Model comparison
GLM-5 vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 45.9 on the Noometry Index. GLM-5 costs 5.2× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 33 shared benchmarks.
Summary
- They share 33 benchmarks with published results for both. GLM-5 scores higher in 0 categories and GPT-5.6 Sol in 9 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.7 to 27.6.
- The biggest single-benchmark swing is ARC-AGI-2: 4.9% for GLM-5 and 92.5% for GPT-5.6 Sol.
- GLM-5 is cheaper at $1 / $3.20 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-5 has downloadable open weights; the other is API-only.
Side by side
| GLM-5 | GPT-5.6 Sol | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 45.9 | 64.4 |
| Released | 2026-02-11 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 205K | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $1 | $4 |
| Output $ / M tokens | $3.20 | $20 |
| Results tracked | 45 | 64 |
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Category by category
Coding GPT-5.6 Sol leads
GLM-5: 49.0 (#47), GPT-5.6 Sol: 61.1 (#7)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| LMArena WebDev | 1434 | 1619 |
| WeirdML | 48.2% | 89.4% |
| LMArena Coding | 1461 | 1493 |
| ALE-Bench | 765.62 | 2,177 |
| SWE-bench Verified | 72.1% | — |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| SWE-bench Verified (bash only) | 72.8% | — |
| CursorBench | — | 41.7% |
| SWE-bench Multilingual | 69.7% | — |
| FrontierSWE | — | 32.2% |
| SciCode | — | 57.1% |
| MirrorCode | — | 20% |
Agentic & Tool Use GPT-5.6 Sol leads
GLM-5: 30.9 (#65), GPT-5.6 Sol: 50.2 (#7)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| τ²-bench Banking | 9.8% | 46.9% |
| Vending-Bench 2 | 4,432 | 9,619 |
| Terminal-Bench | 52.4% | — |
| APEX-Agents | — | 51.4% |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Airline | 82.5% | — |
| τ²-bench Retail | 73.7% | — |
| τ²-bench Telecom | 86.8% | — |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
Reasoning GPT-5.6 Sol leads
GLM-5: 27.6 (#100), GPT-5.6 Sol: 74.7 (#6)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| ARC-AGI-2 | 4.9% | 92.5% |
| SimpleBench | 53.2% | 71.7% |
| Kagi LLM Benchmark | 75% | 67% |
| NYT Connections (extended) | 74.8% | 93.8% |
| ARC-AGI-1 | 44.7% | 97.5% |
| Chess Puzzles | 10% | 64% |
| LMArena Hard Prompts | 1451 | 1481 |
| Epoch Capabilities Index | 145.84 | 161.99 |
| CritPt | — | 32.3% |
| 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 |
| ForecastBench | 61 | — |
Math GPT-5.6 Sol leads
GLM-5: 44.8 (#66), GPT-5.6 Sol: 85.5 (#7)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 80% | 100% |
| LMArena Math | 1440 | 1472 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| MathArena Final-Answer Competitions | 65.7% | — |
| ProofBench | — | 83% |
| FrontierMath (Feb 2025 set) | 16.4% | — |
| FrontierMath Erdős | — | 0% |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge GPT-5.6 Sol leads
GLM-5: 52.2 (#59), GPT-5.6 Sol: 64.3 (#16)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | 87.8% | 93.5% |
| Vectara Hallucination Rate | 10.1% | 12.4% |
| LMArena Expert | 1454 | 1517 |
| SimpleQA Verified | — | 69.7% |
Multimodal Not comparable
GLM-5: —, GPT-5.6 Sol: 48.9 (#8)
| Benchmark | GLM-5 | 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-5: 53.9 (#57), GPT-5.6 Sol: 55.4 (#32)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1430 | 1450 |
| LMArena Chinese | 1511 | 1523 |
| LMArena French | 1455 | 1475 |
| LMArena German | 1445 | 1476 |
| LMArena Japanese | 1416 | 1475 |
| LMArena Korean | 1421 | 1434 |
| LMArena Russian | 1436 | 1466 |
| LMArena Spanish | 1452 | 1449 |
Instruction Following GPT-5.6 Sol leads
GLM-5: 75.4 (#64), GPT-5.6 Sol: 77.8 (#16)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1427 | 1480 |
Long Context Too close to call
GLM-5: 44.6 (#58), GPT-5.6 Sol: 45.3 (#42)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1446 | 1479 |
| CL-bench | 18.7% | — |
Writing & Preference GPT-5.6 Sol leads
GLM-5: 66.2 (#38), GPT-5.6 Sol: 73.5 (#12)
| Benchmark | GLM-5 | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1446 | 1456 |
| LMArena Creative Writing | 1438 | 1450 |
| EQ-Bench Creative Writing | 1601 | 1972 |
| LMArena Multi-Turn | 1456 | 1456 |
| EQ-Bench 4 | — | 1250 |
Frequently asked questions
Is GLM-5 better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 45.9 on the Noometry Index. GLM-5 costs 5.2× 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-5 or GPT-5.6 Sol?
GLM-5 is cheaper. It lists at $1 per million input tokens and $3.20 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GLM-5 or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 49.0 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-5 and GPT-5.6 Sol share?
33 benchmarks have published results for both models. GLM-5 has 45 scored results on Noometry and GPT-5.6 Sol has 64.