Model comparison
GLM-5.3 vs GPT-4 Turbo
GLM-5.3 is the stronger model overall, scoring 54.8 to 30.5 on the Noometry Index.
Last verified . 25 shared benchmarks.
Summary
- They share 25 benchmarks with published results for both. GLM-5.3 scores higher in 8 categories and GPT-4 Turbo in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3 leads 62.3 to 9.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 91.1% for GLM-5.3 and 6.7% for GPT-4 Turbo.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $10 / $30 for GPT-4 Turbo.
- 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-4 Turbo | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 30.5 |
| Released | 2026-08-14 | 2023-11-06 |
| Weights | Open | Proprietary |
| Context window | 1M | 128K |
| Max output | 131K | 4K |
| Input $ / M tokens | $1.40 | $10 |
| Output $ / M tokens | $4.40 | $30 |
| Results tracked | 42 | 36 |
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 Turbo: 33.8 (#249)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| WeirdML | 75.4% | 18% |
| LMArena Coding | 1496 | 1268 |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| CursorBench | 42.6% | — |
| LMArena WebDev | 1622 | — |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| BigCodeBench Instruct | — | 48.2% |
| BigCodeBench Complete | — | 58.2% |
| ALE-Bench | 1,317 | — |
| HumanEval+ | — | 86.6% |
| MBPP+ | — | 73.3% |
Agentic & Tool Use Not comparable
GLM-5.3: 36.4 (#38), GPT-4 Turbo: —
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| APEX-Agents | 56.6% | — |
| METR Time Horizons | — | 36.7% |
| Vending-Bench 2 | 8,164 | — |
Reasoning GLM-5.3 leads
GLM-5.3: 46.1 (#46), GPT-4 Turbo: 15.3 (#317)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| Chess Puzzles | 21% | 6% |
| LMArena Hard Prompts | 1489 | 1251 |
| DTBench | 87.7% | 61.6% |
| LMCA | 55.5% | 9.8% |
| Epoch Capabilities Index | 155.61 | 127.25 |
| SimpleBench | — | 25.1% |
| NYT Connections (extended) | 74.2% | — |
| CritPt | 19.1% | — |
| Mystery Game Puzzles | 33% | — |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 59.4 |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), GPT-4 Turbo: 9.0 (#322)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 0.7% |
| OTIS Mock AIME 2024-2025 | 91.1% | 6.7% |
| LMArena Math | 1489 | 1272 |
| FrontierMath Tier 4 | 29.3% | — |
| ProofBench | 49% | — |
| MATH Level 5 | — | 46.7% |
Knowledge GLM-5.3 leads
GLM-5.3: 58.3 (#37), GPT-4 Turbo: 24.3 (#268)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| GPQA Diamond | 90.9% | 46.6% |
| LMArena Expert | 1516 | 1223 |
| SimpleQA Verified | 41% | — |
| Confabulations | — | 28.4% |
| MMLU | — | 81.3% |
Multimodal Not comparable
GLM-5.3: —, GPT-4 Turbo: 30.6 (#110)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| LMArena Vision | — | 1090 |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), GPT-4 Turbo: 40.5 (#216)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| LMArena Non-English | 1457 | 1245 |
| LMArena Chinese | 1528 | 1242 |
| LMArena French | 1499 | 1276 |
| LMArena German | 1499 | 1259 |
| LMArena Japanese | 1453 | 1194 |
| LMArena Korean | 1472 | 1187 |
| LMArena Russian | 1463 | 1259 |
| LMArena Spanish | 1460 | 1260 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), GPT-4 Turbo: 65.8 (#216)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| LMArena Instruction Following | 1477 | 1249 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), GPT-4 Turbo: 38.0 (#206)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| LMArena Longer Query | 1482 | 1254 |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), GPT-4 Turbo: 47.7 (#206)
| Benchmark | GLM-5.3 | GPT-4 Turbo |
|---|---|---|
| LMArena Text | 1471 | 1272 |
| LMArena Creative Writing | 1457 | 1269 |
| LMArena Multi-Turn | 1472 | 1267 |
| EQ-Bench Creative Writing | 2075 | — |
Frequently asked questions
Is GLM-5.3 better than GPT-4 Turbo?
GLM-5.3 is the stronger model overall, scoring 54.8 to 30.5 on the Noometry Index.
Which is cheaper, GLM-5.3 or GPT-4 Turbo?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-4 Turbo lists at $10 and $30.
Is GLM-5.3 or GPT-4 Turbo better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 33.8 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-4 Turbo share?
25 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-4 Turbo has 36.