GPT-4.1 vs Qwen3.5-Flash
Qwen3.5-Flash costs 30.8x less per output token than GPT-4.1 ($0.26 vs $8.00 per million). Updated October 22, 2026; 47 benchmarks have a result for both.
GPT-4.1 or Qwen3.5-Flash: which to pick
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-4.1 has none, so the two cannot be ranked on coding.
Pick GPT-4.1 for a lower cost per solved task ($0.37 against $0.40) and a larger context window (1.05M against 1M tokens).
- Shared results
- 47
- GPT-4.1 only
- 45
- Qwen3.5-Flash only
- 8
- Like-for-like categories
- 6 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only Qwen3.5-Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
No call
Only GPT-4.1 has a AA Intelligence Index result, 12.7.
Directional only
Reasoning
GPQA Diamond
Qwen3.5-Flash
Qwen3.5-Flash leads, 82.8% against 65.4%.
Same board, both measured
Math
AIME
Qwen3.5-Flash
Qwen3.5-Flash leads, 92.5% against 39.6%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.5-Flash
Qwen3.5-Flash leads, 84.1% against 80.5%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GPT-4.1 has a Design Arena: websites result, 1038.
Directional only
Long documents
Listed context window
GPT-4.1
GPT-4.1 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.5-Flash
GPT-4.1 $0.0060, Qwen3.5-Flash $0.0002 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-4.1
GPT-4.1 $0.37, Qwen3.5-Flash $0.40.
Published attempt cost and pass rate
At a glance
- Coding index
- GPT-4.1 not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
- Widest category
- Math, 25.3 points apart on average over 5 shared benchmarks.
- Widest single gap
- AIME: GPT-4.1 39.6% against Qwen3.5-Flash 92.5%, 52.9 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 $8.00 against Qwen3.5-Flash $0.26 per million output tokens, 30.8x apart.
- Context window
- GPT-4.1 takes 1.05M tokens, Qwen3.5-Flash 1M.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | GPT-4.1 | Bars | Qwen3.5-Flash | Ties |
|---|---|---|---|---|
| Coding | 1 | 3 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 5 | ||
| Knowledge | 4 | 7 | ||
| General | 5 | 14 | ||
| All shared boards | 10 | 37 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index13
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Aider polyglot | 52.4% | Not measured | Only GPT-4.1 measured |
| SWE-bench Verified | 39.6% | Not measured | Only GPT-4.1 measured |
| SWE-bench Multimodal | 31.1% | Not measured | Only GPT-4.1 measured |
| LMArena WebDev | Not measured | 1244 | Only Qwen3.5-Flash measured |
| LiveCodeBench (Vals AI) | 54.7% | 83.3% | Qwen3.5-Flash |
| SWE-bench Verified (Vals AI) | Not measured | 64.4% | Only Qwen3.5-Flash measured |
| LiveCodeBench (Artificial Analysis run) | 45.7% | Not measured | Only GPT-4.1 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 13.6% | Not measured | Only GPT-4.1 measured |
| WeirdML | 39.0% | Not measured | Only GPT-4.1 measured |
| ALE-Bench | 558.1 | 265.9 | GPT-4.1 |
| LMArena Text: coding | 1390 | 1412 | Qwen3.5-Flash |
| LMArena Text: software and IT | 1391 | 1416 | Qwen3.5-Flash |
| LMArena WebDev: React | Not measured | 1229 | Only Qwen3.5-Flash measured |
| LMArena WebDev: HTML | Not measured | 1291 | Only Qwen3.5-Flash measured |
| SciCode | 4.6% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.7 | Not measured | Only GPT-4.1 measured |
| Epoch Capabilities Index | 136.7 | 143.5 | Qwen3.5-Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 47.1% | Not measured | Only GPT-4.1 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 54.0% | Not measured | Only GPT-4.1 measured |
