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GPT-4.1 vs Qwen3.5 Plus 2026-04-20

Qwen3.5 Plus 2026-04-20 costs 4.4x less per output token than GPT-4.1 ($1.80 vs $8.00 per million). Updated October 22, 2026; 18 benchmarks have a result for both.

GPT-4.1OpenAI
Coding
No data
Overall
36.5
Output $/M
$8.00
4
Qwen3.5 Plus 2026-04-20Qwen
Coding
39.5
Overall
No data
Output $/M
$1.80
14
vs boards won of 18 shared

GPT-4.1 or Qwen3.5 Plus 2026-04-20: which to pick

Only Qwen3.5 Plus 2026-04-20 has a coding score, 39.5 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 $1.35) and a larger context window (1.05M against 1M tokens).

Shared results
18
GPT-4.1 only
74
Qwen3.5 Plus 2026-04-20 only
7
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.

At a glance

Coding index
GPT-4.1 not benchmarked, Qwen3.5 Plus 2026-04-20 39.5 from 2 benchmarks.
Widest category
Math, 47.4 points apart on average over 2 shared benchmarks.
Widest single gap
MMMU Pro (Vals AI): GPT-4.1 72.4% against Qwen3.5 Plus 2026-04-20 22.8%, 49.6 points apart on a 0 to 100 scale.
Output price
GPT-4.1 $8.00 against Qwen3.5 Plus 2026-04-20 $1.80 per million output tokens, 4.4x apart.
Context window
GPT-4.1 takes 1.05M tokens, Qwen3.5 Plus 2026-04-20 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.

