Model comparison
GPT-6 Astra vs Qwen3.5-Flash
GPT-6 Astra is the stronger model overall, scoring 70.3 to 42.3 on the Noometry Index. Qwen3.5-Flash costs 114× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. GPT-6 Astra scores higher in 8 categories and Qwen3.5-Flash in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6 Astra leads 93.4 to 36.3.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 93.7% for GPT-6 Astra and 18.2% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 1M.
Side by side
| GPT-6 Astra | Qwen3.5-Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 70.3 | 42.3 |
| Released | 2026-09-03 | 2026-02-23 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1M |
| Max output | 128K | 66K |
| Input $ / M tokens | $10 | $0.10 |
| Output $ / M tokens | $50 | $0.40 |
| Results tracked | 51 | 32 |
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Category by category
Coding GPT-6 Astra leads
GPT-6 Astra: 70.5 (#3), Qwen3.5-Flash: 33.9 (#234)
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| LMArena WebDev | 1792 | 1243 |
| LMArena Coding | 1488 | 1412 |
| ALE-Bench | 2,951 | 221.8 |
| DeepSWE | 74.1% | — |
| FrontierCode | 53.3% | — |
| FrontierSWE | 65.5% | — |
| SciCode | 56.5% | — |
| WeirdML | 93.6% | — |
| MirrorCode | 46.7% | — |
Agentic & Tool Use Not comparable
GPT-6 Astra: 52.3 (#3), Qwen3.5-Flash: —
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | 15,515 | 462.69 |
| APEX-Agents | 64.7% | — |
| Remote Labor Index | 20.8% | — |
| BALROG | 68.3% | — |
Reasoning GPT-6 Astra leads
GPT-6 Astra: 85.1 (#1), Qwen3.5-Flash: 32.9 (#63)
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 72% | 21% |
| LMArena Hard Prompts | 1464 | 1404 |
| Mystery Game Puzzles | 84% | 20% |
| DTBench | 97.3% | 82.9% |
| LMCA | 64.4% | 29.1% |
| Epoch Capabilities Index | 166.6 | 143.98 |
| ARC-AGI-2 | 95% | — |
| NYT Connections (extended) | 98.1% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| EBR-Bench | 76.2% | — |
| Bench to the Future 3 | 0.14 | — |
Math GPT-6 Astra leads
GPT-6 Astra: 93.4 (#1), Qwen3.5-Flash: 36.3 (#117)
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 93.7% | 18.2% |
| OTIS Mock AIME 2024-2025 | 100% | 84.4% |
| LMArena Math | 1463 | 1407 |
| FrontierMath Tier 4 | 97.6% | — |
| ProofBench | 99% | — |
| FrontierMath (Feb 2025 set) | — | 6.2% |
| FrontierMath Erdős | 2.9% | — |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge GPT-6 Astra leads
GPT-6 Astra: 75.3 (#1), Qwen3.5-Flash: 43.2 (#84)
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 95.8% | 82.3% |
| SimpleQA Verified | 75.6% | 20.3% |
| Vectara Hallucination Rate | 8.7% | 10.5% |
| LMArena Expert | 1486 | 1408 |
| Humanity's Last Exam | 54.8% | — |
Multimodal Not comparable
GPT-6 Astra: 55.1 (#3), Qwen3.5-Flash: —
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1280 | — |
| Blueprint-Bench 2 | 49.7% | — |
| Furniture Assembly | 80% | — |
| LMArena Document | 1468 | — |
Multilingual GPT-6 Astra leads
GPT-6 Astra: 54.0 (#53), Qwen3.5-Flash: 50.7 (#116)
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1431 | 1385 |
| LMArena Chinese | 1495 | 1446 |
| LMArena French | 1467 | 1412 |
| LMArena Russian | 1434 | 1379 |
| LMArena Spanish | 1412 | 1399 |
| LMArena German | — | 1389 |
| LMArena Japanese | — | 1369 |
| LMArena Korean | — | 1344 |
Instruction Following GPT-6 Astra leads
GPT-6 Astra: 76.6 (#37), Qwen3.5-Flash: 72.8 (#133)
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1454 | 1374 |
Long Context GPT-6 Astra leads
GPT-6 Astra: 44.7 (#57), Qwen3.5-Flash: 42.4 (#119)
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1460 | 1392 |
Writing & Preference GPT-6 Astra leads
GPT-6 Astra: 75.7 (#7), Qwen3.5-Flash: 58.2 (#117)
| Benchmark | GPT-6 Astra | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1443 | 1397 |
| LMArena Creative Writing | 1423 | 1343 |
| LMArena Multi-Turn | 1457 | 1393 |
| EQ-Bench Creative Writing | 2173 | — |
Frequently asked questions
Is GPT-6 Astra better than Qwen3.5-Flash?
GPT-6 Astra is the stronger model overall, scoring 70.3 to 42.3 on the Noometry Index. Qwen3.5-Flash costs 114× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Which is cheaper, GPT-6 Astra or Qwen3.5-Flash?
Qwen3.5-Flash is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is GPT-6 Astra or Qwen3.5-Flash better for coding?
GPT-6 Astra scores higher on coding benchmarks: 70.5 versus 33.9 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 GPT-6 Astra and Qwen3.5-Flash share?
27 benchmarks have published results for both models. GPT-6 Astra has 51 scored results on Noometry and Qwen3.5-Flash has 32.