Model comparison
GLM-4.7-Flash vs GPT-5.2
GPT-5.2 is the stronger model overall, scoring 54.1 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 33× less per token, which makes it the better buy when GPT-5.2's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
Summary
- They share 21 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and GPT-5.2 in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.2 leads 50.1 to 20.2.
- The biggest single-benchmark swing is Chess Puzzles: 0% for GLM-4.7-Flash and 49% for GPT-5.2.
- GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $1.75 / $14 for GPT-5.2.
- GPT-5.2 accepts more context: 400K tokens versus 200K.
- GLM-4.7-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7-Flash | GPT-5.2 | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 38.4 | 54.1 |
| Released | 2026-01-19 | 2025-12-11 |
| Weights | Open | Proprietary |
| Context window | 200K | 400K |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.06 | $1.75 |
| Output $ / M tokens | $0.40 | $14 |
| Results tracked | 21 | 67 |
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Category by category
Coding GPT-5.2 leads
GLM-4.7-Flash: 40.0 (#133), GPT-5.2: 52.2 (#32)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| LMArena Coding | 1383 | 1447 |
| SWE-bench Verified | — | 73.8% |
| SWE-bench Verified (bash only) | — | 72.8% |
| LMArena WebDev | — | 1415 |
| SWE-bench Multilingual | — | 66.7% |
| GSO | — | 27.4% |
| WeirdML | — | 72.2% |
| ALE-Bench | — | 1,294 |
| AlgoTune | — | 2.05 |
Agentic & Tool Use Not comparable
GLM-4.7-Flash: —, GPT-5.2: 40.0 (#24)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| Terminal-Bench | — | 64.9% |
| 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% |
| Vending-Bench 2 | — | 3,591 |
Reasoning GPT-5.2 leads
GLM-4.7-Flash: 20.2 (#224), GPT-5.2: 50.1 (#33)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| Chess Puzzles | 0% | 49% |
| LMArena Hard Prompts | 1356 | 1445 |
| ARC-AGI-2 | — | 52.9% |
| SimpleBench | — | 45.8% |
| Kagi LLM Benchmark | — | 73.3% |
| NYT Connections (extended) | — | 83.6% |
| ARC-AGI-1 | — | 86.2% |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| Mystery Game Puzzles | — | 23% |
| DTBench | — | 90.9% |
| LMCA | — | 43.9% |
| Epoch Capabilities Index | — | 153.45 |
| ForecastBench | — | 60.1 |
Math GPT-5.2 leads
GLM-4.7-Flash: 34.4 (#159), GPT-5.2: 59.8 (#32)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 58.3% | 96.1% |
| LMArena Math | 1354 | 1439 |
| FrontierMath (Tiers 1-3) | — | 67.4% |
| FrontierMath Tier 4 | — | 31.7% |
| MathArena Final-Answer Competitions | — | 72% |
| ProofBench | — | 15% |
| FrontierMath (Feb 2025 set) | — | 40.7% |
| FrontierMath Tier 4 (v1) | — | 18.8% |
Knowledge GPT-5.2 leads
GLM-4.7-Flash: 35.4 (#177), GPT-5.2: 59.2 (#29)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| GPQA Diamond | 60.5% | 91.4% |
| Vectara Hallucination Rate | 9.3% | 8.4% |
| LMArena Expert | 1357 | 1445 |
| Humanity's Last Exam | — | 27.8% |
| SimpleQA Verified | — | 37.1% |
Multimodal Not comparable
GLM-4.7-Flash: —, GPT-5.2: 50.9 (#7)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| LMArena Vision | — | 1268 |
| VPCT | — | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |
Multilingual GPT-5.2 leads
GLM-4.7-Flash: 46.8 (#152), GPT-5.2: 53.6 (#63)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| LMArena Non-English | 1331 | 1425 |
| LMArena Chinese | 1403 | 1460 |
| LMArena French | 1332 | 1456 |
| LMArena German | 1336 | 1448 |
| LMArena Korean | 1284 | 1390 |
| LMArena Russian | 1333 | 1440 |
| LMArena Spanish | 1351 | 1433 |
| LMArena Japanese | — | 1421 |
Instruction Following GPT-5.2 leads
GLM-4.7-Flash: 70.3 (#162), GPT-5.2: 74.9 (#83)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | 1327 | 1417 |
Long Context GPT-5.2 leads
GLM-4.7-Flash: 40.9 (#143), GPT-5.2: 44.0 (#74)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| LMArena Longer Query | 1345 | 1428 |
| CL-bench | — | 18.2% |
Writing & Preference GPT-5.2 leads
GLM-4.7-Flash: 47.7 (#206), GPT-5.2: 67.0 (#30)
| Benchmark | GLM-4.7-Flash | GPT-5.2 |
|---|---|---|
| LMArena Text | 1351 | 1439 |
| LMArena Creative Writing | 1297 | 1402 |
| EQ-Bench Creative Writing | 1125 | 1703 |
| LMArena Multi-Turn | 1342 | 1458 |
Frequently asked questions
Is GLM-4.7-Flash better than GPT-5.2?
GPT-5.2 is the stronger model overall, scoring 54.1 to 38.4 on the Noometry Index. GLM-4.7-Flash costs 33× 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-Flash or GPT-5.2?
GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GPT-5.2 lists at $1.75 and $14.
Is GLM-4.7-Flash or GPT-5.2 better for coding?
GPT-5.2 scores higher on coding benchmarks: 52.2 versus 40.0 in the Noometry coding category.
Which has the bigger context window?
GPT-5.2 does, with 400K tokens against 200K.
How many benchmarks do GLM-4.7-Flash and GPT-5.2 share?
21 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5.2 has 67.