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GPT-4o (2024-05-13) vs Qwen3.5-Flash

Qwen3.5-Flash costs 57.7x less per output token than GPT-4o (2024-05-13) ($0.26 vs $15.00 per million). Updated October 23, 2026; 31 benchmarks have a result for both.

Replace GPT-4o (2024-05-13) Replace Qwen3.5-Flash
GPT-4o (2024-05-13)OpenAI
Coding
No data
Overall
27.1
Output $/M
$15.00
0
Qwen3.5-FlashQwen
Coding
41.1
Overall
50.1
Output $/M
$0.26
31
vs boards won of 31 shared

GPT-4o (2024-05-13) or Qwen3.5-Flash: which to pick

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-4o (2024-05-13) has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Model AGPT-4o (2024-05-13)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-FlashQwen41.1/1002 independent benchmarks$0.40 per solved task
Shared results
31
GPT-4o (2024-05-13) only
13
Qwen3.5-Flash only
24
Like-for-like categories
5 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-4o (2024-05-13) not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
Widest category
Math, 40.0 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: GPT-4o (2024-05-13) 6.3% against Qwen3.5-Flash 84.4%, 78.2 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-05-13) $15.00 against Qwen3.5-Flash $0.26 per million output tokens, 57.7x apart.
Context window
GPT-4o (2024-05-13) takes 128K 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-4o (2024-05-13)BarsQwen3.5-FlashTies
Coding02
Reasoning05
Math03
Knowledge04
General017
All shared boards031
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-FlashLeader
LMArena WebDevNot measured1244Only Qwen3.5-Flash measured
AA Coding Index24.2Not measuredOnly GPT-4o (2024-05-13) measured
LiveCodeBench (Vals AI)Not measured83.3%Only Qwen3.5-Flash measured
SWE-bench Verified (Vals AI)Not measured64.4%Only Qwen3.5-Flash measured
LiveCodeBench (Artificial Analysis run)33.4%Not measuredOnly GPT-4o (2024-05-13) measured
ALE-BenchNot measured265.9Only Qwen3.5-Flash measured
LMArena Text: coding12971412Qwen3.5-Flash
LMArena Text: software and IT12971416Qwen3.5-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 33 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Intelligence benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-FlashLeader
AA Intelligence Index7.3Not measuredOnly GPT-4o (2024-05-13) measured
Epoch Capabilities IndexNot measured143.5Only Qwen3.5-Flash measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-FlashLeader
METR time horizons40.8%Not measuredOnly GPT-4o (2024-05-13) measured
BALROG32.3%Not measuredOnly GPT-4o (2024-05-13) measured
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-FlashLeader
GPQA Diamond50.2%82.8%Qwen3.5-Flash
MMMU Pro (Vals AI)Not measured81.9%Only Qwen3.5-Flash measured
GPQA Diamond (Artificial Analysis run)52.6%Not measuredOnly GPT-4o (2024-05-13) measured
Humanity's Last Exam (Artificial Analysis run)1.8%Not measuredOnly GPT-4o (2024-05-13) measured
GPQA Diamond (Epoch AI run)48.9%82.3%Qwen3.5-Flash
Chess puzzlesNot measured14.0%Only Qwen3.5-Flash measured
Mystery game puzzlesNot measured20.0%Only Qwen3.5-Flash measured
LMArena Text: hard prompts12811403Qwen3.5-Flash
LMArena Text: expert12501407Qwen3.5-Flash
LMArena Text: hard prompts in English12931409Qwen3.5-Flash
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-FlashLeader
AIMENot measured92.5%Only Qwen3.5-Flash measured
OTIS Mock AIME6.3%84.4%Qwen3.5-Flash
FrontierMath, tiers 1 to 3Not measured18.2%Only Qwen3.5-Flash measured
MATH level 551.0%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: math12841407Qwen3.5-Flash
LMArena Text: mathematical work12841413Qwen3.5-Flash
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-FlashLeader
