Model comparison
GPT-5 Mini vs Qwen3.5 27B
GPT-5 Mini and Qwen3.5 27B score almost the same on the Noometry Index (41.8 vs 41.9), so choose on price, context window or the category you care about most.
Last verified . 24 shared benchmarks.
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. GPT-5 Mini scores higher in 4 categories and Qwen3.5 27B in 5 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5 Mini leads 46.7 to 38.8.
- The biggest single-benchmark swing is WeirdML: 52.7% for GPT-5 Mini and 39.5% for Qwen3.5 27B.
- GPT-5 Mini is cheaper at $0.25 / $2 per million input/output tokens, against $0.30 / $2.40 for Qwen3.5 27B.
- GPT-5 Mini accepts more context: 400K tokens versus 262K.
- Qwen3.5 27B has downloadable open weights; the other is API-only.
Side by side
| GPT-5 Mini | Qwen3.5 27B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.8 | 41.9 |
| Released | 2025-08-07 | 2026-02-23 |
| Weights | Proprietary | Open |
| Context window | 400K | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.25 | $0.30 |
| Output $ / M tokens | $2 | $2.40 |
| Results tracked | 60 | 28 |
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Category by category
Coding GPT-5 Mini leads
GPT-5 Mini: 40.1 (#146), Qwen3.5 27B: 38.9 (#168)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| WeirdML | 52.7% | 39.5% |
| LMArena Coding | 1406 | 1427 |
| ALE-Bench | 799.77 | 349.45 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| LMArena WebDev | — | 1358 |
| SWE-bench Multilingual | 39.7% | — |
| SciCode | 39.2% | — |
| AlgoTune | 1.38 | — |
Agentic & Tool Use Not comparable
GPT-5 Mini: 31.1 (#70), Qwen3.5 27B: —
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| Vending-Bench 2 | -31.18 | 201.98 |
| Terminal-Bench | 34.8% | — |
| Berkeley Function Calling Leaderboard | 55.5% | — |
Reasoning Qwen3.5 27B leads
GPT-5 Mini: 23.9 (#168), Qwen3.5 27B: 27.5 (#117)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| LMArena Hard Prompts | 1380 | 1414 |
| DTBench | 80.5% | 82.4% |
| LMCA | 34.2% | 34% |
| ARC-AGI-2 | 4.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| NYT Connections (extended) | — | 47.9% |
| ARC-AGI-1 | 54.3% | — |
| CritPt | 0% | — |
| Chess Puzzles | 30% | — |
| EnigmaEval | 8.2% | — |
| Thematic Generalization | — | 45.5% |
| Mystery Game Puzzles | 10% | — |
| Epoch Capabilities Index | 145.52 | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.7 (#69), Qwen3.5 27B: 38.8 (#127)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| LMArena Math | 1378 | 1429 |
| FrontierMath (Tiers 1-3) | 46.7% | — |
| FrontierMath Tier 4 | 12.2% | — |
| MathArena Final-Answer Competitions | — | 56.7% |
| OTIS Mock AIME 2024-2025 | 86.7% | — |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| MATH Level 5 | 97.8% | — |
| FrontierMath (Feb 2025 set) | 27.2% | — |
| FrontierMath Tier 4 (v1) | 6.3% | — |
Knowledge GPT-5 Mini leads
GPT-5 Mini: 45.6 (#86), Qwen3.5 27B: 38.0 (#150)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| Vectara Hallucination Rate | 12.9% | 12.1% |
| LMArena Expert | 1379 | 1428 |
| GPQA Diamond | 75% | — |
| Humanity's Last Exam | 19.4% | — |
| SimpleQA Verified | 21.6% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| GPQA (HELM) | 75.6% | — |
Multimodal Qwen3.5 27B leads
GPT-5 Mini: 35.6 (#85), Qwen3.5 27B: 39.4 (#59)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| LMArena Vision | 1202 | 1241 |
| VPCT | 40.2% | — |
Multilingual Qwen3.5 27B leads
GPT-5 Mini: 48.9 (#137), Qwen3.5 27B: 50.8 (#115)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| LMArena Non-English | 1363 | 1390 |
| LMArena Chinese | 1385 | 1478 |
| LMArena French | 1386 | 1410 |
| LMArena German | 1366 | 1393 |
| LMArena Japanese | 1341 | 1345 |
| LMArena Korean | 1308 | 1358 |
| LMArena Russian | 1362 | 1390 |
| LMArena Spanish | 1355 | 1407 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.2 (#46), Qwen3.5 27B: 73.5 (#119)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| LMArena Instruction Following | 1357 | 1393 |
| IFEval | 92.7% | — |
Long Context Qwen3.5 27B leads
GPT-5 Mini: 41.9 (#132), Qwen3.5 27B: 43.1 (#106)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| LMArena Longer Query | 1355 | 1413 |
| Fiction.LiveBench | 69.4% | — |
Writing & Preference Qwen3.5 27B leads
GPT-5 Mini: 55.2 (#148), Qwen3.5 27B: 59.3 (#111)
| Benchmark | GPT-5 Mini | Qwen3.5 27B |
|---|---|---|
| LMArena Text | 1373 | 1409 |
| LMArena Creative Writing | 1325 | 1362 |
| LMArena Multi-Turn | 1363 | 1410 |
| Short-Story Creative Writing | 83.1% | — |
| EQ-Bench Creative Writing | 1313 | — |
| WildBench | 85.5% | — |
Frequently asked questions
Is GPT-5 Mini better than Qwen3.5 27B?
GPT-5 Mini and Qwen3.5 27B score almost the same on the Noometry Index (41.8 vs 41.9), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-5 Mini or Qwen3.5 27B?
GPT-5 Mini is cheaper. It lists at $0.25 per million input tokens and $2 per million output tokens; Qwen3.5 27B lists at $0.30 and $2.40.
Is GPT-5 Mini or Qwen3.5 27B better for coding?
GPT-5 Mini scores higher on coding benchmarks: 40.1 versus 38.9 in the Noometry coding category.
Which has the bigger context window?
GPT-5 Mini does, with 400K tokens against 262K.
How many benchmarks do GPT-5 Mini and Qwen3.5 27B share?
24 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Qwen3.5 27B has 28.