Boards won, where both have a result
CategoryGPT-4.1BarsQwen3.5 Plus 2026-04-20Ties
Coding02
Intelligence01
Reasoning13
Math02
Knowledge34
General02
All shared boards414
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
Aider polyglot52.4%Not measuredOnly GPT-4.1 measured
SWE-bench Verified39.6%Not measuredOnly GPT-4.1 measured
SWE-bench Multimodal31.1%Not measuredOnly GPT-4.1 measured
LiveCodeBench (Vals AI)54.7%85.3%Qwen3.5 Plus 2026-04-20
SWE-bench Verified (Vals AI)Not measured71.2%Only Qwen3.5 Plus 2026-04-20 measured
Vibe Code Bench (Vals AI)Not measured15.7%Only Qwen3.5 Plus 2026-04-20 measured
LiveCodeBench (Artificial Analysis run)45.7%Not measuredOnly GPT-4.1 measured
Terminal-Bench Hard (Artificial Analysis run)13.6%Not measuredOnly GPT-4.1 measured
WeirdML39.0%Not measuredOnly GPT-4.1 measured
ALE-Bench558.1608.9Qwen3.5 Plus 2026-04-20
LMArena Text: coding1390Not measuredOnly GPT-4.1 measured
LMArena Text: software and IT1391Not measuredOnly GPT-4.1 measured
SciCode4.6%Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
AA Intelligence Index12.7Not measuredOnly GPT-4.1 measured
Epoch Capabilities Index136.7146.2Qwen3.5 Plus 2026-04-20
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Agentic work benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
τ²-Bench Telecom47.1%Not measuredOnly GPT-4.1 measured
Vending-Bench 2Not measured$1Only Qwen3.5 Plus 2026-04-20 measured
Berkeley Function Calling Leaderboard (BFCL)54.0%Not measuredOnly GPT-4.1 measured
BrowseComp-Plus36.4%Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index13
Reasoning benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
GPQA Diamond65.4%87.4%Qwen3.5 Plus 2026-04-20
MMMU Pro (Vals AI)72.4%22.8%GPT-4.1
GPQA Diamond (Artificial Analysis run)66.6%Not measuredOnly GPT-4.1 measured
Humanity's Last Exam (Artificial Analysis run)4.2%Not measuredOnly GPT-4.1 measured
GPQA Diamond (Epoch AI run)66.9%84.8%Qwen3.5 Plus 2026-04-20
ARC-AGI5.5%Not measuredOnly GPT-4.1 measured
ARC-AGI-20.4%Not measuredOnly GPT-4.1 measured
SimpleBench27.0%Not measuredOnly GPT-4.1 measured
Chess puzzles3.0%8.0%Qwen3.5 Plus 2026-04-20
Mystery game puzzlesNot measured17.0%Only Qwen3.5 Plus 2026-04-20 measured
LMArena Text: hard prompts1384Not measuredOnly GPT-4.1 measured
LMArena Text: expert1364Not measuredOnly GPT-4.1 measured
LMArena Text: hard prompts in English1393Not measuredOnly GPT-4.1 measured
LMArena Vision: diagrams1215Not measuredOnly GPT-4.1 measured
LMArena Vision: homework1248Not measuredOnly GPT-4.1 measured
Kagi LLM Benchmark52.3%Not measuredOnly 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 index02
Math benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
AIME39.6%86.0%Qwen3.5 Plus 2026-04-20
MATH 500 (Vals AI)87.2%Not measuredOnly GPT-4.1 measured
MGSM (Vals AI)87.7%Not measuredOnly GPT-4.1 measured
AA Math Index34.7Not measuredOnly GPT-4.1 measured
AIME 2025 (Artificial Analysis run)34.7%Not measuredOnly GPT-4.1 measured
OTIS Mock AIME38.3%86.7%Qwen3.5 Plus 2026-04-20
FrontierMath, tiers 1 to 36.0%Not measuredOnly GPT-4.1 measured
MATH level 583.0%Not measuredOnly GPT-4.1 measured
LMArena Text: math1369Not measuredOnly GPT-4.1 measured
LMArena Text: mathematical work1360Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index34
Knowledge benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
MMLU Pro80.5%87.2%Qwen3.5 Plus 2026-04-20
LegalBench (Vals AI)83.1%85.1%Qwen3.5 Plus 2026-04-20
CaseLaw (Vals AI)69.9%59.7%GPT-4.1
CorpFin (Vals AI)63.0%65.3%Qwen3.5 Plus 2026-04-20
TaxEval (Vals AI)75.1%Not measuredOnly GPT-4.1 measured
MortgageTax (Vals AI)65.9%60.8%GPT-4.1
MedQA (Vals AI)91.2%95.2%Qwen3.5 Plus 2026-04-20
MMLU Pro (Artificial Analysis run)80.6%Not measuredOnly GPT-4.1 measured
SimpleQA Verified31.1%25.4%GPT-4.1
GeoBench72.0%Not measuredOnly GPT-4.1 measured
LMArena Text: science1382Not measuredOnly GPT-4.1 measured
LMArena Text: medicine and healthcare1379Not measuredOnly GPT-4.1 measured
LMArena Text: legal and government1399Not measuredOnly GPT-4.1 measured
LMArena Text: business and finance1369Not measuredOnly GPT-4.1 measured
FACTS Benchmark Suite45.8%Not measuredOnly 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
Web design benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
Design Arena: websites1038Not measuredOnly GPT-4.1 measured
Design Arena: UI components997Not measuredOnly GPT-4.1 measured
Design Arena: game dev1079Not measuredOnly GPT-4.1 measured
Design Arena: data viz1104Not measuredOnly GPT-4.1 measured
Design Arena: 3D849Not measuredOnly GPT-4.1 measured
Design Arena: all code categories1025Not measuredOnly 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 index02
General benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
SAGE (Vals AI)Not measured30.4%Only Qwen3.5 Plus 2026-04-20 measured
IFBench43.0%Not measuredOnly GPT-4.1 measured
Long context reasoning (AA-LCR)68.3%Not measuredOnly GPT-4.1 measured
DTBench68.3%80.5%Qwen3.5 Plus 2026-04-20
LMCA25.6%36.4%Qwen3.5 Plus 2026-04-20
Fiction.LiveBench63.9%Not measuredOnly GPT-4.1 measured
CL-benchNot measured19.8%Only Qwen3.5 Plus 2026-04-20 measured
CL-bench LifeNot measured12.4%Only Qwen3.5 Plus 2026-04-20 measured
LMArena Text1383Not measuredOnly GPT-4.1 measured
LMArena Text: instruction following1367Not measuredOnly GPT-4.1 measured
LMArena Text: creative writing1363Not measuredOnly GPT-4.1 measured
LMArena Vision1210Not measuredOnly GPT-4.1 measured
LMArena Text: English1392Not measuredOnly GPT-4.1 measured
LMArena Text: non-English1370Not measuredOnly GPT-4.1 measured
LMArena Text: Chinese1382Not measuredOnly GPT-4.1 measured
LMArena Text: German1381Not measuredOnly GPT-4.1 measured
LMArena Text: Japanese1319Not measuredOnly GPT-4.1 measured
LMArena Text: Korean1338Not measuredOnly GPT-4.1 measured
LMArena Text: French1382Not measuredOnly GPT-4.1 measured
LMArena Text: Spanish1376Not measuredOnly GPT-4.1 measured
LMArena Text: Russian1377Not measuredOnly GPT-4.1 measured
LMArena Text: Polish1389Not measuredOnly GPT-4.1 measured
LMArena Text: multi-turn1397Not measuredOnly GPT-4.1 measured
LMArena Text: longer queries1385Not measuredOnly GPT-4.1 measured
LMArena Text: writing and language1371Not measuredOnly GPT-4.1 measured
LMArena Text: entertainment and media1365Not measuredOnly GPT-4.1 measured
LMArena Vision: English1209Not measuredOnly GPT-4.1 measured
LMArena Vision: Chinese1226Not measuredOnly GPT-4.1 measured
LMArena Vision: reading text1218Not measuredOnly GPT-4.1 measured
LMArena Vision: humor1196Not measuredOnly GPT-4.1 measured
LMArena Vision: captioning1207Not measuredOnly GPT-4.1 measured
LMArena Vision: recognizing things1208Not measuredOnly GPT-4.1 measured
LMArena Vision: creative writing1210Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4.1 and Qwen3.5 Plus 2026-04-20
BenchmarkGPT-4.1Qwen3.5 Plus 2026-04-20Leader
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

