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GPT-4.1 vs Qwen3.5-27B

Qwen3.5-27B leads GPT-4.1 on the Artificial Analysis Intelligence Index, 22.9 to 12.7, and Qwen3.5-27B costs 5.1x less per output token ($1.56 vs $8.00 per million). Updated October 22, 2026; 49 benchmarks have a result for both.

Replace GPT-4.1 Replace Qwen3.5-27B
GPT-4.1OpenAI
Coding
No data
Overall
36.5
Output $/M
$8.00
5
Qwen3.5-27BQwen
Coding
No data
Overall
No data
Output $/M
$1.56
44
vs boards won of 49 shared

GPT-4.1 or Qwen3.5-27B: 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 Terminal-Bench Hard (Artificial Analysis run) or WeirdML, where it leads.

Model AGPT-4.1OpenAINot benchmarkedNo coding benchmark$0.37 per solved taskModel BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
49
GPT-4.1 only
43
Qwen3.5-27B only
4
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.

At a glance

Coding index
GPT-4.1 not benchmarked, Qwen3.5-27B not benchmarked.
Widest category
Agentic work, 46.8 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GPT-4.1 47.1% against Qwen3.5-27B 93.9%, 46.8 points apart on a 0 to 100 scale.
Output price
GPT-4.1 $8.00 against Qwen3.5-27B $1.56 per million output tokens, 5.1x apart.
Context window
GPT-4.1 takes 1.05M tokens, Qwen3.5-27B 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.

