GPT-4.1 vs Qwen3 VL 235B A22B Instruct
GPT-4.1 leads Qwen3 VL 235B A22B Instruct on the Artificial Analysis Intelligence Index, 12.7 to 9.9, and Qwen3 VL 235B A22B Instruct costs 4.2x less per output token ($1.90 vs $8.00 per million). Updated October 22, 2026; 49 benchmarks have a result for both.
GPT-4.1 or Qwen3 VL 235B A22B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-4.1 for a larger context window (1.05M against 262K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
- Shared results
- 49
- GPT-4.1 only
- 43
- Qwen3 VL 235B A22B Instruct only
- 0
- Like-for-like categories
- 7 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GPT-4.1
GPT-4.1 leads, 12.7 against 9.9.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-4.1 has a GPQA Diamond result, 65.4%.
Directional only
Math
AIME
No call
Only GPT-4.1 has a AIME result, 39.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4.1 has a MMLU Pro result, 80.5%.
Directional only
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 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3 VL 235B A22B Instruct
GPT-4.1 $0.0060, Qwen3 VL 235B A22B Instruct $0.0012 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-4.1 has the number, $0.37.
Directional only
At a glance
- Coding index
- GPT-4.1 not benchmarked, Qwen3 VL 235B A22B Instruct not benchmarked.
- Widest category
- Math, 31.6 points apart on average over 4 shared benchmarks.
- Widest single gap
- AIME 2025 (Artificial Analysis run): GPT-4.1 34.7% against Qwen3 VL 235B A22B Instruct 88.3%, 53.7 points apart on a 0 to 100 scale.
- Output price
- GPT-4.1 $8.00 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 4.2x apart.
- Context window
- GPT-4.1 takes 1.05M tokens, Qwen3 VL 235B A22B Instruct 262K.
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 VL 235B A22B Instruct | Ties |
|---|---|---|---|---|
| Coding | 1 | 3 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 4 | ||
| Knowledge | 0 | 5 | ||
| General | 1 | 26 | ||
| All shared boards | 3 | 46 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index13
| Benchmark | GPT-4.1 | Qwen3 VL 235B A22B Instruct | 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 |
| LiveCodeBench (Vals AI) | 54.7% | Not measured | Only GPT-4.1 measured |
| LiveCodeBench (Artificial Analysis run) | 45.7% | 64.6% | Qwen3 VL 235B A22B Instruct |
| Terminal-Bench Hard (Artificial Analysis run) | 13.6% | 11.4% | GPT-4.1 |
| WeirdML | 39.0% | Not measured | Only GPT-4.1 measured |
| ALE-Bench | 558.1 | Not measured | Only GPT-4.1 measured |
| LMArena Text: coding | 1390 | 1438 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: software and IT | 1391 | 1442 | Qwen3 VL 235B A22B Instruct |
| SciCode | 4.6% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-4.1 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.7 | 9.9 | GPT-4.1 |
| Epoch Capabilities Index | 136.7 | Not measured | Only GPT-4.1 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4.1 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| τ²-Bench Telecom | 47.1% | 54.1% | Qwen3 VL 235B A22B Instruct |
| 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 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| GPQA Diamond | 65.4% | Not measured | Only GPT-4.1 measured |
| MMMU Pro (Vals AI) | 72.4% | Not measured | Only GPT-4.1 measured |
| GPQA Diamond (Artificial Analysis run) | 66.6% | 77.2% | Qwen3 VL 235B A22B Instruct |
| Humanity's Last Exam (Artificial Analysis run) | 4.2% | 11.9% | Qwen3 VL 235B A22B Instruct |
| GPQA Diamond (Epoch AI run) | 66.9% | Not measured | Only GPT-4.1 measured |
| 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% | Not measured | Only GPT-4.1 measured |
| LMArena Text: hard prompts | 1384 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: expert | 1364 | 1442 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: hard prompts in English | 1393 | 1439 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: diagrams | 1215 | 1255 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: homework | 1248 | 1277 | Qwen3 VL 235B A22B Instruct |
| Kagi LLM Benchmark | 52.3% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GPT-4.1 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| AIME | 39.6% | Not measured | Only GPT-4.1 measured |
| 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 | 88.3 | Qwen3 VL 235B A22B Instruct |
| AIME 2025 (Artificial Analysis run) | 34.7% | 88.3% | Qwen3 VL 235B A22B Instruct |
| OTIS Mock AIME | 38.3% | Not measured | Only GPT-4.1 measured |
| FrontierMath, tiers 1 to 3 | 6.0% | Not measured | Only GPT-4.1 measured |
| MATH level 5 | 83.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: math | 1369 | 1425 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: mathematical work | 1360 | 1419 | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 3 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-4.1 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| MMLU Pro | 80.5% | Not measured | Only GPT-4.1 measured |
