Model comparison
GPT-4.1 mini vs Qwen3.6 Flash
Qwen3.6 Flash is the stronger model overall, scoring 38.4 to 33.5 on the Noometry Index.
Last verified . 10 shared benchmarks.
Summary
- They share 10 benchmarks with published results for both. GPT-4.1 mini scores higher in 0 categories and Qwen3.6 Flash in 3 categories; 3 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where Qwen3.6 Flash leads 28.4 to 10.9.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 44.7% for GPT-4.1 mini and 84.4% for Qwen3.6 Flash.
- Qwen3.6 Flash is cheaper at $0.19 / $1.13 per million input/output tokens, against $0.40 / $1.60 for GPT-4.1 mini.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 1M.
Side by side
| GPT-4.1 mini | Qwen3.6 Flash | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.5 | 38.4 |
| Released | 2025-04-14 | 2026-04-27 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 1M |
| Max output | 33K | 66K |
| Input $ / M tokens | $0.40 | $0.19 |
| Output $ / M tokens | $1.60 | $1.13 |
| Results tracked | 47 | 13 |
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Category by category
Coding Not comparable
GPT-4.1 mini: 30.4 (#286), Qwen3.6 Flash: —
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| SWE-bench Verified (bash only) | 23.9% | — |
| Aider Polyglot | 32.4% | — |
| SciCode | 40.4% | — |
| WeirdML | 37.6% | — |
| BigCodeBench Instruct | 48.9% | — |
| LMArena Coding | 1367 | — |
| CadEval | 16% | — |
| ALE-Bench | — | 326.4 |
Agentic & Tool Use Not comparable
GPT-4.1 mini: 33.0 (#52), Qwen3.6 Flash: —
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| Berkeley Function Calling Leaderboard | 50.5% | — |
Reasoning Qwen3.6 Flash leads
GPT-4.1 mini: 10.9 (#335), Qwen3.6 Flash: 28.4 (#90)
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| Chess Puzzles | 7% | 20% |
| Mystery Game Puzzles | 7% | 18% |
| DTBench | 68.8% | 77.1% |
| LMCA | 21.1% | 31% |
| Epoch Capabilities Index | 135.01 | 143.26 |
| ARC-AGI-2 | 0% | — |
| SimpleBench | — | 35.2% |
| Kagi LLM Benchmark | 48.6% | — |
| ARC-AGI-1 | 3.5% | — |
| CritPt | 0% | — |
| LMArena Hard Prompts | 1349 | — |
Math Qwen3.6 Flash leads
GPT-4.1 mini: 23.1 (#264), Qwen3.6 Flash: 38.0 (#88)
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| FrontierMath (Tiers 1-3) | 6.7% | 22.5% |
| OTIS Mock AIME 2024-2025 | 44.7% | 84.4% |
| FrontierMath (Feb 2025 set) | 4.5% | 10.3% |
| Omni-MATH | 49.1% | — |
| LMArena Math | 1343 | — |
| MATH Level 5 | 87.3% | — |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3.6 Flash leads
GPT-4.1 mini: 34.7 (#183), Qwen3.6 Flash: 42.2 (#90)
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| GPQA Diamond | 65.8% | 83.3% |
| SimpleQA Verified | 12.7% | 15.9% |
| MMLU-Pro | 78.3% | — |
| GPQA (HELM) | 61.4% | — |
| LMArena Expert | 1338 | — |
Multimodal Not comparable
GPT-4.1 mini: 35.2 (#77), Qwen3.6 Flash: —
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| LMArena Vision | 1181 | — |
Multilingual Not comparable
GPT-4.1 mini: 45.9 (#161), Qwen3.6 Flash: —
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| LMArena Non-English | 1318 | — |
| LMArena Chinese | 1329 | — |
| LMArena French | 1357 | — |
| LMArena German | 1349 | — |
| LMArena Japanese | 1291 | — |
| LMArena Korean | 1298 | — |
| LMArena Russian | 1324 | — |
| LMArena Spanish | 1319 | — |
Instruction Following Not comparable
GPT-4.1 mini: 73.9 (#113), Qwen3.6 Flash: —
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| IFEval | 90.4% | — |
| LMArena Instruction Following | 1332 | — |
Long Context Not comparable
GPT-4.1 mini: 31.8 (#270), Qwen3.6 Flash: —
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| Fiction.LiveBench | 44.4% | — |
| LMArena Longer Query | 1344 | — |
Writing & Preference Not comparable
GPT-4.1 mini: 48.8 (#193), Qwen3.6 Flash: —
| Benchmark | GPT-4.1 mini | Qwen3.6 Flash |
|---|---|---|
| LMArena Text | 1341 | — |
| LMArena Creative Writing | 1300 | — |
| EQ-Bench Creative Writing | 1147 | — |
| WildBench | 83.8% | — |
| LMArena Multi-Turn | 1354 | — |
Frequently asked questions
Is GPT-4.1 mini better than Qwen3.6 Flash?
Qwen3.6 Flash is the stronger model overall, scoring 38.4 to 33.5 on the Noometry Index.
Which is cheaper, GPT-4.1 mini or Qwen3.6 Flash?
Qwen3.6 Flash is cheaper. It lists at $0.19 per million input tokens and $1.13 per million output tokens; GPT-4.1 mini lists at $0.40 and $1.60.
Which has the bigger context window?
GPT-4.1 mini does, with 1.05M tokens against 1M.
How many benchmarks do GPT-4.1 mini and Qwen3.6 Flash share?
10 benchmarks have published results for both models. GPT-4.1 mini has 47 scored results on Noometry and Qwen3.6 Flash has 13.