Model comparison
GLM-4.7 vs GPT-6 Sol
GPT-6 Sol is the stronger model overall, scoring 61.8 to 42.0 on the Noometry Index. GLM-4.7 costs 4.0× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. GLM-4.7 scores higher in 1 category and GPT-6 Sol in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 74.0 to 24.3.
- The biggest single-benchmark swing is ProofBench: 6% for GLM-4.7 and 83% for GPT-6 Sol.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 205K.
- GLM-4.7 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.7 | GPT-6 Sol | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 42.0 | 61.8 |
| Released | 2025-12-22 | 2026-09-22 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.60 | $2 |
| Output $ / M tokens | $2.20 | $10 |
| Results tracked | 36 | 45 |
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Category by category
Coding GPT-6 Sol leads
GLM-4.7: 44.0 (#79), GPT-6 Sol: 60.1 (#11)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| LMArena WebDev | 1435 | 1688 |
| SciCode | 45.1% | 57.6% |
| LMArena Coding | 1454 | 1447 |
| ALE-Bench | 399.48 | 2,462 |
| DeepSWE | — | 68.8% |
| FrontierCode | — | 49.3% |
Agentic & Tool Use GPT-6 Sol leads
GLM-4.7: 26.5 (#103), GPT-6 Sol: 37.2 (#36)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| Vending-Bench 2 | 2,377 | 14,428 |
| Terminal-Bench | 33.4% | — |
| APEX-Agents | — | 54.3% |
| GDP.pdf | — | 26.4% |
Reasoning GPT-6 Sol leads
GLM-4.7: 24.3 (#164), GPT-6 Sol: 74.0 (#9)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| CritPt | 1.7% | 30.9% |
| LMArena Hard Prompts | 1443 | 1418 |
| Epoch Capabilities Index | 143.51 | 162.72 |
| ARC-AGI-2 | — | 89.6% |
| SimpleBench | 47.7% | — |
| NYT Connections (extended) | — | 90.1% |
| ARC-AGI-1 | — | 95.5% |
| Chess Puzzles | 6% | — |
| EBR-Bench | — | 53.3% |
| Mystery Game Puzzles | — | 56% |
| DTBench | — | 97.3% |
| LMCA | — | 59.1% |
Math GPT-6 Sol leads
GLM-4.7: 38.6 (#135), GPT-6 Sol: 87.2 (#7)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 100% |
| ProofBench | 6% | 83% |
| LMArena Math | 1423 | 1402 |
| FrontierMath (Tiers 1-3) | — | 89.8% |
| FrontierMath Tier 4 | — | 90% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GPT-6 Sol leads
GLM-4.7: 47.0 (#80), GPT-6 Sol: 64.8 (#15)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| GPQA Diamond | 83.3% | 94.3% |
| SimpleQA Verified | 32.2% | 60.7% |
| Vectara Hallucination Rate | 11.7% | 6.5% |
| LMArena Expert | 1424 | 1439 |
Multimodal Not comparable
GLM-4.7: —, GPT-6 Sol: 47.6 (#10)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| LMArena Vision | — | 1245 |
| Blueprint-Bench 2 | — | 36.9% |
| Furniture Assembly | — | 58.3% |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), GPT-6 Sol: 50.5 (#118)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| LMArena Non-English | 1417 | 1385 |
| LMArena Chinese | 1495 | 1405 |
| LMArena French | 1432 | 1410 |
| LMArena German | 1424 | 1390 |
| LMArena Japanese | 1439 | 1385 |
| LMArena Korean | 1399 | 1341 |
| LMArena Russian | 1423 | 1401 |
| LMArena Spanish | 1434 | 1384 |
Instruction Following Too close to call
GLM-4.7: 74.4 (#95), GPT-6 Sol: 74.5 (#94)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| LMArena Instruction Following | 1411 | 1412 |
Long Context Too close to call
GLM-4.7: 42.8 (#116), GPT-6 Sol: 43.1 (#108)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| LMArena Longer Query | 1432 | 1411 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GPT-6 Sol leads
GLM-4.7: 60.9 (#93), GPT-6 Sol: 71.9 (#18)
| Benchmark | GLM-4.7 | GPT-6 Sol |
|---|---|---|
| LMArena Text | 1435 | 1395 |
| LMArena Creative Writing | 1401 | 1378 |
| EQ-Bench Creative Writing | 1413 | 2125 |
| LMArena Multi-Turn | 1446 | 1412 |
Frequently asked questions
Is GLM-4.7 better than GPT-6 Sol?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 42.0 on the Noometry Index. GLM-4.7 costs 4.0× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, GLM-4.7 or GPT-6 Sol?
GLM-4.7 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is GLM-4.7 or GPT-6 Sol better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 44.0 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 205K.
How many benchmarks do GLM-4.7 and GPT-6 Sol share?
29 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and GPT-6 Sol has 45.