Model comparison
GLM-4.6 vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 40.9 on the Noometry Index. GLM-4.6 costs 8.0× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Summary
- They share 24 benchmarks with published results for both. GLM-4.6 scores higher in 0 categories and GPT-5.6 Sol in 9 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.7 to 23.1.
- The biggest single-benchmark swing is CritPt: 1.1% for GLM-4.6 and 32.3% for GPT-5.6 Sol.
- GLM-4.6 is cheaper at $0.60 / $2.20 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 205K.
- GLM-4.6 has downloadable open weights; the other is API-only.
Side by side
| GLM-4.6 | GPT-5.6 Sol | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 40.9 | 64.4 |
| Released | 2025-09-30 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.60 | $4 |
| Output $ / M tokens | $2.20 | $20 |
| Results tracked | 29 | 64 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.6 Sol leads
GLM-4.6: 39.9 (#137), GPT-5.6 Sol: 61.1 (#7)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| LMArena WebDev | 1339 | 1619 |
| SciCode | 38.4% | 57.1% |
| LMArena Coding | 1449 | 1493 |
| ALE-Bench | 340.82 | 2,177 |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| SWE-bench Verified (bash only) | 55.4% | — |
| CursorBench | — | 41.7% |
| FrontierSWE | — | 32.2% |
| WeirdML | — | 89.4% |
| MirrorCode | — | 20% |
Agentic & Tool Use GPT-5.6 Sol leads
GLM-4.6: 32.1 (#60), GPT-5.6 Sol: 50.2 (#7)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| Terminal-Bench | 24.5% | — |
| APEX-Agents | — | 51.4% |
| Berkeley Function Calling Leaderboard | 72.4% | — |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
| Vending-Bench 2 | — | 9,619 |
Reasoning GPT-5.6 Sol leads
GLM-4.6: 23.1 (#168), GPT-5.6 Sol: 74.7 (#6)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| Kagi LLM Benchmark | 47.4% | 67% |
| CritPt | 1.1% | 32.3% |
| LMArena Hard Prompts | 1440 | 1481 |
| ARC-AGI-2 | — | 92.5% |
| SimpleBench | — | 71.7% |
| NYT Connections (extended) | — | 93.8% |
| ARC-AGI-1 | — | 97.5% |
| Chess Puzzles | — | 64% |
| EnigmaEval | — | 37.1% |
| EBR-Bench | — | 44.8% |
| Mystery Game Puzzles | — | 58% |
| DTBench | — | 96% |
| LMCA | — | 59.2% |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
| Epoch Capabilities Index | — | 161.99 |
Math GPT-5.6 Sol leads
GLM-4.6: 36.4 (#115), GPT-5.6 Sol: 85.5 (#7)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| LMArena Math | 1431 | 1472 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 83% |
| FrontierMath (Feb 2025 set) | 3.8% | — |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-5.6 Sol leads
GLM-4.6: 39.6 (#118), GPT-5.6 Sol: 64.3 (#16)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| Vectara Hallucination Rate | 9.5% | 12.4% |
| LMArena Expert | 1432 | 1517 |
| GPQA Diamond | — | 93.5% |
| SimpleQA Verified | — | 69.7% |
Multimodal Not comparable
GLM-4.6: —, GPT-5.6 Sol: 48.9 (#8)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | — | 1281 |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |
Multilingual GPT-5.6 Sol leads
GLM-4.6: 53.6 (#62), GPT-5.6 Sol: 55.4 (#32)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1426 | 1450 |
| LMArena Chinese | 1500 | 1523 |
| LMArena French | 1459 | 1475 |
| LMArena German | 1447 | 1476 |
| LMArena Japanese | 1394 | 1475 |
| LMArena Korean | 1401 | 1434 |
| LMArena Russian | 1418 | 1466 |
| LMArena Spanish | 1436 | 1449 |
Instruction Following GPT-5.6 Sol leads
GLM-4.6: 74.5 (#93), GPT-5.6 Sol: 77.8 (#16)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1410 | 1480 |
Long Context GPT-5.6 Sol leads
GLM-4.6: 43.4 (#91), GPT-5.6 Sol: 45.3 (#42)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1423 | 1479 |
Writing & Preference GPT-5.6 Sol leads
GLM-4.6: 61.3 (#86), GPT-5.6 Sol: 73.5 (#12)
| Benchmark | GLM-4.6 | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1440 | 1456 |
| LMArena Creative Writing | 1411 | 1450 |
| EQ-Bench Creative Writing | 1411 | 1972 |
| LMArena Multi-Turn | 1427 | 1456 |
| EQ-Bench 4 | — | 1250 |
Frequently asked questions
Is GLM-4.6 better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 40.9 on the Noometry Index. GLM-4.6 costs 8.0× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, GLM-4.6 or GPT-5.6 Sol?
GLM-4.6 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GLM-4.6 or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 39.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 205K.
How many benchmarks do GLM-4.6 and GPT-5.6 Sol share?
24 benchmarks have published results for both models. GLM-4.6 has 29 scored results on Noometry and GPT-5.6 Sol has 64.