Model comparison
GLM-5.3-Flash vs GPT-6.1 Sol
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 17× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 30 shared benchmarks.
Summary
- They share 30 benchmarks with published results for both. GLM-5.3-Flash scores higher in 4 categories and GPT-6.1 Sol in 6 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6.1 Sol leads 93.7 to 53.3.
- The biggest single-benchmark swing is FrontierMath Tier 4: 17.1% for GLM-5.3-Flash and 100% for GPT-6.1 Sol.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 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-6.1 Sol | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 51.8 | 65.6 |
| Released | 2026-08-20 | 2026-09-29 |
| 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 | 34 |
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Category by category
Coding GPT-6.1 Sol leads
GLM-5.3-Flash: 53.1 (#31), GPT-6.1 Sol: 63.2 (#8)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| DeepSWE | 63.4% | 75.2% |
| FrontierCode | 31.8% | 50.2% |
| LMArena WebDev | 1609 | 1755 |
| SciCode | 51.6% | 55.8% |
| LMArena Coding | 1508 | 1487 |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GPT-6.1 Sol leads
GLM-5.3-Flash: 34.2 (#47), GPT-6.1 Sol: 39.6 (#26)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| APEX-Agents | 52.8% | 60% |
| GDP.pdf | 14% | 32% |
Reasoning GPT-6.1 Sol leads
GLM-5.3-Flash: 48.0 (#42), GPT-6.1 Sol: 81.9 (#2)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| ARC-AGI-2 | 65.8% | 94.2% |
| ARC-AGI-1 | 91% | 98.5% |
| CritPt | 15.4% | 31.7% |
| Chess Puzzles | 14% | 61% |
| LMArena Hard Prompts | 1491 | 1466 |
| Mystery Game Puzzles | 8% | 80% |
| Epoch Capabilities Index | 151.88 | 166.09 |
| NYT Connections (extended) | — | 95.5% |
| EBR-Bench | — | 54.3% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GPT-6.1 Sol leads
GLM-5.3-Flash: 53.3 (#47), GPT-6.1 Sol: 93.7 (#1)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 93.7% |
| FrontierMath Tier 4 | 17.1% | 100% |
| OTIS Mock AIME 2024-2025 | 93.9% | 100% |
| ProofBench | 21% | 99% |
| LMArena Math | 1500 | 1464 |
Knowledge GPT-6.1 Sol leads
GLM-5.3-Flash: 58.4 (#36), GPT-6.1 Sol: 71.8 (#4)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| GPQA Diamond | 90.2% | 95.4% |
| LMArena Expert | 1513 | 1502 |
| SimpleQA Verified | — | 73.9% |
Multimodal GPT-6.1 Sol leads
GLM-5.3-Flash: 42.8 (#27), GPT-6.1 Sol: 52.7 (#5)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Vision | 1296 | 1288 |
| Furniture Assembly | — | 80% |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), GPT-6.1 Sol: 54.3 (#46)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Non-English | 1462 | 1438 |
| LMArena Chinese | 1527 | 1477 |
| LMArena Russian | 1469 | 1455 |
| LMArena French | 1496 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Spanish | 1471 | — |
Instruction Following Too close to call
GLM-5.3-Flash: 77.5 (#20), GPT-6.1 Sol: 77.0 (#29)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Instruction Following | 1478 | 1468 |
Long Context Too close to call
GLM-5.3-Flash: 45.4 (#39), GPT-6.1 Sol: 44.9 (#54)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Longer Query | 1482 | 1465 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), GPT-6.1 Sol: 63.6 (#63)
| Benchmark | GLM-5.3-Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Text | 1471 | 1447 |
| LMArena Creative Writing | 1442 | 1432 |
| LMArena Multi-Turn | 1467 | 1449 |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-6.1 Sol?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 51.8 on the Noometry Index. GLM-5.3-Flash costs 17× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3-Flash or GPT-6.1 Sol?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is GLM-5.3-Flash or GPT-6.1 Sol better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 53.1 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and GPT-6.1 Sol share?
30 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and GPT-6.1 Sol has 34.