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GPT-4o-mini (2024-07-18) vs Qwen3.5-27B

Updated October 21, 2026. 32 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-4o-mini (2024-07-18) Replace Qwen3.5-27B
GPT-4o-mini (2024-07-18)OpenAI
Coding
3.6
Overall
No data
Output $/M
$0.60
0
Qwen3.5-27BQwen
Coding
22.6
Overall
22.9
Output $/M
$2.16
32
vs boards won of 32 shared

Decision reading

Pick Qwen3.5-27B for coding: it scores 22.6 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18).

Pick GPT-4o-mini (2024-07-18) when output tokens cost 3.6x less.

Model AGPT-4o-mini (2024-07-18)OpenAI3.6/1001 independent benchmarkNo cost per solved taskModel BQwen3.5-27BQwen22.6/1001 independent benchmarkNo cost per solved task
Shared results
32
GPT-4o-mini (2024-07-18) only
14
Qwen3.5-27B only
21
Like-for-like categories
5 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-4o-mini (2024-07-18) 3.6 from 1 benchmark, Qwen3.5-27B 22.6 from 1 benchmark.
Widest category
Reasoning, 27.3 points apart on average over 3 shared benchmarks.
Widest single gap
LMArena Vision: Chinese: GPT-4o-mini (2024-07-18) 1033 against Qwen3.5-27B 1301, 44.5 points apart on a 0 to 100 scale.
Output price
GPT-4o-mini (2024-07-18) $0.60 against Qwen3.5-27B $2.16 per million output tokens, 3.6x apart.
Context window
GPT-4o-mini (2024-07-18) 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-mini (2024-07-18)BarsQwen3.5-27BTies
Coding03
Reasoning03
Math02
Knowledge04
General020
All shared boards032
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
Coding benchmarks for GPT-4o-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)Qwen3.5-27BLeader
Aider polyglot3.6%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena WebDevNot measured1358Only Qwen3.5-27B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured32.6%Only Qwen3.5-27B measured
WeirdML11.8%39.5%Qwen3.5-27B
ALE-BenchNot measured349.4Only Qwen3.5-27B measured
LMArena Text: coding12901427Qwen3.5-27B
LMArena Text: software and IT12911431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1386Only Qwen3.5-27B measured
Neither model has a result on the other 29 boards here
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Intelligence benchmarks for GPT-4o-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)Qwen3.5-27BLeader
AA Intelligence IndexNot measured22.9Only Qwen3.5-27B measured
Neither model has a result on the other 1 board here
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for GPT-4o-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)Qwen3.5-27BLeader
τ²-Bench TelecomNot measured93.9%Only Qwen3.5-27B measured
BALROG17.4%Not measuredOnly GPT-4o-mini (2024-07-18) measured
Vending-Bench 2Not measured$202Only Qwen3.5-27B measured
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index03
Reasoning benchmarks for GPT-4o-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)Qwen3.5-27BLeader
GPQA Diamond (Artificial Analysis run)Not measured85.8%Only Qwen3.5-27B measured
Humanity's Last Exam (Artificial Analysis run)Not measured23.9%Only Qwen3.5-27B measured
GPQA Diamond (Epoch AI run)37.7%Not measuredOnly GPT-4o-mini (2024-07-18) measured
SimpleBench10.7%Not measuredOnly GPT-4o-mini (2024-07-18) measured
Chess puzzles3.0%Not measuredOnly GPT-4o-mini (2024-07-18) measured
Mystery game puzzles12.0%Not measuredOnly GPT-4o-mini (2024-07-18) measured
VPCT34.0%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: hard prompts12671415Qwen3.5-27B
LMArena Text: expert12341427Qwen3.5-27B
LMArena Text: hard prompts in English12781429Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4o-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)Qwen3.5-27BLeader
OTIS Mock AIME6.9%Not measuredOnly GPT-4o-mini (2024-07-18) measured
FrontierMath, tiers 1 to 30.7%Not measuredOnly GPT-4o-mini (2024-07-18) measured
MATH level 552.6%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: math12671429Qwen3.5-27B
LMArena Text: mathematical work12711429Qwen3.5-27B
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-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)Qwen3.5-27BLeader
SimpleQA Verified8.3%Not measuredOnly GPT-4o-mini (2024-07-18) measured
MMLU76.7%Not measuredOnly GPT-4o-mini (2024-07-18) measured
GeoBench64.0%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text: science12861437Qwen3.5-27B
LMArena Text: medicine and healthcare12821425Qwen3.5-27B
LMArena Text: legal and government13051405Qwen3.5-27B
LMArena Text: business and finance12651399Qwen3.5-27B
Neither model has a result on the other 10 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4o-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)Qwen3.5-27BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for GPT-4o-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)Qwen3.5-27BLeader
IFBenchNot measured75.6%Only Qwen3.5-27B measured
Long context reasoning (AA-LCR)Not measured77.7%Only Qwen3.5-27B measured
DTBenchNot measured82.4%Only Qwen3.5-27B measured
LMCANot measured34.0%Only Qwen3.5-27B measured
Creative writing (Lech Mazur)67.2%Not measuredOnly GPT-4o-mini (2024-07-18) measured
LMArena Text12861409Qwen3.5-27B
LMArena Text: instruction following12581393Qwen3.5-27B
LMArena Text: creative writing12681362Qwen3.5-27B
LMArena Vision10661241Qwen3.5-27B
LMArena Text: English13011425Qwen3.5-27B
LMArena Text: non-English12661390Qwen3.5-27B
LMArena Text: Chinese12651478Qwen3.5-27B
LMArena Text: German12721392Qwen3.5-27B
LMArena Text: Japanese12161346Qwen3.5-27B
LMArena Text: Korean11951359Qwen3.5-27B
LMArena Text: French12971410Qwen3.5-27B
LMArena Text: Spanish12761407Qwen3.5-27B
LMArena Text: Russian12751390Qwen3.5-27B
LMArena Text: Polish12441375Qwen3.5-27B
LMArena Text: multi-turn12851410Qwen3.5-27B
LMArena Text: longer queries12891413Qwen3.5-27B
LMArena Text: writing and language12821380Qwen3.5-27B
LMArena Text: entertainment and media12531363Qwen3.5-27B
LMArena Vision: English10721246Qwen3.5-27B
LMArena Vision: Chinese10331301Qwen3.5-27B
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 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o-mini (2024-07-18) and Qwen3.5-27B
BenchmarkGPT-4o-mini (2024-07-18)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-mini (2024-07-18)$0.0004Fits in one request
Qwen3.5-27B$0.0014Fits in one request

