Model comparison
GPT-4.1 mini vs Qwen3 235B-A22B
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 33.6 on the Noometry Index. GPT-4.1 mini costs 1.7× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Last verified . 42 shared benchmarks.
Alibaba (Qwen)Summary
- They share 42 benchmarks with published results for both. GPT-4.1 mini scores higher in 1 category and Qwen3 235B-A22B in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3 235B-A22B leads 50.4 to 24.1.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 44.7% for GPT-4.1 mini and 86.7% for Qwen3 235B-A22B.
- GPT-4.1 mini is cheaper at $0.40 / $1.60 per million input/output tokens, against $0.70 / $2.80 for Qwen3 235B-A22B.
- GPT-4.1 mini accepts more context: 1.05M tokens versus 131K.
- Qwen3 235B-A22B has downloadable open weights; the other is API-only.
Side by side
| GPT-4.1 mini | Qwen3 235B-A22B | |
|---|---|---|
| Provider | OpenAI | Alibaba (Qwen) |
| Noometry Index | 33.6 | 43.5 |
| Released | 2025-04-14 | 2025-04 |
| Weights | Proprietary | Open |
| Context window | 1.05M | 131K |
| Max output | 33K | 16K |
| Input $ / M tokens | $0.40 | $0.70 |
| Output $ / M tokens | $1.60 | $2.80 |
| Results tracked | 47 | 49 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Qwen3 235B-A22B leads
GPT-4.1 mini: 30.6 (#293), Qwen3 235B-A22B: 44.3 (#75)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| Aider Polyglot | 32.4% | 59.6% |
| SciCode | 40.4% | 42.4% |
| WeirdML | 37.6% | 41% |
| LMArena Coding | 1367 | 1445 |
| SWE-bench Verified (bash only) | 23.9% | — |
| BigCodeBench Instruct | 48.9% | — |
| CadEval | 16% | — |
Agentic & Tool Use Too close to call
GPT-4.1 mini: 33.3 (#55), Qwen3 235B-A22B: 33.9 (#51)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| Berkeley Function Calling Leaderboard | 50.5% | 52.1% |
| Vending-Bench 2 | — | -11.34 |
Reasoning Qwen3 235B-A22B leads
GPT-4.1 mini: 10.8 (#340), Qwen3 235B-A22B: 15.7 (#311)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| ARC-AGI-2 | 0% | 1.3% |
| Kagi LLM Benchmark | 48.6% | 69.4% |
| ARC-AGI-1 | 3.5% | 11% |
| CritPt | 0% | 0% |
| Chess Puzzles | 7% | 12% |
| LMArena Hard Prompts | 1349 | 1433 |
| Mystery Game Puzzles | 7% | 9% |
| DTBench | 68.8% | 80.3% |
| LMCA | 21.1% | 29.3% |
| Epoch Capabilities Index | 135.01 | 143.85 |
| SimpleBench | — | 31% |
| ForecastBench | — | 59.7 |
Math Qwen3 235B-A22B leads
GPT-4.1 mini: 24.1 (#270), Qwen3 235B-A22B: 50.4 (#57)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 44.7% | 86.7% |
| Omni-MATH | 49.1% | 71.8% |
| LMArena Math | 1343 | 1432 |
| MATH Level 5 | 87.3% | 68.9% |
| FrontierMath (Feb 2025 set) | 4.5% | 8.5% |
| FrontierMath (Tiers 1-3) | 6.7% | — |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge Qwen3 235B-A22B leads
GPT-4.1 mini: 34.7 (#194), Qwen3 235B-A22B: 49.6 (#73)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| GPQA Diamond | 65.8% | 80.1% |
| SimpleQA Verified | 12.7% | 40.4% |
| MMLU-Pro | 78.3% | 84.4% |
| GPQA (HELM) | 61.4% | 72.7% |
| LMArena Expert | 1338 | 1463 |
| Confabulations | — | 15.6% |
| Vectara Hallucination Rate | — | 9.3% |
Multimodal Not comparable
GPT-4.1 mini: 35.8 (#82), Qwen3 235B-A22B: —
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| LMArena Vision | 1181 | — |
Multilingual Qwen3 235B-A22B leads
GPT-4.1 mini: 45.7 (#166), Qwen3 235B-A22B: 52.3 (#89)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| LMArena Non-English | 1318 | 1409 |
| LMArena Chinese | 1329 | 1481 |
| LMArena French | 1358 | 1445 |
| LMArena German | 1351 | 1433 |
| LMArena Japanese | 1290 | 1399 |
| LMArena Korean | 1298 | 1391 |
| LMArena Russian | 1324 | 1411 |
| LMArena Spanish | 1319 | 1430 |
Instruction Following GPT-4.1 mini leads
GPT-4.1 mini: 73.7 (#118), Qwen3 235B-A22B: 72.6 (#136)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| IFEval | 90.4% | 83.5% |
| LMArena Instruction Following | 1333 | 1408 |
Long Context Qwen3 235B-A22B leads
GPT-4.1 mini: 31.8 (#275), Qwen3 235B-A22B: 46.1 (#26)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| Fiction.LiveBench | 44.4% | 75% |
| LMArena Longer Query | 1344 | 1426 |
Writing & Preference Qwen3 235B-A22B leads
GPT-4.1 mini: 48.6 (#199), Qwen3 235B-A22B: 59.6 (#108)
| Benchmark | GPT-4.1 mini | Qwen3 235B-A22B |
|---|---|---|
| LMArena Text | 1340 | 1419 |
| LMArena Creative Writing | 1300 | 1384 |
| EQ-Bench Creative Writing | 1147 | 1366 |
| WildBench | 83.8% | 86.6% |
| LMArena Multi-Turn | 1354 | 1432 |
| Short-Story Creative Writing | — | 83% |
Frequently asked questions
Is GPT-4.1 mini better than Qwen3 235B-A22B?
Qwen3 235B-A22B is the stronger model overall, scoring 43.5 to 33.6 on the Noometry Index. GPT-4.1 mini costs 1.7× less per token, which makes it the better buy when Qwen3 235B-A22B's lead doesn't matter for your workload.
Which is cheaper, GPT-4.1 mini or Qwen3 235B-A22B?
GPT-4.1 mini is cheaper. It lists at $0.40 per million input tokens and $1.60 per million output tokens; Qwen3 235B-A22B lists at $0.70 and $2.80.
Is GPT-4.1 mini or Qwen3 235B-A22B better for coding?
Qwen3 235B-A22B scores higher on coding benchmarks: 44.3 versus 30.6 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 mini does, with 1.05M tokens against 131K.
How many benchmarks do GPT-4.1 mini and Qwen3 235B-A22B share?
42 benchmarks have published results for both models. GPT-4.1 mini has 47 scored results on Noometry and Qwen3 235B-A22B has 49.