LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

GPT-4.1 Nano vs Qwen3.5-Flash

Updated October 21, 2026. 40 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-4.1 Nano Replace Qwen3.5-Flash
GPT-4.1 NanoOpenAI
Coding
8.9
Overall
7.8
Output $/M
$0.40
0
Qwen3.5-FlashQwen
Coding
6.2
Overall
No data
Output $/M
$0.26
40
vs boards won of 40 shared

Decision reading

Pick GPT-4.1 Nano for coding: it scores 8.9 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash.

Pick Qwen3.5-Flash when output tokens cost 1.5x less, or when what matters is LiveCodeBench (Vals AI) or LMArena Text: coding, where it leads.

Model AGPT-4.1 NanoOpenAI8.9/1001 independent benchmarkNo cost per solved taskModel BQwen3.5-FlashQwen6.2/1001 independent benchmark$0.40 per solved task
Shared results
40
GPT-4.1 Nano only
29
Qwen3.5-Flash only
15
Like-for-like categories
6 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 8.9 from 1 benchmark, Qwen3.5-Flash 6.2 from 1 benchmark.
Widest category
Math, 41.7 points apart on average over 4 shared benchmarks.
Widest single gap
AIME: GPT-4.1 Nano 26.5% against Qwen3.5-Flash 92.5%, 66.0 points apart on a 0 to 100 scale.
Output price
GPT-4.1 Nano $0.40 against Qwen3.5-Flash $0.26 per million output tokens, 1.5x apart.
Context window
GPT-4.1 Nano 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.

