Model comparison
GLM-5.3 vs GPT-4
GLM-5.3 is the stronger model overall, scoring 54.8 to 29.1 on the Noometry Index.
Last verified . 26 shared benchmarks.
Summary
- They share 26 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and GPT-4 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 10.8.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 1.1% for GPT-4.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $30 / $60 for GPT-4.
- GLM-5.3 accepts more context: 1M tokens versus 8K.
- GLM-5.3 has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3 | GPT-4 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 29.1 |
| Released | 2026-08-14 | 2023-03-14 |
| Weights | Open | Proprietary |
| Context window | 1M | 8K |
| Max output | 131K | 8K |
| Input $ / M tokens | $1.40 | $30 |
| Output $ / M tokens | $4.40 | $60 |
| Results tracked | 42 | 38 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), GPT-4: 31.6 (#283)
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| WeirdML | 75.4% | 12.4% |
| LMArena Coding | 1496 | 1254 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| BigCodeBench Instruct | — | 46% |
| BigCodeBench Complete | — | 57.2% |
| ALE-Bench | 1,317 | — |
| HumanEval+ | — | 79.3% |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), GPT-4: —
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| APEX-Agents | 56.6% | — |
| METR Time Horizons | — | 36.1% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), GPT-4: 17.8 (#289)
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| Chess Puzzles | 21% | 4% |
| LMArena Hard Prompts | 1489 | 1241 |
| Mystery Game Puzzles | 33% | 12% |
| DTBench | 87.7% | 62.7% |
| LMCA | 55.5% | 17.1% |
| Epoch Capabilities Index | 155.61 | 125.89 |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Bench to the Future 3 | 0.15 | — |
| BIG-Bench Hard | — | 75.1% |
| ForecastBench | — | 57.8 |
| HellaSwag | — | 95.3% |
| WinoGrande | — | 87.5% |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), GPT-4: 10.8 (#309)
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 91.1% | 1.1% |
| LMArena Math | 1489 | 1269 |
| FrontierMath (Tiers 1-3) | 68.8% | — |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| MATH Level 5 | — | 23% |
| GSM8K | — | 92% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), GPT-4: 18.4 (#282)
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| GPQA Diamond | 90.9% | 35.7% |
| LMArena Expert | 1516 | 1211 |
| SimpleQA Verified | 41% | — |
| MMLU | — | 86.4% |
| TriviaQA | — | 84.8% |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), GPT-4: 40.6 (#215)
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| LMArena Non-English | 1457 | 1246 |
| LMArena Chinese | 1528 | 1242 |
| LMArena French | 1499 | 1283 |
| LMArena German | 1499 | 1251 |
| LMArena Japanese | 1453 | 1209 |
| LMArena Korean | 1472 | 1184 |
| LMArena Russian | 1463 | 1251 |
| LMArena Spanish | 1460 | 1261 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), GPT-4: 65.3 (#222)
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| LMArena Instruction Following | 1477 | 1241 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), GPT-4: 37.7 (#212)
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| LMArena Longer Query | 1482 | 1244 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), GPT-4: 34.9 (#268)
| Benchmark | GLM-5.3 | GPT-4 |
|---|---|---|
| LMArena Text | 1471 | 1263 |
| LMArena Creative Writing | 1457 | 1244 |
| EQ-Bench Creative Writing | 2075 | 752 |
| LMArena Multi-Turn | 1472 | 1257 |
Frequently asked questions
Is GLM-5.3 better than GPT-4?
GLM-5.3 is the stronger model overall, scoring 54.8 to 29.1 on the Noometry Index.
Which is cheaper, GLM-5.3 or GPT-4?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-4 lists at $30 and $60.
Is GLM-5.3 or GPT-4 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 31.6 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3 does, with 1M tokens against 8K.
How many benchmarks do GLM-5.3 and GPT-4 share?
26 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-4 has 38.