Model comparison
GPT-5 Nano vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 33.5 on the Noometry Index. GPT-5 Nano costs 8.1× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Last verified . 24 shared benchmarks.
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. GPT-5 Nano scores higher in 0 categories and Qwen3.8 27B in 10 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where Qwen3.8 27B leads 65.8 to 39.1.
- The biggest single-benchmark swing is ARC-AGI-1: 20.7% for GPT-5 Nano and 87.5% for Qwen3.8 27B.
- GPT-5 Nano is cheaper at $0.05 / $0.40 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- GPT-5 Nano accepts more context: 400K tokens versus 262K.
- Qwen3.8 27B has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Nano | Qwen3.8 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.5 | 46.0 |
| Released | 2025-08-07 | 2026-08-14 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 33K |
| Input $ / M tokens | $0.05 | $0.99 |
| Output $ / M tokens | $0.40 | $1.49 |
| Results tracked | 49 | 31 |
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Category by category
Coding Qwen3.8 27B leads
GPT-5 Nano: 33.6 (#254), Qwen3.8 27B: 50.5 (#44)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| LMArena Coding | 1351 | 1482 |
| SWE-bench Verified (bash only) | 34.8% | — |
| LMArena WebDev | — | 1593 |
| SciCode | — | 46.6% |
| WeirdML | 38.1% | — |
| ALE-Bench | 718.67 | — |
Agentic & Tool Use Qwen3.8 27B leads
GPT-5 Nano: 25.8 (#106), Qwen3.8 27B: 32.9 (#57)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| Terminal-Bench | 21.8% | — |
| APEX-Agents | — | 47.5% |
| Berkeley Function Calling Leaderboard | 51.5% | — |
Reasoning Qwen3.8 27B leads
GPT-5 Nano: 16.3 (#306), Qwen3.8 27B: 41.0 (#54)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| ARC-AGI-2 | 2.6% | 42.4% |
| ARC-AGI-1 | 20.7% | 87.5% |
| LMArena Hard Prompts | 1328 | 1460 |
| DTBench | 62.7% | 88% |
| LMCA | 7.9% | 41.4% |
| Epoch Capabilities Index | 139.38 | 149.38 |
| Kagi LLM Benchmark | 62.2% | — |
| NYT Connections (extended) | — | 54.5% |
| CritPt | — | 5.4% |
| Chess Puzzles | 27% | — |
| Mystery Game Puzzles | 9% | — |
| Surface Evolver Bench | — | 45% |
| ForecastBench | 59.1 | — |
Math Qwen3.8 27B leads
GPT-5 Nano: 29.4 (#241), Qwen3.8 27B: 37.1 (#161)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| ProofBench | 12% | 16% |
| LMArena Math | 1317 | 1456 |
| FrontierMath (Tiers 1-3) | 20% | — |
| FrontierMath Tier 4 | 2.4% | — |
| OTIS Mock AIME 2024-2025 | 81.1% | — |
| Omni-MATH | 54.6% | — |
| MATH Level 5 | 95.2% | — |
| FrontierMath (Feb 2025 set) | 8.3% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge Qwen3.8 27B leads
GPT-5 Nano: 35.9 (#178), Qwen3.8 27B: 41.6 (#109)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1321 | 1482 |
| GPQA Diamond | 69.4% | — |
| SimpleQA Verified | 11.7% | — |
| MMLU-Pro | 77.8% | — |
| Vectara Hallucination Rate | 10.5% | — |
| GPQA (HELM) | 67.9% | — |
Multimodal Qwen3.8 27B leads
GPT-5 Nano: 31.3 (#108), Qwen3.8 27B: 41.3 (#37)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | 1159 | 1271 |
| VPCT | 37.2% | — |
Multilingual Qwen3.8 27B leads
GPT-5 Nano: 45.3 (#172), Qwen3.8 27B: 53.7 (#60)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1313 | 1430 |
| LMArena Chinese | 1356 | 1504 |
| LMArena German | 1327 | 1438 |
| LMArena Japanese | 1226 | 1384 |
| LMArena Korean | 1269 | 1393 |
| LMArena Russian | 1296 | 1415 |
| LMArena Spanish | 1360 | 1448 |
| LMArena French | — | 1465 |
Instruction Following Too close to call
GPT-5 Nano: 75.0 (#79), Qwen3.8 27B: 75.8 (#53)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1306 | 1439 |
| IFEval | 93.2% | — |
Long Context Qwen3.8 27B leads
GPT-5 Nano: 31.3 (#281), Qwen3.8 27B: 44.3 (#70)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1312 | 1450 |
| Fiction.LiveBench | 44.4% | — |
Writing & Preference Qwen3.8 27B leads
GPT-5 Nano: 39.1 (#249), Qwen3.8 27B: 65.8 (#43)
| Benchmark | GPT-5 Nano | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1320 | 1441 |
| LMArena Creative Writing | 1249 | 1384 |
| EQ-Bench Creative Writing | 705 | 1671 |
| LMArena Multi-Turn | 1311 | 1441 |
| WildBench | 80.6% | — |
Frequently asked questions
Is GPT-5 Nano better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 46.0 to 33.5 on the Noometry Index. GPT-5 Nano costs 8.1× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Which is cheaper, GPT-5 Nano or Qwen3.8 27B?
GPT-5 Nano is cheaper. It lists at $0.05 per million input tokens and $0.40 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.
Is GPT-5 Nano or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 33.6 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Nano does, with 400K tokens against 262K.
How many benchmarks do GPT-5 Nano and Qwen3.8 27B share?
24 benchmarks have published results for both models. GPT-5 Nano has 49 scored results on Noometry and Qwen3.8 27B has 31.