MMLU ProNot measured84.1%Only Qwen3.5-Flash measured
LegalBench (Vals AI)Not measured84.3%Only Qwen3.5-Flash measured
CaseLaw (Vals AI)Not measured56.0%Only Qwen3.5-Flash measured
CorpFin (Vals AI)Not measured63.6%Only Qwen3.5-Flash measured
TaxEval (Vals AI)Not measured72.2%Only Qwen3.5-Flash measured
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedCode (Vals AI)Not measured33.0%Only Qwen3.5-Flash measured
MMLU Pro (Artificial Analysis run)74.0%Not measuredOnly GPT-4o (2024-05-13) measured
SimpleQA VerifiedNot measured20.3%Only Qwen3.5-Flash measured
MMLU84.2%Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: science12951415Qwen3.5-Flash
LMArena Text: medicine and healthcare12781407Qwen3.5-Flash
LMArena Text: legal and government13141397Qwen3.5-Flash
LMArena Text: business and finance12611394Qwen3.5-Flash
Neither model has a result on the other 6 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-FlashLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index017
General benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
DTBenchNot measured82.9%Only Qwen3.5-Flash measured
LMCANot measured29.1%Only Qwen3.5-Flash measured
LMArena Text13001397Qwen3.5-Flash
LMArena Text: instruction following12781375Qwen3.5-Flash
LMArena Text: creative writing12911343Qwen3.5-Flash
LMArena Vision1137Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Text: English13121404Qwen3.5-Flash
LMArena Text: non-English12831385Qwen3.5-Flash
LMArena Text: Chinese12771446Qwen3.5-Flash
LMArena Text: German12821391Qwen3.5-Flash
LMArena Text: Japanese12571369Qwen3.5-Flash
LMArena Text: Korean12341344Qwen3.5-Flash
LMArena Text: French13041413Qwen3.5-Flash
LMArena Text: Spanish12921399Qwen3.5-Flash
LMArena Text: Russian12851380Qwen3.5-Flash
LMArena Text: Polish13651376Qwen3.5-Flash
LMArena Text: multi-turn13021393Qwen3.5-Flash
LMArena Text: longer queries12881392Qwen3.5-Flash
LMArena Text: writing and language12981369Qwen3.5-Flash
LMArena Text: entertainment and media12891347Qwen3.5-Flash
LMArena Vision: English1147Not measuredOnly GPT-4o (2024-05-13) measured
LMArena Vision: Chinese1132Not measuredOnly GPT-4o (2024-05-13) measured
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o (2024-05-13) and Qwen3.5-Flash
BenchmarkGPT-4o (2024-05-13)Qwen3.5-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-4o (2024-05-13)$0.013Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-05-13)$0.295Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-05-13)$1.15Exceeds the context window
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-4o (2024-05-13) and Qwen3.5-Flash
SpecificationOpenAI GPT-4o (2024-05-13)Qwen3.5-Flash
MakerOpenAIQwen
WeightsNo public weights listedNo public weights listed
ReleasedMay 13, 2024February 25, 2026
Context window128K1M
Input $/Mtok$5.00$0.065
Output $/Mtok$15.00$0.26
Coding indexNot benchmarked41.1
Independent measurements behind it02
Cost per solved taskNot computable$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-05-13) better than Qwen3.5-Flash for coding?

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; GPT-4o (2024-05-13) has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, GPT-4o (2024-05-13) or Qwen3.5-Flash?

GPT-4o (2024-05-13) costs $5.00 per million input tokens and $15.00 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-4o (2024-05-13) or Qwen3.5-Flash?

Qwen3.5-Flash, with 1M tokens against 128K.

How many benchmarks is the GPT-4o (2024-05-13) vs Qwen3.5-Flash comparison based on?

31 benchmarks have a result for both models as of October 23, 2026. GPT-4o (2024-05-13)'s coding index rests on 0 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.

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