| LegalBench (Vals AI) | 83.1% | Not measured | Only GPT-4.1 measured |
| CaseLaw (Vals AI) | 69.9% | Not measured | Only GPT-4.1 measured |
| CorpFin (Vals AI) | 63.0% | Not measured | Only GPT-4.1 measured |
| TaxEval (Vals AI) | 75.1% | Not measured | Only GPT-4.1 measured |
| MortgageTax (Vals AI) | 65.9% | Not measured | Only GPT-4.1 measured |
| MedQA (Vals AI) | 91.2% | Not measured | Only GPT-4.1 measured |
| MMLU Pro (Artificial Analysis run) | 80.6% | 83.6% | Qwen3 VL 235B A22B Instruct |
| SimpleQA Verified | 31.1% | Not measured | Only GPT-4.1 measured |
| GeoBench | 72.0% | Not measured | Only GPT-4.1 measured |
| LMArena Text: science | 1382 | 1444 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: medicine and healthcare | 1379 | 1431 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: legal and government | 1399 | 1438 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: business and finance | 1369 | 1433 | Qwen3 VL 235B A22B Instruct |
| FACTS Benchmark Suite | 45.8% | Not measured | Only GPT-4.1 measured |
| Neither model has a result on the other 3 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4.1 | Qwen3 VL 235B A22B Instruct | 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 index126
| Benchmark | GPT-4.1 | Qwen3 VL 235B A22B Instruct | Leader |
|---|---|---|---|
| IFBench | 43.0% | 56.5% | Qwen3 VL 235B A22B Instruct |
| Long context reasoning (AA-LCR) | 68.3% | 63.7% | GPT-4.1 |
| DTBench | 68.3% | Not measured | Only GPT-4.1 measured |
| LMCA | 25.6% | Not measured | Only GPT-4.1 measured |
| Fiction.LiveBench | 63.9% | Not measured | Only GPT-4.1 measured |
| LMArena Text | 1383 | 1420 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: instruction following | 1367 | 1406 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: creative writing | 1363 | 1366 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision | 1210 | 1247 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: English | 1392 | 1429 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: non-English | 1370 | 1405 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Chinese | 1382 | 1463 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: German | 1381 | 1424 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Japanese | 1319 | 1385 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Korean | 1338 | 1394 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: French | 1382 | 1452 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Spanish | 1376 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Russian | 1377 | 1408 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: Polish | 1389 | 1419 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: multi-turn | 1397 | 1428 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: longer queries | 1385 | 1419 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: writing and language | 1371 | 1392 | Qwen3 VL 235B A22B Instruct |
| LMArena Text: entertainment and media | 1365 | 1368 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: English | 1209 | 1256 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: Chinese | 1226 | 1283 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: reading text | 1218 | 1253 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: humor | 1196 | 1209 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: captioning | 1207 | 1281 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: recognizing things | 1208 | 1249 | Qwen3 VL 235B A22B Instruct |
| LMArena Vision: creative writing | 1210 | 1220 | Qwen3 VL 235B A22B Instruct |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4.1 | Qwen3 VL 235B A22B Instruct | 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 VL 235B A22B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen3 VL 235B A22B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 14, 2025 | September 23, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $2.00 | $0.21 |
| Output $/Mtok | $8.00 | $1.90 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $0.37 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4.1 better than Qwen3 VL 235B A22B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4.1 for a larger context window (1.05M against 262K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.
Which is cheaper, GPT-4.1 or Qwen3 VL 235B A22B Instruct?
GPT-4.1 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for GPT-4.1 only.
Which has the bigger context window, GPT-4.1 or Qwen3 VL 235B A22B Instruct?
GPT-4.1, with 1.05M tokens against 262K.
How many benchmarks is the GPT-4.1 vs Qwen3 VL 235B A22B Instruct comparison based on?
49 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 VL 235B A22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.