Enter opens the highlighted result. Esc closes.

GPT-4.1 Nano vs Qwen3.5-27B

Qwen3.5-27B leads GPT-4.1 Nano on the Artificial Analysis Intelligence Index, 22.9 to 7.8, and GPT-4.1 Nano costs 3.9x less per output token ($0.40 vs $1.56 per million). Updated October 22, 2026; 36 benchmarks have a result for both.

Replace GPT-4.1 Nano Replace Qwen3.5-27B
GPT-4.1 NanoOpenAI
Coding
No data
Overall
24.7
Output $/M
$0.40
0
Qwen3.5-27BQwen
Coding
No data
Overall
No data
Output $/M
$1.56
36
vs boards won of 36 shared

GPT-4.1 Nano 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 Nano for output tokens 3.9x cheaper ($0.40 vs $1.56 per million) and a larger context window (1.05M against 262K tokens).

Model AGPT-4.1 NanoOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
36
GPT-4.1 Nano only
35
Qwen3.5-27B only
18
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 Nano not benchmarked, Qwen3.5-27B not benchmarked.
Widest category
Agentic work, 76.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GPT-4.1 Nano 17.3% against Qwen3.5-27B 93.9%, 76.6 points apart on a 0 to 100 scale.
Output price
GPT-4.1 Nano $0.40 against Qwen3.5-27B $1.56 per million output tokens, 3.9x apart.
Context window
GPT-4.1 Nano 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-4.1 NanoBarsQwen3.5-27BTies
Coding04
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General019
All shared boards036
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
Aider polyglot8.9%Not measuredOnly GPT-4.1 Nano measured
LMArena WebDevNot measured1358Only Qwen3.5-27B measured
AA Coding Index11.1Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Vals AI)42.7%Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Artificial Analysis run)32.6%Not measuredOnly GPT-4.1 Nano measured
Terminal-Bench Hard (Artificial Analysis run)3.8%32.6%Qwen3.5-27B
WeirdML19.0%39.5%Qwen3.5-27B
ALE-BenchNot measured349.4Only Qwen3.5-27B measured
LMArena Text: coding13061427Qwen3.5-27B
LMArena Text: software and IT13051431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1386Only Qwen3.5-27B measured
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
AA Intelligence Index7.822.9Qwen3.5-27B
Epoch Capabilities Index129.5Not measuredOnly GPT-4.1 Nano measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
τ²-Bench Telecom17.3%93.9%Qwen3.5-27B
Vending-Bench 2Not measured$202Only Qwen3.5-27B measured
Berkeley Function Calling Leaderboard (BFCL)33.1%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
GPQA Diamond50.8%Not measuredOnly GPT-4.1 Nano measured
MMMU Pro (Vals AI)55.0%Not measuredOnly GPT-4.1 Nano measured
GPQA Diamond (Artificial Analysis run)51.2%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)3.8%23.9%Qwen3.5-27B
GPQA Diamond (Epoch AI run)48.9%Not measuredOnly GPT-4.1 Nano measured
ARC-AGI0.0%Not measuredOnly GPT-4.1 Nano measured
ARC-AGI-20.0%Not measuredOnly GPT-4.1 Nano measured
Chess puzzles0.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: hard prompts12861415Qwen3.5-27B
LMArena Text: expert12711427Qwen3.5-27B
LMArena Text: hard prompts in English12931429Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Kagi LLM Benchmark33.3%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
AIME26.5%Not measuredOnly GPT-4.1 Nano measured
MATH 500 (Vals AI)80.2%Not measuredOnly GPT-4.1 Nano measured
MGSM (Vals AI)69.3%Not measuredOnly GPT-4.1 Nano measured
AA Math Index24.0Not measuredOnly GPT-4.1 Nano measured
AIME 2025 (Artificial Analysis run)24.0%Not measuredOnly GPT-4.1 Nano measured
OTIS Mock AIME28.9%Not measuredOnly GPT-4.1 Nano measured
MATH level 570.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: math12741429Qwen3.5-27B
LMArena Text: mathematical work12741429Qwen3.5-27B
MathArenaNot measured15.8%Only Qwen3.5-27B measured
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
MMLU Pro63.5%Not measuredOnly GPT-4.1 Nano measured
LegalBench (Vals AI)61.1%Not measuredOnly GPT-4.1 Nano measured
CorpFin (Vals AI)42.1%Not measuredOnly GPT-4.1 Nano measured
TaxEval (Vals AI)60.8%Not measuredOnly GPT-4.1 Nano measured
