Model comparison
GLM-5.3 vs GPT-5.2
GLM-5.3 and GPT-5.2 score almost the same on the Noometry Index (54.8 vs 54.1), so choose on price, context window or the category you care about most.
Last verified . 34 shared benchmarks.
Summary
- They share 34 benchmarks with published results for both. GLM-5.3 scores higher in 6 categories and GPT-5.2 in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3 leads 75.7 to 66.8.
- The biggest single-benchmark swing is ProofBench: 49% for GLM-5.3 and 15% for GPT-5.2.
- GLM-5.3 is cheaper at $1.40 / $4.40 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- 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-5.2 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 54.8 | 54.1 |
| Released | 2026-08-14 | 2025-12-11 |
| Weights | Open | Proprietary |
| Context window | 1M | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $1.40 | $1.75 |
| Output $ / M tokens | $4.40 | $14 |
| Results tracked | 42 | 67 |
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Category by category
Coding GLM-5.3 leads
GLM-5.3: 59.5 (#14), GPT-5.2: 51.6 (#37)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| LMArena WebDev | 1622 | 1416 |
| WeirdML | 75.4% | 72.2% |
| LMArena Coding | 1496 | 1447 |
| ALE-Bench | 1,317 | 1,294 |
| SWE-bench Verified | — | 73.8% |
| DeepSWE | 69% | — |
| FrontierCode | 40.1% | — |
| SWE-bench Verified (bash only) | — | 72.8% |
| CursorBench | 42.6% | — |
| SWE-bench Multilingual | — | 66.7% |
| FrontierSWE | 30.2% | — |
| SciCode | 59% | — |
| GSO | — | 27.4% |
| AlgoTune | — | 2.05 |
Agentic & Tool Use GPT-5.2 leads
GLM-5.3: 36.4 (#38), GPT-5.2: 40.2 (#24)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| Vending-Bench 2 | 8,164 | 3,591 |
| Terminal-Bench | — | 64.9% |
| APEX-Agents | 56.6% | — |
| Berkeley Function Calling Leaderboard | — | 55.9% |
| GDPval | — | 49.7% |
| Remote Labor Index | — | 2.5% |
| τ²-bench Airline | — | 83% |
| τ²-bench Banking | — | 32.2% |
| τ²-bench Retail | — | 81.6% |
| τ²-bench Telecom | — | 89.7% |
| DeepResearch Bench | — | 41.1% |
| LMArena Search | — | 1207 |
| METR Time Horizons | — | 75.3% |
Reasoning GPT-5.2 leads
GLM-5.3: 46.1 (#46), GPT-5.2: 50.2 (#35)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| NYT Connections (extended) | 74.2% | 83.6% |
| Chess Puzzles | 21% | 49% |
| LMArena Hard Prompts | 1489 | 1445 |
| Mystery Game Puzzles | 33% | 23% |
| DTBench | 87.7% | 90.9% |
| LMCA | 55.5% | 43.9% |
| Epoch Capabilities Index | 155.61 | 153.45 |
| ARC-AGI-2 | — | 52.9% |
| SimpleBench | — | 45.8% |
| Kagi LLM Benchmark | — | 73.3% |
| ARC-AGI-1 | — | 86.2% |
| CritPt | 19.1% | — |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| Bench to the Future 3 | 0.15 | — |
| ForecastBench | — | 60.1 |
Math GLM-5.3 leads
GLM-5.3: 62.3 (#33), GPT-5.2: 60.0 (#38)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 68.8% | 67.4% |
| FrontierMath Tier 4 | 29.3% | 31.7% |
| OTIS Mock AIME 2024-2025 | 91.1% | 96.1% |
| ProofBench | 49% | 15% |
| LMArena Math | 1489 | 1440 |
| MathArena Final-Answer Competitions | — | 72% |
| FrontierMath (Feb 2025 set) | — | 40.7% |
| FrontierMath Tier 4 (v1) | — | 18.8% |
Knowledge Too close to call
GLM-5.3: 58.3 (#37), GPT-5.2: 59.3 (#32)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| GPQA Diamond | 90.9% | 91.4% |
| SimpleQA Verified | 41% | 37.1% |
| LMArena Expert | 1516 | 1445 |
| Humanity's Last Exam | — | 27.8% |
| Vectara Hallucination Rate | — | 8.4% |
Multimodal Not comparable
GLM-5.3: —, GPT-5.2: 51.3 (#7)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| LMArena Vision | — | 1268 |
| VPCT | — | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |
Multilingual GLM-5.3 leads
GLM-5.3: 55.7 (#28), GPT-5.2: 53.4 (#67)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1457 | 1425 |
| LMArena Chinese | 1528 | 1460 |
| LMArena French | 1499 | 1455 |
| LMArena German | 1499 | 1448 |
| LMArena Japanese | 1453 | 1420 |
| LMArena Korean | 1472 | 1392 |
| LMArena Russian | 1463 | 1440 |
| LMArena Spanish | 1460 | 1433 |
Instruction Following GLM-5.3 leads
GLM-5.3: 77.5 (#23), GPT-5.2: 74.7 (#89)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1477 | 1417 |
Long Context GLM-5.3 leads
GLM-5.3: 45.4 (#41), GPT-5.2: 44.0 (#78)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1482 | 1428 |
| CL-bench | — | 18.2% |
Writing & Preference GLM-5.3 leads
GLM-5.3: 75.7 (#6), GPT-5.2: 66.8 (#32)
| Benchmark | GLM-5.3 | GPT-5.2 |
|---|---|---|
| LMArena Text | 1471 | 1439 |
| LMArena Creative Writing | 1457 | 1401 |
| EQ-Bench Creative Writing | 2075 | 1703 |
| LMArena Multi-Turn | 1472 | 1458 |
Frequently asked questions
Is GLM-5.3 better than GPT-5.2?
GLM-5.3 and GPT-5.2 score almost the same on the Noometry Index (54.8 vs 54.1), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-5.3 or GPT-5.2?
GLM-5.3 is cheaper. It lists at $1.40 per million input tokens and $4.40 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GLM-5.3 or GPT-5.2 better for coding?
GLM-5.3 scores higher on coding benchmarks: 59.5 versus 51.6 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-5.2 share?
34 benchmarks have published results for both models. GLM-5.3 has 42 scored results on Noometry and GPT-5.2 has 67.