Model comparison
GPT-5 vs Qwen3.8 27B
GPT-5 is the stronger model overall, scoring 50.9 to 45.3 on the Noometry Index. Qwen3.8 27B costs 3.1× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Last verified . 26 shared benchmarks.
Alibaba (Qwen)Summary
- They share 26 benchmarks with published results for both. GPT-5 scores higher in 6 categories and Qwen3.8 27B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in long context, where GPT-5 leads 69.5 to 44.3.
- The biggest single-benchmark swing is CritPt: 12.6% for GPT-5 and 5.4% for Qwen3.8 27B.
- Qwen3.8 27B is cheaper at $0.99 / $1.49 per million input/output tokens, against $1.25 / $10 for GPT-5.
- GPT-5 accepts more context: 400K tokens versus 262K.
- Qwen3.8 27B has downloadable open weights; the other is API-only.
Side by side
| GPT-5 | Qwen3.8 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 50.9 | 45.3 |
| Released | 2025-08-07 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 33K |
| Input $ / M tokens | $1.25 | $0.99 |
| Output $ / M tokens | $10 | $1.49 |
| Results tracked | 69 | 28 |
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Category by category
Coding Too close to call
GPT-5: 50.7 (#39), Qwen3.8 27B: 50.3 (#42)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| LMArena WebDev | 1417 | 1592 |
| SciCode | 42.9% | 46.6% |
| LMArena Coding | 1436 | 1487 |
| SWE-bench Verified | 73.6% | — |
| SWE-bench Verified (bash only) | 65% | — |
| Aider Polyglot | 88% | — |
| GSO | 6.9% | — |
| WeirdML | 60.7% | — |
| ALE-Bench | 1,162 | — |
| AlgoTune | 1.67 | — |
Agentic & Tool Use Not comparable
GPT-5: 33.0 (#53), Qwen3.8 27B: —
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| Terminal-Bench | 49.6% | — |
| GDPval | 34.8% | — |
| Remote Labor Index | 1.7% | — |
| DeepResearch Bench | 49.6% | — |
| BALROG | 32.8% | — |
| LMArena Search | 1133 | — |
| METR Time Horizons | 69.6% | — |
Reasoning GPT-5 leads
GPT-5: 38.3 (#55), Qwen3.8 27B: 31.5 (#71)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| CritPt | 12.6% | 5.4% |
| LMArena Hard Prompts | 1415 | 1459 |
| DTBench | 90.7% | 88% |
| LMCA | 40% | 41.4% |
| Epoch Capabilities Index | 150 | 149.38 |
| ARC-AGI-2 | 9.9% | — |
| SimpleBench | 56.7% | — |
| Kagi LLM Benchmark | 72.7% | — |
| NYT Connections (extended) | — | 54.5% |
| ARC-AGI-1 | 65.7% | — |
| Chess Puzzles | 37% | — |
| EnigmaEval | 10.5% | — |
| EBR-Bench | 12.7% | — |
| Mystery Game Puzzles | 23% | — |
| Surface Evolver Bench | — | 45% |
| ForecastBench | 61.4 | — |
Math GPT-5 leads
GPT-5: 54.6 (#36), Qwen3.8 27B: 35.7 (#139)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| ProofBench | 18% | 16% |
| LMArena Math | 1406 | 1453 |
| FrontierMath (Tiers 1-3) | 55.4% | — |
| FrontierMath Tier 4 | 22% | — |
| OTIS Mock AIME 2024-2025 | 91.4% | — |
| Omni-MATH | 64.7% | — |
| MATH Level 5 | 98.1% | — |
| FrontierMath (Feb 2025 set) | 32.4% | — |
| FrontierMath Tier 4 (v1) | 12.5% | — |
Knowledge GPT-5 leads
GPT-5: 56.5 (#37), Qwen3.8 27B: 40.9 (#105)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1420 | 1482 |
| GPQA Diamond | 86.2% | — |
| Humanity's Last Exam | 25.3% | — |
| SimpleQA Verified | 50.1% | — |
| MMLU-Pro | 86.3% | — |
| Confabulations | 10.3% | — |
| Vectara Hallucination Rate | 14.7% | — |
| GPQA (HELM) | 79.2% | — |
Multimodal GPT-5 leads
GPT-5: 46.1 (#12), Qwen3.8 27B: 40.5 (#36)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | 1232 | 1270 |
| GeoBench | 81% | — |
| VPCT | 66% | — |
Multilingual Qwen3.8 27B leads
GPT-5: 51.5 (#105), Qwen3.8 27B: 54.0 (#54)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1397 | 1431 |
| LMArena Chinese | 1423 | 1504 |
| LMArena French | 1409 | 1468 |
| LMArena German | 1415 | 1435 |
| LMArena Japanese | 1411 | 1392 |
| LMArena Korean | 1361 | 1389 |
| LMArena Russian | 1406 | 1421 |
| LMArena Spanish | 1398 | 1444 |
Instruction Following Qwen3.8 27B leads
GPT-5: 74.0 (#107), Qwen3.8 27B: 75.8 (#54)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1388 | 1436 |
| IFEval | 87.5% | — |
Long Context GPT-5 leads
GPT-5: 69.5 (#2), Qwen3.8 27B: 44.3 (#66)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1399 | 1448 |
| Fiction.LiveBench | 97.2% | — |
Writing & Preference Qwen3.8 27B leads
GPT-5: 63.5 (#63), Qwen3.8 27B: 65.9 (#41)
| Benchmark | GPT-5 | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1406 | 1441 |
| LMArena Creative Writing | 1365 | 1382 |
| EQ-Bench Creative Writing | 1627 | 1671 |
| LMArena Multi-Turn | 1426 | 1443 |
| Short-Story Creative Writing | 86% | — |
| WildBench | 85.7% | — |
Frequently asked questions
Is GPT-5 better than Qwen3.8 27B?
GPT-5 is the stronger model overall, scoring 50.9 to 45.3 on the Noometry Index. Qwen3.8 27B costs 3.1× less per token, which makes it the better buy when GPT-5's lead doesn't matter for your workload.
Which is cheaper, GPT-5 or Qwen3.8 27B?
Qwen3.8 27B is cheaper. It lists at $0.99 per million input tokens and $1.49 per million output tokens; GPT-5 lists at $1.25 and $10.
Is GPT-5 or Qwen3.8 27B better for coding?
They score almost the same on coding (50.7 vs 50.3); test both on your own repository before choosing.
Which has the bigger context window?
GPT-5 does, with 400K tokens against 262K.
How many benchmarks do GPT-5 and Qwen3.8 27B share?
26 benchmarks have published results for both models. GPT-5 has 69 scored results on Noometry and Qwen3.8 27B has 28.