GPT-4o-mini (2024-07-18) costs less

Repository review

50K input + 3K output tokens

GPT-4o-mini (2024-07-18)$0.0093Fits in one request
Qwen3.5-27B$0.020Fits in one request

GPT-4o-mini (2024-07-18) costs less

Agent loop

200K input + 10K output tokens

GPT-4o-mini (2024-07-18)$0.036Exceeds the context window
Qwen3.5-27B$0.076Fits in one request

GPT-4o-mini (2024-07-18) 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-mini (2024-07-18) and Qwen3.5-27B
SpecificationOpenAI GPT-4o-mini (2024-07-18)Qwen3.5-27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedJuly 18, 2024February 25, 2026
Context window128K262K
Input $/Mtok$0.15$0.27
Output $/Mtok$0.60$2.16
Coding index3.622.6
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o-mini (2024-07-18) better than Qwen3.5-27B for coding?

Pick Qwen3.5-27B for coding: it scores 22.6 on the Vibe Coding Index against 3.6 for GPT-4o-mini (2024-07-18). Pick GPT-4o-mini (2024-07-18) when output tokens cost 3.6x less.

Which is cheaper, GPT-4o-mini (2024-07-18) or Qwen3.5-27B?

GPT-4o-mini (2024-07-18) costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.5-27B costs $0.27 and $2.16. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4o-mini (2024-07-18) or Qwen3.5-27B?

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

How many benchmarks is the GPT-4o-mini (2024-07-18) vs Qwen3.5-27B comparison based on?

32 benchmarks have a result for both models as of October 21, 2026. GPT-4o-mini (2024-07-18)'s coding index rests on 1 independent benchmark 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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