Model comparison
GLM-5.3-Flash vs GPT-6 Astra
GPT-6 Astra is the stronger model overall, scoring 70.3 to 49.6 on the Noometry Index. GLM-5.3-Flash costs 84× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 31 shared benchmarks.
Summary
- They share 31 benchmarks with published results for both. GLM-5.3-Flash scores higher in 3 categories and GPT-6 Astra in 7 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Astra leads 85.1 to 31.7.
- The biggest single-benchmark swing is FrontierMath Tier 4: 17.1% for GLM-5.3-Flash and 97.6% for GPT-6 Astra.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra 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 Astra | |
|---|---|---|
| Provider | Z.ai (Zhipu) | OpenAI |
| Noometry Index | 49.6 | 70.3 |
| Released | 2026-08-20 | 2026-09-03 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 131K | 128K |
| Input $ / M tokens | $0.15 | $10 |
| Output $ / M tokens | $0.50 | $50 |
| Results tracked | 36 | 51 |
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Category by category
Coding GPT-6 Astra leads
GLM-5.3-Flash: 53.7 (#26), GPT-6 Astra: 70.5 (#3)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| DeepSWE | 63.4% | 74.1% |
| FrontierCode | 31.8% | 53.3% |
| LMArena WebDev | 1614 | 1792 |
| SciCode | 51.6% | 56.5% |
| LMArena Coding | 1506 | 1488 |
| ALE-Bench | 303.55 | 2,951 |
| FrontierSWE | — | 65.5% |
| WeirdML | — | 93.6% |
| MirrorCode | — | 46.7% |
Agentic & Tool Use GPT-6 Astra leads
GLM-5.3-Flash: 34.3 (#41), GPT-6 Astra: 52.3 (#3)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| APEX-Agents | 52.8% | 64.7% |
| Remote Labor Index | — | 20.8% |
| BALROG | — | 68.3% |
| GDP.pdf | 14% | — |
| Vending-Bench 2 | — | 15,515 |
Reasoning GPT-6 Astra leads
GLM-5.3-Flash: 31.7 (#69), GPT-6 Astra: 85.1 (#1)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| CritPt | 15.4% | 31.7% |
| Chess Puzzles | 14% | 72% |
| LMArena Hard Prompts | 1490 | 1464 |
| Mystery Game Puzzles | 8% | 84% |
| Bench to the Future 3 | 0.15 | 0.14 |
| Epoch Capabilities Index | 151.9 | 166.6 |
| ARC-AGI-2 | — | 95% |
| NYT Connections (extended) | — | 98.1% |
| ARC-AGI-1 | — | 98.5% |
| EBR-Bench | — | 76.2% |
| DTBench | — | 97.3% |
| LMCA | — | 64.4% |
| Surface Evolver Bench | 52.5% | — |
Math GPT-6 Astra leads
GLM-5.3-Flash: 53.1 (#38), GPT-6 Astra: 93.4 (#1)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 93.7% |
| FrontierMath Tier 4 | 17.1% | 97.6% |
| OTIS Mock AIME 2024-2025 | 93.9% | 100% |
| ProofBench | 21% | 99% |
| LMArena Math | 1499 | 1463 |
| FrontierMath Erdős | — | 2.9% |
Knowledge GPT-6 Astra leads
GLM-5.3-Flash: 58.4 (#33), GPT-6 Astra: 75.3 (#1)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| GPQA Diamond | 90.2% | 95.8% |
| LMArena Expert | 1512 | 1486 |
| Humanity's Last Exam | — | 54.8% |
| SimpleQA Verified | — | 75.6% |
| Vectara Hallucination Rate | — | 8.7% |
Multimodal GPT-6 Astra leads
GLM-5.3-Flash: 42.6 (#25), GPT-6 Astra: 55.1 (#3)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| LMArena Vision | 1304 | 1280 |
| Blueprint-Bench 2 | — | 49.7% |
| Furniture Assembly | — | 80% |
| LMArena Document | — | 1468 |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.2 (#24), GPT-6 Astra: 54.0 (#53)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| LMArena Non-English | 1462 | 1431 |
| LMArena Chinese | 1529 | 1495 |
| LMArena French | 1495 | 1467 |
| LMArena Russian | 1472 | 1434 |
| LMArena Spanish | 1471 | 1412 |
| LMArena German | 1457 | — |
| LMArena Japanese | 1420 | — |
| LMArena Korean | 1443 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.8 (#17), GPT-6 Astra: 76.6 (#37)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| LMArena Instruction Following | 1480 | 1454 |
Long Context Too close to call
GLM-5.3-Flash: 45.3 (#41), GPT-6 Astra: 44.7 (#57)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| LMArena Longer Query | 1479 | 1460 |
Writing & Preference GPT-6 Astra leads
GLM-5.3-Flash: 65.4 (#49), GPT-6 Astra: 75.7 (#7)
| Benchmark | GLM-5.3-Flash | GPT-6 Astra |
|---|---|---|
| LMArena Text | 1471 | 1443 |
| LMArena Creative Writing | 1440 | 1423 |
| LMArena Multi-Turn | 1471 | 1457 |
| EQ-Bench Creative Writing | — | 2173 |
Frequently asked questions
Is GLM-5.3-Flash better than GPT-6 Astra?
GPT-6 Astra is the stronger model overall, scoring 70.3 to 49.6 on the Noometry Index. GLM-5.3-Flash costs 84× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, GLM-5.3-Flash or GPT-6 Astra?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is GLM-5.3-Flash or GPT-6 Astra better for coding?
GPT-6 Astra scores higher on coding benchmarks: 70.5 versus 53.7 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 1M.
How many benchmarks do GLM-5.3-Flash and GPT-6 Astra share?
31 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-6 Astra has 51.