Model comparison
GLM-5.3-Flash vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 34× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 40 shared benchmarks.
Summary
- They share 40 benchmarks with published results for both. GLM-5.3-Flash scores higher in 2 categories and GPT-5.6 Sol in 8 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.6 to 53.3.
- The biggest single-benchmark swing is FrontierMath Tier 4: 17.1% for GLM-5.3-Flash and 82.9% for GPT-5.6 Sol.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 1M.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | GPT-5.6 Sol | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 65.0 |
| Released | 2026-08-20 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.15 | $4 |
| Output $ / M tokens | $0.50 | $20 |
| Results tracked | 40 | 65 |
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Category by category
Coding GPT-5.6 Sol leads
GLM-5.3-Flash: 53.1 (#31), GPT-5.6 Sol: 65.1 (#7)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| DeepSWE | 63.4% | 72.7% |
| FrontierCode | 31.8% | 47.5% |
| CursorBench | 36.8% | 41.7% |
| LMArena WebDev | 1609 | 1618 |
| FrontierSWE | 18.1% | 32.2% |
| SciCode | 51.6% | 57.1% |
| LMArena Coding | 1508 | 1498 |
| ALE-Bench | 303.55 | 2,177 |
| GSO | — | 76.5% |
| WeirdML | — | 89.4% |
| MirrorCode | — | 20% |
Agentic & Tool Use GPT-5.6 Sol leads
GLM-5.3-Flash: 34.2 (#47), GPT-5.6 Sol: 50.3 (#7)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| APEX-Agents | 52.8% | 51.4% |
| GDP.pdf | 14% | 30.7% |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| LMArena Search | — | 1257 |
| Vending-Bench 2 | — | 9,619 |
Reasoning GPT-5.6 Sol leads
GLM-5.3-Flash: 48.0 (#42), GPT-5.6 Sol: 74.8 (#8)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| ARC-AGI-2 | 65.8% | 92.5% |
| ARC-AGI-1 | 91% | 97.5% |
| CritPt | 15.4% | 32.3% |
| Chess Puzzles | 14% | 64% |
| LMArena Hard Prompts | 1491 | 1484 |
| Mystery Game Puzzles | 8% | 58% |
| Surface Evolver Bench | 52.5% | 93.1% |
| Bench to the Future 3 | 0.15 | 0.14 |
| Epoch Capabilities Index | 151.88 | 161.66 |
| SimpleBench | — | 71.7% |
| Kagi LLM Benchmark | — | 67% |
| NYT Connections (extended) | — | 93.8% |
| EnigmaEval | — | 37.1% |
| EBR-Bench | — | 44.8% |
| DTBench | — | 96% |
| LMCA | — | 59.2% |
Math GPT-5.6 Sol leads
GLM-5.3-Flash: 53.3 (#47), GPT-5.6 Sol: 85.6 (#9)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 89.1% |
| FrontierMath Tier 4 | 17.1% | 82.9% |
| OTIS Mock AIME 2024-2025 | 93.9% | 100% |
| ProofBench | 21% | 83% |
| LMArena Math | 1500 | 1474 |
| FrontierMath Erdős | — | 0% |
Knowledge GPT-5.6 Sol leads
GLM-5.3-Flash: 58.4 (#36), GPT-5.6 Sol: 64.3 (#18)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| GPQA Diamond | 90.2% | 93.5% |
| LMArena Expert | 1513 | 1516 |
| SimpleQA Verified | — | 69.7% |
| Vectara Hallucination Rate | — | 12.4% |
Multimodal GPT-5.6 Sol leads
GLM-5.3-Flash: 42.8 (#27), GPT-5.6 Sol: 48.6 (#9)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | 1296 | 1281 |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |
Multilingual Too close to call
GLM-5.3-Flash: 56.0 (#25), GPT-5.6 Sol: 55.3 (#32)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1462 | 1452 |
| LMArena Chinese | 1527 | 1527 |
| LMArena French | 1496 | 1477 |
| LMArena German | 1470 | 1476 |
| LMArena Japanese | 1429 | 1471 |
| LMArena Korean | 1446 | 1442 |
| LMArena Russian | 1469 | 1468 |
| LMArena Spanish | 1471 | 1441 |
Instruction Following Too close to call
GLM-5.3-Flash: 77.5 (#20), GPT-5.6 Sol: 77.7 (#16)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1478 | 1482 |
Long Context Too close to call
GLM-5.3-Flash: 45.4 (#39), GPT-5.6 Sol: 45.4 (#42)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1482 | 1480 |
Writing & Preference GPT-5.6 Sol leads
GLM-5.3-Flash: 65.3 (#50), GPT-5.6 Sol: 73.3 (#12)
| Benchmark | GLM-5.3-Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1471 | 1457 |
| LMArena Creative Writing | 1442 | 1448 |
| LMArena Multi-Turn | 1467 | 1460 |
| EQ-Bench Creative Writing | — | 1972 |
| EQ-Bench 4 | — | 1250 |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 34× 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-5.3-Flash or GPT-5.6 Sol?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is GLM-5.3-Flash or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 53.1 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and GPT-5.6 Sol share?
40 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-5.6 Sol has 65.