GPT-4.1$0.0060Fits in one request
Qwen3.5 Plus 2026-04-20$0.0012Fits in one request

Qwen3.5 Plus 2026-04-20 costs less

Repository review

50K input + 3K output tokens

GPT-4.1$0.124Fits in one request
Qwen3.5 Plus 2026-04-20$0.020Fits in one request

Qwen3.5 Plus 2026-04-20 costs less

Agent loop

200K input + 10K output tokens

GPT-4.1$0.480Fits in one request
Qwen3.5 Plus 2026-04-20$0.078Fits in one request

Qwen3.5 Plus 2026-04-20 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.

Specifications of GPT-4.1 and Qwen3.5 Plus 2026-04-20
SpecificationOpenAI GPT-4.1Qwen3.5 Plus 2026-04-20
MakerOpenAIQwen
WeightsNo public weights listedNo public weights listed
ReleasedApril 14, 2025April 27, 2026
Context window1.05M1M
Input $/Mtok$2.00$0.30
Output $/Mtok$8.00$1.80
Coding indexNot benchmarked39.5
Independent measurements behind it02
Cost per solved task$0.37$1.35
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 better than Qwen3.5 Plus 2026-04-20 for coding?

Only Qwen3.5 Plus 2026-04-20 has a coding score, 39.5 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 $1.35) and a larger context window (1.05M against 1M tokens).

Which is cheaper, GPT-4.1 or Qwen3.5 Plus 2026-04-20?

GPT-4.1 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3.5 Plus 2026-04-20 costs $0.30 and $1.80. Per solved SWE-bench Verified task, GPT-4.1 costs $0.37 and Qwen3.5 Plus 2026-04-20 $1.35.

Which has the bigger context window, GPT-4.1 or Qwen3.5 Plus 2026-04-20?

GPT-4.1, with 1.05M tokens against 1M.

How many benchmarks is the GPT-4.1 vs Qwen3.5 Plus 2026-04-20 comparison based on?

18 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 Plus 2026-04-20's on 2. Where only one model has a result, the page says so instead of naming a winner.

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