Model comparison
GPT-5 Mini vs Qwen3.5-Flash
GPT-5 Mini and Qwen3.5-Flash score almost the same on the Noometry Index (41.8 vs 42.5), so choose on price, context window or the category you care about most.
Last verified . 31 shared benchmarks.
Alibaba (Qwen)Summary
- They share 31 benchmarks with published results for both. GPT-5 Mini scores higher in 4 categories and Qwen3.5-Flash in 4 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.5-Flash leads 33.7 to 23.9.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 46.7% for GPT-5 Mini and 18.2% for Qwen3.5-Flash.
- Qwen3.5-Flash is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.25 / $2 for GPT-5 Mini.
- Qwen3.5-Flash accepts more context: 1M tokens versus 400K.
Side by side
| GPT-5 Mini | Qwen3.5-Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 41.8 | 42.5 |
| Released | 2025-08-07 | 2026-02-23 |
| Weights | Proprietary | Proprietary |
| Context window | 400K | 1M |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.25 | $0.10 |
| Output $ / M tokens | $2 | $0.40 |
| Results tracked | 60 | 32 |
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Category by category
Coding GPT-5 Mini leads
GPT-5 Mini: 40.1 (#146), Qwen3.5-Flash: 34.2 (#242)
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| LMArena Coding | 1406 | 1412 |
| ALE-Bench | 799.77 | 221.8 |
| SWE-bench Verified | 64.7% | — |
| SWE-bench Verified (bash only) | 59.8% | — |
| LMArena WebDev | — | 1244 |
| SWE-bench Multilingual | 39.7% | — |
| SciCode | 39.2% | — |
| WeirdML | 52.7% | — |
| AlgoTune | 1.38 | — |
Agentic & Tool Use Not comparable
GPT-5 Mini: 31.1 (#70), Qwen3.5-Flash: —
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| Vending-Bench 2 | -31.18 | 462.69 |
| Terminal-Bench | 34.8% | — |
| Berkeley Function Calling Leaderboard | 55.5% | — |
Reasoning Qwen3.5-Flash leads
GPT-5 Mini: 23.9 (#168), Qwen3.5-Flash: 33.7 (#72)
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| Chess Puzzles | 30% | 21% |
| LMArena Hard Prompts | 1380 | 1403 |
| Mystery Game Puzzles | 10% | 20% |
| DTBench | 80.5% | 82.9% |
| LMCA | 34.2% | 29.1% |
| Epoch Capabilities Index | 145.52 | 143.98 |
| ARC-AGI-2 | 4.4% | — |
| Kagi LLM Benchmark | 70.3% | — |
| ARC-AGI-1 | 54.3% | — |
| CritPt | 0% | — |
| EnigmaEval | 8.2% | — |
| ForecastBench | 61 | — |
Math GPT-5 Mini leads
GPT-5 Mini: 46.7 (#69), Qwen3.5-Flash: 37.4 (#158)
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 46.7% | 18.2% |
| OTIS Mock AIME 2024-2025 | 86.7% | 84.4% |
| LMArena Math | 1378 | 1407 |
| FrontierMath (Feb 2025 set) | 27.2% | 6.2% |
| FrontierMath Tier 4 (v1) | 6.3% | 0% |
| FrontierMath Tier 4 | 12.2% | — |
| ProofBench | 9% | — |
| Omni-MATH | 72.2% | — |
| MATH Level 5 | 97.8% | — |
Knowledge GPT-5 Mini leads
GPT-5 Mini: 45.6 (#86), Qwen3.5-Flash: 43.2 (#93)
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| GPQA Diamond | 75% | 82.3% |
| SimpleQA Verified | 21.6% | 20.3% |
| Vectara Hallucination Rate | 12.9% | 10.5% |
| LMArena Expert | 1379 | 1407 |
| Humanity's Last Exam | 19.4% | — |
| MMLU-Pro | 83.5% | — |
| Confabulations | 13.3% | — |
| GPQA (HELM) | 75.6% | — |
Multimodal Not comparable
GPT-5 Mini: 35.6 (#85), Qwen3.5-Flash: —
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| LMArena Vision | 1202 | — |
| VPCT | 40.2% | — |
Multilingual Qwen3.5-Flash leads
GPT-5 Mini: 48.9 (#137), Qwen3.5-Flash: 50.5 (#121)
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| LMArena Non-English | 1363 | 1385 |
| LMArena Chinese | 1385 | 1446 |
| LMArena French | 1386 | 1412 |
| LMArena German | 1366 | 1390 |
| LMArena Japanese | 1341 | 1368 |
| LMArena Korean | 1308 | 1344 |
| LMArena Russian | 1362 | 1379 |
| LMArena Spanish | 1355 | 1400 |
Instruction Following GPT-5 Mini leads
GPT-5 Mini: 76.2 (#46), Qwen3.5-Flash: 72.6 (#139)
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| LMArena Instruction Following | 1357 | 1374 |
| IFEval | 92.7% | — |
Long Context Too close to call
GPT-5 Mini: 41.9 (#132), Qwen3.5-Flash: 42.4 (#124)
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| LMArena Longer Query | 1355 | 1392 |
| Fiction.LiveBench | 69.4% | — |
Writing & Preference Qwen3.5-Flash leads
GPT-5 Mini: 55.2 (#148), Qwen3.5-Flash: 57.9 (#122)
| Benchmark | GPT-5 Mini | Qwen3.5-Flash |
|---|---|---|
| LMArena Text | 1373 | 1397 |
| LMArena Creative Writing | 1325 | 1343 |
| LMArena Multi-Turn | 1363 | 1393 |
| 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-Flash?
GPT-5 Mini and Qwen3.5-Flash score almost the same on the Noometry Index (41.8 vs 42.5), so choose on price, context window or the category you care about most.
Which is cheaper, GPT-5 Mini 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-5 Mini lists at $0.25 and $2.
Is GPT-5 Mini or Qwen3.5-Flash better for coding?
GPT-5 Mini scores higher on coding benchmarks: 40.1 versus 34.2 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-Flash does, with 1M tokens against 400K.
How many benchmarks do GPT-5 Mini and Qwen3.5-Flash share?
31 benchmarks have published results for both models. GPT-5 Mini has 60 scored results on Noometry and Qwen3.5-Flash has 32.