Model comparison
GLM-4.7 vs GPT-5.2
GPT-5.2 is the stronger model overall, scoring 54.1 to 42.0 on the Noometry Index. GLM-4.7 costs 4.8× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
Summary
- They share 33 benchmarks with published results for both. GLM-4.7 scores higher in 0 categories and GPT-5.2 in 9 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.2 leads 50.2 to 24.3.
- The biggest single-benchmark swing is Chess Puzzles: 6% for GLM-4.7 and 49% for GPT-5.2.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- GPT-5.2 accepts more context: 400K tokens versus 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7 | GPT-5.2 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 42.0 | 54.1 |
| Released | 2025-12-22 | 2025-12-11 |
| Weights | Open | Proprietary |
| Context window | 205K | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.60 | $1.75 |
| Output $ / M tokens | $2.20 | $14 |
| Results tracked | 36 | 67 |
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Category by category
Coding GPT-5.2 leads
GLM-4.7: 44.0 (#79), GPT-5.2: 51.6 (#37)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| LMArena WebDev | 1435 | 1416 |
| LMArena Coding | 1454 | 1447 |
| ALE-Bench | 399.48 | 1,294 |
| SWE-bench Verified | — | 73.8% |
| SWE-bench Verified (bash only) | — | 72.8% |
| SWE-bench Multilingual | — | 66.7% |
| SciCode | 45.1% | — |
| GSO | — | 27.4% |
| WeirdML | — | 72.2% |
| AlgoTune | — | 2.05 |
Agentic & Tool Use GPT-5.2 leads
GLM-4.7: 26.5 (#103), GPT-5.2: 40.2 (#24)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| Terminal-Bench | 33.4% | 64.9% |
| Vending-Bench 2 | 2,377 | 3,591 |
| 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-4.7: 24.3 (#164), GPT-5.2: 50.2 (#35)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| SimpleBench | 47.7% | 45.8% |
| Chess Puzzles | 6% | 49% |
| LMArena Hard Prompts | 1443 | 1445 |
| Epoch Capabilities Index | 143.51 | 153.45 |
| ARC-AGI-2 | — | 52.9% |
| Kagi LLM Benchmark | — | 73.3% |
| NYT Connections (extended) | — | 83.6% |
| ARC-AGI-1 | — | 86.2% |
| CritPt | 1.7% | — |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| Mystery Game Puzzles | — | 23% |
| DTBench | — | 90.9% |
| LMCA | — | 43.9% |
| ForecastBench | — | 60.1 |
Math GPT-5.2 leads
GLM-4.7: 38.6 (#135), GPT-5.2: 60.0 (#38)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 96.1% |
| ProofBench | 6% | 15% |
| LMArena Math | 1423 | 1440 |
| FrontierMath (Feb 2025 set) | 2.4% | 40.7% |
| FrontierMath Tier 4 (v1) | 0% | 18.8% |
| FrontierMath (Tiers 1-3) | — | 67.4% |
| FrontierMath Tier 4 | — | 31.7% |
| MathArena Final-Answer Competitions | — | 72% |
Knowledge GPT-5.2 leads
GLM-4.7: 47.0 (#80), GPT-5.2: 59.3 (#32)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| GPQA Diamond | 83.3% | 91.4% |
| SimpleQA Verified | 32.2% | 37.1% |
| Vectara Hallucination Rate | 11.7% | 8.4% |
| LMArena Expert | 1424 | 1445 |
| Humanity's Last Exam | — | 27.8% |
Multimodal Not comparable
GLM-4.7: —, GPT-5.2: 51.3 (#7)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| LMArena Vision | — | 1268 |
| VPCT | — | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |
Multilingual Too close to call
GLM-4.7: 52.8 (#79), GPT-5.2: 53.4 (#67)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1417 | 1425 |
| LMArena Chinese | 1495 | 1460 |
| LMArena French | 1432 | 1455 |
| LMArena German | 1424 | 1448 |
| LMArena Japanese | 1439 | 1420 |
| LMArena Korean | 1399 | 1392 |
| LMArena Russian | 1423 | 1440 |
| LMArena Spanish | 1434 | 1433 |
Instruction Following Too close to call
GLM-4.7: 74.4 (#95), GPT-5.2: 74.7 (#89)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1411 | 1417 |
Long Context GPT-5.2 leads
GLM-4.7: 42.8 (#116), GPT-5.2: 44.0 (#78)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| CL-bench | 15.9% | 18.2% |
| LMArena Longer Query | 1432 | 1428 |
| CL-bench Life | 10.9% | — |
Writing & Preference GPT-5.2 leads
GLM-4.7: 60.9 (#93), GPT-5.2: 66.8 (#32)
| Benchmark | GLM-4.7 | GPT-5.2 |
|---|---|---|
| LMArena Text | 1435 | 1439 |
| LMArena Creative Writing | 1401 | 1401 |
| EQ-Bench Creative Writing | 1413 | 1703 |
| LMArena Multi-Turn | 1446 | 1458 |
Frequently asked questions
Is GLM-4.7 better than GPT-5.2?
GPT-5.2 is the stronger model overall, scoring 54.1 to 42.0 on the Noometry Index. GLM-4.7 costs 4.8× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 or GPT-5.2?
GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GLM-4.7 or GPT-5.2 better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
GPT-5.2 does, with 400K tokens against 205K.
How many benchmarks do GLM-4.7 and GPT-5.2 share?
33 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and GPT-5.2 has 67.