Model comparison
GLM-5.3-Flash vs GPT-6 Sol
GPT-6 Sol is the stronger model overall, scoring 61.8 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 17× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 35 shared benchmarks.
Summary
- They share 35 benchmarks with published results for both. GLM-5.3-Flash scores higher in 3 categories and GPT-6 Sol in 7 categories; 10 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Sol leads 87.2 to 53.3.
- The biggest single-benchmark swing is FrontierMath Tier 4: 17.1% for GLM-5.3-Flash and 90% for GPT-6 Sol.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-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-6 Sol | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 61.8 |
| Released | 2026-08-20 | 2026-09-22 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.15 | $2 |
| Output $ / M tokens | $0.50 | $10 |
| Results tracked | 40 | 45 |
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Category by category
Coding GPT-6 Sol leads
GLM-5.3-Flash: 53.1 (#31), GPT-6 Sol: 60.1 (#11)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| DeepSWE | 63.4% | 68.8% |
| FrontierCode | 31.8% | 49.3% |
| LMArena WebDev | 1609 | 1688 |
| SciCode | 51.6% | 57.6% |
| LMArena Coding | 1508 | 1447 |
| ALE-Bench | 303.55 | 2,462 |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
Agentic & Tool Use GPT-6 Sol leads
GLM-5.3-Flash: 34.2 (#47), GPT-6 Sol: 37.2 (#36)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| APEX-Agents | 52.8% | 54.3% |
| GDP.pdf | 14% | 26.4% |
| Vending-Bench 2 | — | 14,428 |
Reasoning GPT-6 Sol leads
GLM-5.3-Flash: 48.0 (#42), GPT-6 Sol: 74.0 (#9)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| ARC-AGI-2 | 65.8% | 89.6% |
| ARC-AGI-1 | 91% | 95.5% |
| CritPt | 15.4% | 30.9% |
| LMArena Hard Prompts | 1491 | 1418 |
| Mystery Game Puzzles | 8% | 56% |
| Epoch Capabilities Index | 151.88 | 162.72 |
| NYT Connections (extended) | — | 90.1% |
| Chess Puzzles | 14% | — |
| EBR-Bench | — | 53.3% |
| DTBench | — | 97.3% |
| LMCA | — | 59.1% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GPT-6 Sol leads
GLM-5.3-Flash: 53.3 (#47), GPT-6 Sol: 87.2 (#7)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 89.8% |
| FrontierMath Tier 4 | 17.1% | 90% |
| OTIS Mock AIME 2024-2025 | 93.9% | 100% |
| ProofBench | 21% | 83% |
| LMArena Math | 1500 | 1402 |
Knowledge GPT-6 Sol leads
GLM-5.3-Flash: 58.4 (#36), GPT-6 Sol: 64.8 (#15)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| GPQA Diamond | 90.2% | 94.3% |
| LMArena Expert | 1513 | 1439 |
| SimpleQA Verified | — | 60.7% |
| Vectara Hallucination Rate | — | 6.5% |
Multimodal GPT-6 Sol leads
GLM-5.3-Flash: 42.8 (#27), GPT-6 Sol: 47.6 (#10)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| LMArena Vision | 1296 | 1245 |
| Blueprint-Bench 2 | — | 36.9% |
| Furniture Assembly | — | 58.3% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), GPT-6 Sol: 50.5 (#118)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| LMArena Non-English | 1462 | 1385 |
| LMArena Chinese | 1527 | 1405 |
| LMArena French | 1496 | 1410 |
| LMArena German | 1470 | 1390 |
| LMArena Japanese | 1429 | 1385 |
| LMArena Korean | 1446 | 1341 |
| LMArena Russian | 1469 | 1401 |
| LMArena Spanish | 1471 | 1384 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), GPT-6 Sol: 74.5 (#94)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| LMArena Instruction Following | 1478 | 1412 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), GPT-6 Sol: 43.1 (#108)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| LMArena Longer Query | 1482 | 1411 |
Writing & Preference GPT-6 Sol leads
GLM-5.3-Flash: 65.3 (#50), GPT-6 Sol: 71.9 (#18)
| Benchmark | GLM-5.3-Flash | GPT-6 Sol |
|---|---|---|
| LMArena Text | 1471 | 1395 |
| LMArena Creative Writing | 1442 | 1378 |
| LMArena Multi-Turn | 1467 | 1412 |
| EQ-Bench Creative Writing | — | 2125 |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-6 Sol?
GPT-6 Sol is the stronger model overall, scoring 61.8 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 17× 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-5.3-Flash or GPT-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-6 Sol lists at $2 and $10.
Is GLM-5.3-Flash or GPT-6 Sol better for coding?
GPT-6 Sol scores higher on coding benchmarks: 60.1 versus 53.1 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and GPT-6 Sol share?
35 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-6 Sol has 45.