Model comparison
GPT-6 Astra vs Qwen2.5-Coder-32B
GPT-6 Astra is the stronger model overall, scoring 70.8 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 27× less per token, which makes it the better buy when GPT-6 Astra's lead doesn't matter for your workload.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. GPT-6 Astra scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Astra leads 85.1 to 21.2.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $10 / $50 for GPT-6 Astra.
- GPT-6 Astra accepts more context: 1.05M tokens versus 33K.
- Qwen2.5-Coder-32B has downloadable open weights; the other is API-only.
Side by side
| GPT-6 Astra | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 70.8 | 33.4 |
| Released | 2026-09-03 | 2024-09-18 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 33K |
| Max output | 128K | 29K |
| Input $ / M tokens | $10 | $0.66 |
| Output $ / M tokens | $50 | $1 |
| Results tracked | 56 | 31 |
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Category by category
Coding GPT-6 Astra leads
GPT-6 Astra: 73.7 (#2), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1487 | 1276 |
| DeepSWE | 74.1% | — |
| FrontierCode | 53.3% | — |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1786 | — |
| FrontierSWE | 65.5% | — |
| SciCode | 56.5% | — |
| GSO | 79.4% | — |
| WeirdML | 93.6% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| MirrorCode | 46.7% | — |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 2,951 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
GPT-6 Astra: 52.9 (#3), Qwen2.5-Coder-32B: —
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| APEX-Agents | 64.7% | — |
| Remote Labor Index | 20.8% | — |
| BALROG | 68.3% | — |
| GDP.pdf | 34.2% | — |
| Vending-Bench 2 | 15,515 | — |
Reasoning GPT-6 Astra leads
GPT-6 Astra: 85.1 (#1), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1462 | 1251 |
| Epoch Capabilities Index | 166.45 | 119.49 |
| ARC-AGI-2 | 95% | — |
| NYT Connections (extended) | 98.1% | — |
| ARC-AGI-1 | 98.5% | — |
| CritPt | 31.7% | — |
| Chess Puzzles | 72% | — |
| EBR-Bench | 76.2% | — |
| LiveBench Reasoning | — | 42.1% |
| Mystery Game Puzzles | 84% | — |
| DTBench | 97.3% | — |
| LiveBench Data Analysis | — | 49.9% |
| LMCA | 64.4% | — |
| Bench to the Future 3 | 0.14 | — |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math GPT-6 Astra leads
GPT-6 Astra: 93.5 (#2), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1465 | 1251 |
| FrontierMath (Tiers 1-3) | 93.7% | — |
| FrontierMath Tier 4 | 97.6% | — |
| OTIS Mock AIME 2024-2025 | 100% | — |
| ProofBench | 99% | — |
| LiveBench Math | — | 46.6% |
| FrontierMath Erdős | 2.9% | — |
| GSM8K | — | 93% |
Knowledge GPT-6 Astra leads
GPT-6 Astra: 75.3 (#1), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1483 | 1221 |
| GPQA Diamond | 95.8% | — |
| Humanity's Last Exam | 54.8% | — |
| SimpleQA Verified | 75.6% | — |
| Vectara Hallucination Rate | 8.7% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multimodal Not comparable
GPT-6 Astra: 55.0 (#3), Qwen2.5-Coder-32B: —
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Vision | 1281 | — |
| Blueprint-Bench 2 | 49.7% | — |
| Furniture Assembly | 80% | — |
| LMArena Document | 1468 | — |
Multilingual GPT-6 Astra leads
GPT-6 Astra: 53.7 (#61), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1430 | 1205 |
| LMArena Chinese | 1484 | 1222 |
| LMArena Russian | 1436 | 1228 |
| LMArena French | 1456 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1379 | — |
| LMArena Korean | 1426 | — |
| LMArena Spanish | 1407 | — |
Instruction Following GPT-6 Astra leads
GPT-6 Astra: 76.3 (#44), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1450 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context GPT-6 Astra leads
GPT-6 Astra: 44.5 (#62), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1456 | 1251 |
Writing & Preference GPT-6 Astra leads
GPT-6 Astra: 75.3 (#7), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | GPT-6 Astra | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1441 | 1230 |
| LMArena Creative Writing | 1418 | 1174 |
| LMArena Multi-Turn | 1448 | 1222 |
| EQ-Bench Creative Writing | 2173 | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is GPT-6 Astra better than Qwen2.5-Coder-32B?
GPT-6 Astra is the stronger model overall, scoring 70.8 to 33.4 on the Noometry Index. Qwen2.5-Coder-32B costs 27× 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 Qwen2.5-Coder-32B?
Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; GPT-6 Astra lists at $10 and $50.
Is GPT-6 Astra or Qwen2.5-Coder-32B better for coding?
GPT-6 Astra scores higher on coding benchmarks: 73.7 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Astra does, with 1.05M tokens against 33K.
How many benchmarks do GPT-6 Astra and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. GPT-6 Astra has 56 scored results on Noometry and Qwen2.5-Coder-32B has 31.