| BrowseComp-Plus | 36.4% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| GPQA Diamond | 65.4% | 82.8% | Qwen3.5-Flash |
| MMMU Pro (Vals AI) | 72.4% | 81.9% | Qwen3.5-Flash |
| GPQA Diamond (Artificial Analysis run) | 66.6% | Not measured | Only GPT-4.1 measured |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | Not measured | Only GPT-4.1 measured |
| GPQA Diamond (Epoch AI run) | 66.9% | 82.3% | Qwen3.5-Flash |
| ARC-AGI | 5.5% | Not measured | Only GPT-4.1 measured |
| ARC-AGI-2 | 0.4% | Not measured | Only GPT-4.1 measured |
| SimpleBench | 27.0% | Not measured | Only GPT-4.1 measured |
| Chess puzzles | 3.0% | 14.0% | Qwen3.5-Flash |
| Mystery game puzzles | Not measured | 20.0% | Only Qwen3.5-Flash measured |
| LMArena Text: hard prompts | 1384 | 1403 | Qwen3.5-Flash |
| LMArena Text: expert | 1364 | 1407 | Qwen3.5-Flash |
| LMArena Text: hard prompts in English | 1393 | 1409 | Qwen3.5-Flash |
| LMArena Vision: diagrams | 1215 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: homework | 1248 | Not measured | Only GPT-4.1 measured |
| Kagi LLM Benchmark | 52.3% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| AIME | 39.6% | 92.5% | Qwen3.5-Flash |
| MATH 500 (Vals AI) | 87.2% | Not measured | Only GPT-4.1 measured |
| MGSM (Vals AI) | 87.7% | Not measured | Only GPT-4.1 measured |
| AA Math Index | 34.7 | Not measured | Only GPT-4.1 measured |
| AIME 2025 (Artificial Analysis run) | 34.7% | Not measured | Only GPT-4.1 measured |
| OTIS Mock AIME | 38.3% | 84.4% | Qwen3.5-Flash |
| FrontierMath, tiers 1 to 3 | 6.0% | 18.2% | Qwen3.5-Flash |
| MATH level 5 | 83.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: math | 1369 | 1407 | Qwen3.5-Flash |
| LMArena Text: mathematical work | 1360 | 1413 | Qwen3.5-Flash |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index47
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| MMLU Pro | 80.5% | 84.1% | Qwen3.5-Flash |
| LegalBench (Vals AI) | 83.1% | 84.3% | Qwen3.5-Flash |
| CaseLaw (Vals AI) | 69.9% | 56.0% | GPT-4.1 |
| CorpFin (Vals AI) | 63.0% | 63.6% | Qwen3.5-Flash |
| TaxEval (Vals AI) | 75.1% | 72.2% | GPT-4.1 |
| MortgageTax (Vals AI) | 65.9% | 67.4% | Qwen3.5-Flash |
| MedQA (Vals AI) | 91.2% | Not measured | Only GPT-4.1 measured |
| MedCode (Vals AI) | Not measured | 33.0% | Only Qwen3.5-Flash measured |
| MMLU Pro (Artificial Analysis run) | 80.6% | Not measured | Only GPT-4.1 measured |
| SimpleQA Verified | 31.1% | 20.3% | GPT-4.1 |
| GeoBench | 72.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: science | 1382 | 1415 | Qwen3.5-Flash |
| LMArena Text: medicine and healthcare | 1379 | 1407 | Qwen3.5-Flash |
| LMArena Text: legal and government | 1399 | 1398 | GPT-4.1 |
| LMArena Text: business and finance | 1369 | 1394 | Qwen3.5-Flash |
| FACTS Benchmark Suite | 45.8% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 2 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1038 | Not measured | Only GPT-4.1 measured |
| Design Arena: UI components | 997 | Not measured | Only GPT-4.1 measured |
| Design Arena: game dev | 1079 | Not measured | Only GPT-4.1 measured |
| Design Arena: data viz | 1104 | Not measured | Only GPT-4.1 measured |
| Design Arena: 3D | 849 | Not measured | Only GPT-4.1 measured |
| Design Arena: all code categories | 1025 | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 3 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index514
