 | 1460 |
| LMArena Russian | 1463 | 1473 |
| LMArena Spanish | 1460 | 1468 |
Instruction Following Too close to call
GLM-5.3: 77.5 (#23), GPT-5.5: 77.5 (#18)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| LMArena Instruction Following | 1477 | 1479 |
Long Context GPT-5.5 leads
GLM-5.3: 45.4 (#41), GPT-5.5: 48.3 (#12)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| LMArena Longer Query | 1482 | 1484 |
| CL-bench Life | — | 22.2% |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), GPT-5.5: 72.7 (#13)
| Benchmark | GLM-5.3 | GPT-5.5 |
|---|---|---|
| LMArena Text | 1471 | 1472 |
| LMArena Creative Writing | 1457 | 1455 |
| EQ-Bench Creative Writing | 2075 | 1844 |
| LMArena Multi-Turn | 1472 | 1476 |
| EQ-Bench 4 | — | 1315 |
Frequently asked questions
Is GLM-5.3 better than GPT-5.5?
GPT-5.5 is the stronger model overall, scoring 63.4 to 54.8 on the Noometry Index. GLM-5.3 costs 5.2× less per token, which makes it the better buy when GPT-5.5's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3 or GPT-5.5?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.5 lists at $5 and $30.
Is GLM-5.3 or GPT-5.5 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 58.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.5 does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3 and GPT-5.5 share?
40 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-5.5 has 71.