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GPT-4o (2024-08-06) vs Qwen3.5-27B

Qwen3.5-27B leads GPT-4o (2024-08-06) on the Artificial Analysis Intelligence Index, 22.9 to 7.7, and Qwen3.5-27B costs 6.4x less per output token ($1.56 vs $10.00 per million). Updated October 23, 2026; 37 benchmarks have a result for both.

Replace GPT-4o (2024-08-06) Replace Qwen3.5-27B
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
0
Qwen3.5-27BQwen
Coding
No data
Overall
No data
Output $/M
$1.56
37
vs boards won of 37 shared

GPT-4o (2024-08-06) or Qwen3.5-27B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick GPT-4o (2024-08-06) for work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model AGPT-4o (2024-08-06)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
37
GPT-4o (2024-08-06) only
21
Qwen3.5-27B only
17
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-4o (2024-08-06) not benchmarked, Qwen3.5-27B not benchmarked.
Widest category
Agentic work, 64.9 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GPT-4o (2024-08-06) 28.9% against Qwen3.5-27B 93.9%, 64.9 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against Qwen3.5-27B $1.56 per million output tokens, 6.4x apart.
Context window
GPT-4o (2024-08-06) takes 128K 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-4o (2024-08-06)BarsQwen3.5-27BTies
Coding03
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General021
All shared boards037
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4o (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
Aider polyglot23.1%Not measuredOnly GPT-4o (2024-08-06) measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena WebDevNot measured1359Only Qwen3.5-27B measured
LiveCodeBench (Artificial Analysis run)31.7%Not measuredOnly GPT-4o (2024-08-06) measured
Terminal-Bench Hard (Artificial Analysis run)8.3%32.6%Qwen3.5-27B
WeirdMLNot measured39.5%Only Qwen3.5-27B measured
ALE-BenchNot measured349.4Only Qwen3.5-27B measured
LMArena Text: coding12831427Qwen3.5-27B
LMArena Text: software and IT12781431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1387Only Qwen3.5-27B measured
Neither model has a result on the other 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4o (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
AA Intelligence Index7.722.9Qwen3.5-27B
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GPT-4o (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
τ²-Bench Telecom28.9%93.9%Qwen3.5-27B
Vending-Bench 2Not measured$202Only Qwen3.5-27B 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 (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
MMMU Pro (Vals AI)64.0%Not measuredOnly GPT-4o (2024-08-06) measured
GPQA Diamond (Artificial Analysis run)52.1%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)2.3%23.9%Qwen3.5-27B
GPQA Diamond (Epoch AI run)49.2%Not measuredOnly GPT-4o (2024-08-06) measured
SimpleBench17.8%Not measuredOnly GPT-4o (2024-08-06) measured
Chess puzzles6.0%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: hard prompts12641414Qwen3.5-27B
LMArena Text: expert12411428Qwen3.5-27B
LMArena Text: hard prompts in English12771428Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4o (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
AIME14.0%Not measuredOnly GPT-4o (2024-08-06) measured
MATH 500 (Vals AI)75.2%Not measuredOnly GPT-4o (2024-08-06) measured
MGSM (Vals AI)90.7%Not measuredOnly GPT-4o (2024-08-06) measured
OTIS Mock AIME6.4%Not measuredOnly GPT-4o (2024-08-06) measured
FrontierMath, tiers 1 to 30.4%Not measuredOnly GPT-4o (2024-08-06) measured
MATH level 553.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: math12851429Qwen3.5-27B
LMArena Text: mathematical work12831429Qwen3.5-27B
MathArenaNot measured15.8%Only Qwen3.5-27B measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4o (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
MMLU Pro74.1%Not measuredOnly GPT-4o (2024-08-06) measured
LegalBench (Vals AI)80.1%Not measuredOnly GPT-4o (2024-08-06) measured
CorpFin (Vals AI)39.4%Not measuredOnly GPT-4o (2024-08-06) measured
TaxEval (Vals AI)71.1%Not measuredOnly GPT-4o (2024-08-06) measured
MortgageTax (Vals AI)61.0%Not measuredOnly GPT-4o (2024-08-06) measured
MedQA (Vals AI)88.2%Not measuredOnly GPT-4o (2024-08-06) measured
SimpleQA Verified26.0%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU84.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: science12741437Qwen3.5-27B
LMArena Text: medicine and healthcare12521425Qwen3.5-27B
LMArena Text: legal and government12751405Qwen3.5-27B
LMArena Text: business and finance12321399Qwen3.5-27B
Neither model has a result on the other 8 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4o (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
General benchmarks for GPT-4o (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
IFBench36.0%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)41.0%77.7%Qwen3.5-27B
DTBenchNot measured82.4%Only Qwen3.5-27B measured
LMCANot measured34.0%Only Qwen3.5-27B measured
LMArena Text12831409Qwen3.5-27B
LMArena Text: instruction following12671393Qwen3.5-27B
LMArena Text: creative writing12741363Qwen3.5-27B
LMArena Vision10651241Qwen3.5-27B
LMArena Text: English12971425Qwen3.5-27B
LMArena Text: non-English12621390Qwen3.5-27B
LMArena Text: Chinese12541478Qwen3.5-27B
LMArena Text: German12571393Qwen3.5-27B
LMArena Text: Japanese12341346Qwen3.5-27B
LMArena Text: Korean12181359Qwen3.5-27B
LMArena Text: French12631410Qwen3.5-27B
LMArena Text: Spanish12691407Qwen3.5-27B
LMArena Text: Russian12721390Qwen3.5-27B
LMArena Text: PolishNot measured1375Only Qwen3.5-27B measured
LMArena Text: multi-turn12791410Qwen3.5-27B
LMArena Text: longer queries12831413Qwen3.5-27B
LMArena Text: writing and language12901381Qwen3.5-27B
LMArena Text: entertainment and media12671363Qwen3.5-27B
LMArena Vision: English10721246Qwen3.5-27B
LMArena Vision: Chinese10091301Qwen3.5-27B
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 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o (2024-08-06) and Qwen3.5-27B
BenchmarkGPT-4o (2024-08-06)Qwen3.5-27BLeader
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-4o (2024-08-06)$0.0075Fits in one request
Qwen3.5-27B$0.0010Fits in one request

Qwen3.5-27B costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
Qwen3.5-27B$0.014Fits in one request

Qwen3.5-27B costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
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-4o (2024-08-06) and Qwen3.5-27B
SpecificationOpenAI GPT-4o (2024-08-06)Qwen3.5-27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 6, 2024February 25, 2026
Context window128K262K
Input $/Mtok$2.50$0.195
Output $/Mtok$10.00$1.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-08-06) better than Qwen3.5-27B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-08-06) for work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, GPT-4o (2024-08-06) or Qwen3.5-27B?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 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-4o (2024-08-06) or Qwen3.5-27B?

Qwen3.5-27B, with 262K tokens against 128K.

How many benchmarks is the GPT-4o (2024-08-06) vs Qwen3.5-27B comparison based on?

37 benchmarks have a result for both models as of October 23, 2026. GPT-4o (2024-08-06)'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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