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 42.5% | Only Qwen3.5-Flash measured |
| MedScribe (Vals AI) | Not measured | 70.6% | Only Qwen3.5-Flash measured |
| IFBench | 43.0% | Not measured | Only GPT-4.1 measured |
| Long context reasoning (AA-LCR) | 68.3% | Not measured | Only GPT-4.1 measured |
| DTBench | 68.3% | 82.9% | Qwen3.5-Flash |
| LMCA | 25.6% | 29.1% | Qwen3.5-Flash |
| Fiction.LiveBench | 63.9% | Not measured | Only GPT-4.1 measured |
| LMArena Text | 1383 | 1397 | Qwen3.5-Flash |
| LMArena Text: instruction following | 1367 | 1374 | Qwen3.5-Flash |
| LMArena Text: creative writing | 1363 | 1343 | GPT-4.1 |
| LMArena Vision | 1210 | Not measured | Only GPT-4.1 measured |
| LMArena Text: English | 1392 | 1404 | Qwen3.5-Flash |
| LMArena Text: non-English | 1370 | 1385 | Qwen3.5-Flash |
| LMArena Text: Chinese | 1382 | 1445 | Qwen3.5-Flash |
| LMArena Text: German | 1381 | 1390 | Qwen3.5-Flash |
| LMArena Text: Japanese | 1319 | 1369 | Qwen3.5-Flash |
| LMArena Text: Korean | 1338 | 1344 | Qwen3.5-Flash |
| LMArena Text: French | 1382 | 1413 | Qwen3.5-Flash |
| LMArena Text: Spanish | 1376 | 1400 | Qwen3.5-Flash |
| LMArena Text: Russian | 1377 | 1380 | Qwen3.5-Flash |
| LMArena Text: Polish | 1389 | 1376 | GPT-4.1 |
| LMArena Text: multi-turn | 1397 | 1393 | GPT-4.1 |
| LMArena Text: longer queries | 1385 | 1392 | Qwen3.5-Flash |
| LMArena Text: writing and language | 1371 | 1369 | GPT-4.1 |
| LMArena Text: entertainment and media | 1365 | 1347 | GPT-4.1 |
| LMArena Vision: English | 1209 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: Chinese | 1226 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: reading text | 1218 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: humor | 1196 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: captioning | 1207 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: recognizing things | 1208 | Not measured | Only GPT-4.1 measured |
| LMArena Vision: creative writing | 1210 | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 | Qwen3.5-Flash | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
Qwen3.5-Flash costs less
Repository review
50K input + 3K output tokens
Qwen3.5-Flash costs less
Agent loop
200K input + 10K output tokens
Qwen3.5-Flash costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | OpenAI GPT-4.1 | Qwen3.5-Flash |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | April 14, 2025 | February 25, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $2.00 | $0.065 |
| Output $/Mtok | $8.00 | $0.26 |
| Coding index | Not benchmarked | 41.1 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | $0.37 | $0.40 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 better than Qwen3.5-Flash for coding?
Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-4.1 has none, so the two cannot be ranked on coding. Pick GPT-4.1 for a lower cost per solved task ($0.37 against $0.40) and a larger context window (1.05M against 1M tokens).
Which is cheaper, GPT-4.1 or Qwen3.5-Flash?
GPT-4.1 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Per solved SWE-bench Verified task, GPT-4.1 costs $0.37 and Qwen3.5-Flash $0.40.
Which has the bigger context window, GPT-4.1 or Qwen3.5-Flash?
GPT-4.1, with 1.05M tokens against 1M.
How many benchmarks is the GPT-4.1 vs Qwen3.5-Flash comparison based on?
47 benchmarks have a result for both models as of October 22, 2026. GPT-4.1's coding index rests on 0 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.