Boards won, where both have a result
CategoryGPT-4.1 NanoBarsQwen3.5-FlashTies
Coding03
Intelligence01
Reasoning07
Math04
Knowledge010
General015
All shared boards040
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4.1 Nano and Qwen3.5-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
Aider polyglot8.9%Not measuredOnly GPT-4.1 Nano measured
LMArena WebDevNot measured1244Only Qwen3.5-Flash measured
AA Coding Index11.1Not measuredOnly GPT-4.1 Nano measured
LiveCodeBench (Vals AI)42.7%83.3%Qwen3.5-Flash
SWE-bench Verified (Vals AI)Not measured64.4%Only Qwen3.5-Flash measured
LiveCodeBench (Artificial Analysis run)32.6%Not measuredOnly GPT-4.1 Nano measured
Terminal-Bench Hard (Artificial Analysis run)3.8%Not measuredOnly GPT-4.1 Nano measured
WeirdML19.0%Not measuredOnly GPT-4.1 Nano measured
ALE-BenchNot measured265.9Only Qwen3.5-Flash measured
LMArena Text: coding13061412Qwen3.5-Flash
LMArena Text: software and IT13051416Qwen3.5-Flash
LMArena WebDev: ReactNot measured1229Only Qwen3.5-Flash measured
LMArena WebDev: HTMLNot measured1291Only Qwen3.5-Flash 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 Nano and Qwen3.5-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
AA Intelligence Index7.8Not measuredOnly GPT-4.1 Nano measured
Epoch Capabilities Index129.5143.5Qwen3.5-Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GPT-4.1 Nano and Qwen3.5-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
τ²-Bench Telecom17.3%Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for GPT-4.1 Nano and Qwen3.5-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
GPQA Diamond50.8%82.8%Qwen3.5-Flash
MMMU Pro (Vals AI)55.0%81.9%Qwen3.5-Flash
GPQA Diamond (Artificial Analysis run)51.2%Not measuredOnly GPT-4.1 Nano measured
Humanity's Last Exam (Artificial Analysis run)3.8%Not measuredOnly GPT-4.1 Nano measured
GPQA Diamond (Epoch AI run)48.9%82.3%Qwen3.5-Flash
ARC-AGI0.0%Not measuredOnly GPT-4.1 Nano measured
ARC-AGI-20.0%Not measuredOnly GPT-4.1 Nano measured
Chess puzzles0.0%14.0%Qwen3.5-Flash
Mystery game puzzlesNot measured20.0%Only Qwen3.5-Flash measured
LMArena Text: hard prompts12861403Qwen3.5-Flash
LMArena Text: expert12711407Qwen3.5-Flash
LMArena Text: hard prompts in English12931409Qwen3.5-Flash
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for GPT-4.1 Nano and Qwen3.5-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
AIME26.5%92.5%Qwen3.5-Flash
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%84.4%Qwen3.5-Flash
FrontierMath, tiers 1 to 3Not measured18.2%Only Qwen3.5-Flash measured
MATH level 570.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text: math12741407Qwen3.5-Flash
LMArena Text: mathematical work12741413Qwen3.5-Flash
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index010
Knowledge benchmarks for GPT-4.1 Nano and Qwen3.5-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
MMLU Pro63.5%84.1%Qwen3.5-Flash
LegalBench (Vals AI)61.1%84.3%Qwen3.5-Flash
CaseLaw (Vals AI)Not measured56.0%Only Qwen3.5-Flash measured
CorpFin (Vals AI)42.1%63.6%Qwen3.5-Flash
TaxEval (Vals AI)60.8%72.2%Qwen3.5-Flash
MortgageTax (Vals AI)52.8%67.4%Qwen3.5-Flash
MedQA (Vals AI)68.2%Not measuredOnly GPT-4.1 Nano measured
MedCode (Vals AI)Not measured33.0%Only Qwen3.5-Flash measured
MMLU Pro (Artificial Analysis run)65.7%Not measuredOnly GPT-4.1 Nano measured
SimpleQA Verified6.0%20.3%Qwen3.5-Flash
LMArena Text: science12971415Qwen3.5-Flash
LMArena Text: medicine and healthcare12761407Qwen3.5-Flash
LMArena Text: legal and government13001398Qwen3.5-Flash
LMArena Text: business and finance12671394Qwen3.5-Flash
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 Nano and Qwen3.5-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
Design Arena: websites973Not measuredOnly GPT-4.1 Nano measured
Design Arena: UI components916Not measuredOnly GPT-4.1 Nano measured
Design Arena: game dev971Not measuredOnly GPT-4.1 Nano measured
Design Arena: data viz891Not measuredOnly GPT-4.1 Nano measured
Design Arena: 3D925Not measuredOnly GPT-4.1 Nano measured
Design Arena: all code categories963Not 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 index015
General benchmarks for GPT-4.1 Nano and Qwen3.5-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
IFBench32.0%Not measuredOnly GPT-4.1 Nano measured
Long context reasoning (AA-LCR)20.3%Not measuredOnly GPT-4.1 Nano measured
DTBench52.5%82.9%Qwen3.5-Flash
LMCA5.5%29.1%Qwen3.5-Flash
Fiction.LiveBench25.0%Not measuredOnly GPT-4.1 Nano measured
LMArena Text12851397Qwen3.5-Flash
LMArena Text: instruction following12661374Qwen3.5-Flash
LMArena Text: creative writing12601343Qwen3.5-Flash
LMArena Vision1063Not measuredOnly GPT-4.1 Nano measured
LMArena Text: English13031404Qwen3.5-Flash
LMArena Text: non-English12601385Qwen3.5-Flash
LMArena Text: Chinese12701445Qwen3.5-Flash
LMArena Text: German12881390Qwen3.5-Flash
LMArena Text: Japanese11981369Qwen3.5-Flash
LMArena Text: KoreanNot measured1344Only Qwen3.5-Flash measured
LMArena Text: FrenchNot measured1413Only Qwen3.5-Flash measured
LMArena Text: SpanishNot measured1400Only Qwen3.5-Flash measured
LMArena Text: Russian12611380Qwen3.5-Flash
LMArena Text: PolishNot measured1376Only Qwen3.5-Flash measured
LMArena Text: multi-turn12771393Qwen3.5-Flash
LMArena Text: longer queries12831392Qwen3.5-Flash
LMArena Text: writing and language12611369Qwen3.5-Flash
LMArena Text: entertainment and media12431347Qwen3.5-Flash
LMArena Vision: English1101Not measuredOnly GPT-4.1 Nano measured
Neither model has a result on the other 20 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-Flash
BenchmarkGPT-4.1 NanoQwen3.5-FlashLeader
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-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

GPT-4.1 Nano$0.0062Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

GPT-4.1 Nano$0.024Fits in one request
Qwen3.5-Flash$0.016Fits in one request

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.

Specifications of GPT-4.1 Nano and Qwen3.5-Flash
SpecificationOpenAI GPT-4.1 NanoQwen3.5-Flash
MakerOpenAIQwen
WeightsNo public weights listedNo public weights listed
ReleasedApril 14, 2025February 25, 2026
Context window1.05M1M
Input $/Mtok$0.10$0.065
Output $/Mtok$0.40$0.26
Coding index8.96.2
Independent measurements behind it11
Cost per solved taskNot computable$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 Nano better than Qwen3.5-Flash for coding?

Pick GPT-4.1 Nano for coding: it scores 8.9 on the Vibe Coding Index against 6.2 for Qwen3.5-Flash. Pick Qwen3.5-Flash when output tokens cost 1.5x less, or when what matters is LiveCodeBench (Vals AI) or LMArena Text: coding, where it leads.

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

GPT-4.1 Nano costs $0.10 per million input tokens and $0.40 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.

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

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

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

40 benchmarks have a result for both models as of October 21, 2026. GPT-4.1 Nano's coding index rests on 1 independent benchmark and Qwen3.5-Flash's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next