Boards won, where both have a result
CategoryGPT-4.1BarsQwen3.5-27BTies
Coding14
Intelligence01
Agentic work01
Reasoning07
Math02
Knowledge04
General425
All shared boards544
CodingLike-for-likeSix of these boards feed the Vibe Coding Index14
Coding benchmarks for GPT-4.1 and Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
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
LMArena WebDevNot measured1358Only Qwen3.5-27B measured
LiveCodeBench (Vals AI)54.7%Not measuredOnly GPT-4.1 measured
LiveCodeBench (Artificial Analysis run)45.7%Not measuredOnly GPT-4.1 measured
Terminal-Bench Hard (Artificial Analysis run)13.6%32.6%Qwen3.5-27B
WeirdML39.0%39.5%Qwen3.5-27B
ALE-Bench558.1349.4GPT-4.1
LMArena Text: coding13901427Qwen3.5-27B
LMArena Text: software and IT13911431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1386Only Qwen3.5-27B measured
SciCode4.6%Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4.1 and Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
AA Intelligence Index12.722.9Qwen3.5-27B
Epoch Capabilities Index136.7Not measuredOnly GPT-4.1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4.1 and Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
τ²-Bench Telecom47.1%93.9%Qwen3.5-27B
Vending-Bench 2Not measured$202Only Qwen3.5-27B 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 index07
Reasoning benchmarks for GPT-4.1 and Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
GPQA Diamond65.4%Not measuredOnly GPT-4.1 measured
MMMU Pro (Vals AI)72.4%Not measuredOnly GPT-4.1 measured
GPQA Diamond (Artificial Analysis run)66.6%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)4.2%23.9%Qwen3.5-27B
GPQA Diamond (Epoch AI run)66.9%Not measuredOnly GPT-4.1 measured
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%Not measuredOnly GPT-4.1 measured
LMArena Text: hard prompts13841415Qwen3.5-27B
LMArena Text: expert13641427Qwen3.5-27B
LMArena Text: hard prompts in English13931429Qwen3.5-27B
LMArena Vision: diagrams12151249Qwen3.5-27B
LMArena Vision: homework12481256Qwen3.5-27B
Kagi LLM Benchmark52.3%Not measuredOnly 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 index02
Math benchmarks for GPT-4.1 and Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
AIME39.6%Not measuredOnly GPT-4.1 measured
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%Not measuredOnly GPT-4.1 measured
FrontierMath, tiers 1 to 36.0%Not measuredOnly GPT-4.1 measured
MATH level 583.0%Not measuredOnly GPT-4.1 measured
LMArena Text: math13691429Qwen3.5-27B
LMArena Text: mathematical work13601429Qwen3.5-27B
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4.1 and Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
MMLU Pro80.5%Not measuredOnly GPT-4.1 measured
LegalBench (Vals AI)83.1%Not measuredOnly GPT-4.1 measured
CaseLaw (Vals AI)69.9%Not measuredOnly GPT-4.1 measured
CorpFin (Vals AI)63.0%Not measuredOnly GPT-4.1 measured
TaxEval (Vals AI)75.1%Not measuredOnly GPT-4.1 measured
MortgageTax (Vals AI)65.9%Not measuredOnly GPT-4.1 measured
MedQA (Vals AI)91.2%Not measuredOnly GPT-4.1 measured
MMLU Pro (Artificial Analysis run)80.6%Not measuredOnly GPT-4.1 measured
SimpleQA Verified31.1%Not measuredOnly GPT-4.1 measured
GeoBench72.0%Not measuredOnly GPT-4.1 measured
LMArena Text: science13821437Qwen3.5-27B
LMArena Text: medicine and healthcare13791425Qwen3.5-27B
LMArena Text: legal and government13991405Qwen3.5-27B
LMArena Text: business and finance13691399Qwen3.5-27B
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 Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
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 index425
General benchmarks for GPT-4.1 and Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
IFBench43.0%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)68.3%77.7%Qwen3.5-27B
DTBench68.3%82.4%Qwen3.5-27B
LMCA25.6%34.0%Qwen3.5-27B
Fiction.LiveBench63.9%Not measuredOnly GPT-4.1 measured
LMArena Text13831409Qwen3.5-27B
LMArena Text: instruction following13671393Qwen3.5-27B
LMArena Text: creative writing13631362GPT-4.1
LMArena Vision12101241Qwen3.5-27B
LMArena Text: English13921425Qwen3.5-27B
LMArena Text: non-English13701390Qwen3.5-27B
LMArena Text: Chinese13821478Qwen3.5-27B
LMArena Text: German13811392Qwen3.5-27B
LMArena Text: Japanese13191346Qwen3.5-27B
LMArena Text: Korean13381359Qwen3.5-27B
LMArena Text: French13821410Qwen3.5-27B
LMArena Text: Spanish13761407Qwen3.5-27B
LMArena Text: Russian13771390Qwen3.5-27B
LMArena Text: Polish13891375GPT-4.1
LMArena Text: multi-turn13971410Qwen3.5-27B
LMArena Text: longer queries13851413Qwen3.5-27B
LMArena Text: writing and language13711380Qwen3.5-27B
LMArena Text: entertainment and media13651363GPT-4.1
LMArena Vision: English12091246Qwen3.5-27B
LMArena Vision: Chinese12261301Qwen3.5-27B
LMArena Vision: reading text12181246Qwen3.5-27B
LMArena Vision: humor11961227Qwen3.5-27B
LMArena Vision: captioning12071170GPT-4.1
LMArena Vision: recognizing things12081210Qwen3.5-27B
LMArena Vision: creative writing12101224Qwen3.5-27B
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
Image generation benchmarks for GPT-4.1 and Qwen3.5-27B
BenchmarkGPT-4.1Qwen3.5-27BLeader
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
Qwen3.5-27B$0.0010Fits in one request

Qwen3.5-27B costs less

Repository review

50K input + 3K output tokens

GPT-4.1$0.124Fits in one request
Qwen3.5-27B$0.014Fits in one request

Qwen3.5-27B costs less

Agent loop

200K input + 10K output tokens

GPT-4.1$0.480Fits in one request
Qwen3.5-27B$0.055Fits in one request

Qwen3.5-27B 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 Qwen3.5-27B
SpecificationOpenAI GPT-4.1Qwen3.5-27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 14, 2025February 25, 2026
Context window1.05M262K
Input $/Mtok$2.00$0.195
Output $/Mtok$8.00$1.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved task$0.37Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 better than Qwen3.5-27B 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 Terminal-Bench Hard (Artificial Analysis run) or WeirdML, where it leads.

Which is cheaper, GPT-4.1 or Qwen3.5-27B?

GPT-4.1 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for GPT-4.1 only.

Which has the bigger context window, GPT-4.1 or Qwen3.5-27B?

GPT-4.1, with 1.05M tokens against 262K.

How many benchmarks is the GPT-4.1 vs Qwen3.5-27B 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 Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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