MortgageTax (Vals AI)52.8%Not measuredOnly GPT-4.1 Nano measured
MedQA (Vals AI)68.2%Not measuredOnly GPT-4.1 Nano measured
MMLU Pro (Artificial Analysis run)65.7%Not measuredOnly GPT-4.1 Nano measured
SimpleQA Verified6.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: science12971437Qwen3.5-27B
LMArena Text: medicine and healthcare12761425Qwen3.5-27B
LMArena Text: legal and government13001405Qwen3.5-27B
LMArena Text: business and finance12671399Qwen3.5-27B
Neither model has a result on the other 8 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
Design Arena: websites973Not measuredOnly GPT-4.1 Nano measured
Design Arena: UI components913Not measuredOnly GPT-4.1 Nano measured
Design Arena: game dev970Not measuredOnly GPT-4.1 Nano measured
Design Arena: data viz891Not measuredOnly GPT-4.1 Nano measured
Design Arena: 3D924Not measuredOnly GPT-4.1 Nano measured
Design Arena: all code categories962Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
IFBench32.0%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)20.3%77.7%Qwen3.5-27B
DTBench52.5%82.4%Qwen3.5-27B
LMCA5.5%34.0%Qwen3.5-27B
Fiction.LiveBench25.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text12851409Qwen3.5-27B
LMArena Text: instruction following12661393Qwen3.5-27B
LMArena Text: creative writing12601362Qwen3.5-27B
LMArena Vision10631241Qwen3.5-27B
LMArena Text: English13031425Qwen3.5-27B
LMArena Text: non-English12601390Qwen3.5-27B
LMArena Text: Chinese12701478Qwen3.5-27B
LMArena Text: German12881392Qwen3.5-27B
LMArena Text: Japanese11981346Qwen3.5-27B
LMArena Text: KoreanNot measured1359Only Qwen3.5-27B measured
LMArena Text: FrenchNot measured1410Only Qwen3.5-27B measured
LMArena Text: SpanishNot measured1407Only Qwen3.5-27B measured
LMArena Text: Russian12611390Qwen3.5-27B
LMArena Text: PolishNot measured1375Only Qwen3.5-27B measured
LMArena Text: multi-turn12771410Qwen3.5-27B
LMArena Text: longer queries12831413Qwen3.5-27B
LMArena Text: writing and language12611380Qwen3.5-27B
LMArena Text: entertainment and media12431363Qwen3.5-27B
LMArena Vision: English11011246Qwen3.5-27B
LMArena Vision: ChineseNot measured1301Only Qwen3.5-27B measured
LMArena Vision: reading textNot measured1246Only Qwen3.5-27B measured
LMArena Vision: humorNot measured1227Only Qwen3.5-27B measured
LMArena Vision: captioningNot measured1170Only Qwen3.5-27B measured
LMArena Vision: recognizing thingsNot measured1210Only Qwen3.5-27B measured
LMArena Vision: creative writingNot measured1224Only Qwen3.5-27B measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4.1 Nano and Qwen3.5-27B
BenchmarkGPT-4.1 NanoQwen3.5-27BLeader
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 Nano$0.0003Fits in one request
Qwen3.5-27B$0.0010Fits in one request

GPT-4.1 Nano costs less

Repository review

50K input + 3K output tokens

GPT-4.1 Nano$0.0062Fits in one request
Qwen3.5-27B$0.014Fits in one request

GPT-4.1 Nano costs less

Agent loop

200K input + 10K output tokens

GPT-4.1 Nano$0.024Fits in one request
Qwen3.5-27B$0.055Fits in one request

GPT-4.1 Nano 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 Nano and Qwen3.5-27B
SpecificationOpenAI GPT-4.1 NanoQwen3.5-27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 14, 2025February 25, 2026
Context window1.05M262K
Input $/Mtok$0.10$0.195
Output $/Mtok$0.40$1.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 Nano 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 Nano for output tokens 3.9x cheaper ($0.40 vs $1.56 per million) and a larger context window (1.05M against 262K tokens).

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

GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.

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

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

How many benchmarks is the GPT-4.1 Nano vs Qwen3.5-27B comparison based on?

36 benchmarks have a result for both models as of October 22, 2026. GPT-4.1 Nano'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.

Compare next