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GPT-4o (2024-08-06) vs Qwen2.5 Coder 32B Instruct

GPT-4o (2024-08-06) leads Qwen2.5 Coder 32B Instruct on the Artificial Analysis Intelligence Index, 7.7 to 6.7, and Qwen2.5 Coder 32B Instruct costs 10.0x less per output token ($1.00 vs $10.00 per million). Updated October 22, 2026; 28 benchmarks have a result for both.

Replace GPT-4o (2024-08-06) Replace Qwen2.5 Coder 32B Instruct
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
27
Qwen2.5 Coder 32B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.00
1
vs boards won of 28 shared

GPT-4o (2024-08-06) or Qwen2.5 Coder 32B Instruct: 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 a larger context window (128K against 33K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.

Model AGPT-4o (2024-08-06)OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen2.5 Coder 32B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
28
GPT-4o (2024-08-06) only
30
Qwen2.5 Coder 32B Instruct only
7
Like-for-like categories
6 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, Qwen2.5 Coder 32B Instruct not benchmarked.
Widest category
General, 10.4 points apart on average over 11 shared benchmarks.
Widest single gap
LMArena Text: entertainment and media: GPT-4o (2024-08-06) 1267 against Qwen2.5 Coder 32B Instruct 1150, 19.5 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against Qwen2.5 Coder 32B Instruct $1.00 per million output tokens, 10.0x apart.
Context window
GPT-4o (2024-08-06) takes 128K tokens, Qwen2.5 Coder 32B Instruct 33K.

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)BarsQwen2.5 Coder 32B InstructTies
Coding40
Intelligence10
Reasoning41
Math20
Knowledge50
General110
All shared boards271
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GPT-4o (2024-08-06) and Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
Aider polyglot23.1%16.4%GPT-4o (2024-08-06)
SWE-bench VerifiedNot measured47.0%Only Qwen2.5 Coder 32B Instruct measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
LiveCodeBench (Artificial Analysis run)31.7%29.5%GPT-4o (2024-08-06)
Terminal-Bench Hard (Artificial Analysis run)8.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: coding12831276GPT-4o (2024-08-06)
LMArena Text: software and IT12781261GPT-4o (2024-08-06)
Neither model has a result on the other 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-4o (2024-08-06) and Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
AA Intelligence Index7.76.7GPT-4o (2024-08-06)
Epoch Capabilities IndexNot measured119.5Only Qwen2.5 Coder 32B Instruct measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for GPT-4o (2024-08-06) and Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
τ²-Bench Telecom28.9%Not measuredOnly GPT-4o (2024-08-06) measured
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
Reasoning benchmarks for GPT-4o (2024-08-06) and Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
MMMU Pro (Vals AI)64.0%Not measuredOnly GPT-4o (2024-08-06) measured
GPQA Diamond (Artificial Analysis run)52.1%41.7%GPT-4o (2024-08-06)
Humanity's Last Exam (Artificial Analysis run)2.3%3.5%Qwen2.5 Coder 32B Instruct
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 prompts12641251GPT-4o (2024-08-06)
LMArena Text: expert12411221GPT-4o (2024-08-06)
LMArena Text: hard prompts in English12771269GPT-4o (2024-08-06)
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for GPT-4o (2024-08-06) and Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
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
GSM8KNot measured93.0%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: math12851251GPT-4o (2024-08-06)
LMArena Text: mathematical work12831243GPT-4o (2024-08-06)
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GPT-4o (2024-08-06) and Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
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
MMLU Pro (Artificial Analysis run)Not measured63.5%Only Qwen2.5 Coder 32B Instruct measured
SimpleQA Verified26.0%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU84.3%79.1%GPT-4o (2024-08-06)
AI2 Reasoning ChallengeNot measured70.5%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text: science12741221GPT-4o (2024-08-06)
LMArena Text: medicine and healthcare12521187GPT-4o (2024-08-06)
LMArena Text: legal and government12751257GPT-4o (2024-08-06)
LMArena Text: business and finance12321224GPT-4o (2024-08-06)
Neither model has a result on the other 4 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 Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index110
General benchmarks for GPT-4o (2024-08-06) and Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
IFBench36.0%Not measuredOnly GPT-4o (2024-08-06) measured
Long context reasoning (AA-LCR)41.0%Not measuredOnly GPT-4o (2024-08-06) measured
HellaSwagNot measured83.0%Only Qwen2.5 Coder 32B Instruct measured
WinoGrandeNot measured80.8%Only Qwen2.5 Coder 32B Instruct measured
LMArena Text12831230GPT-4o (2024-08-06)
LMArena Text: instruction following12671223GPT-4o (2024-08-06)
LMArena Text: creative writing12741174GPT-4o (2024-08-06)
LMArena Vision1065Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: English12971248GPT-4o (2024-08-06)
LMArena Text: non-English12621205GPT-4o (2024-08-06)
LMArena Text: Chinese12541222GPT-4o (2024-08-06)
LMArena Text: German1257Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Japanese1234Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Korean1218Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: French1263Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Spanish1269Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: Russian12721228GPT-4o (2024-08-06)
LMArena Text: multi-turn12791222GPT-4o (2024-08-06)
LMArena Text: longer queries12831251GPT-4o (2024-08-06)
LMArena Text: writing and language12901191GPT-4o (2024-08-06)
LMArena Text: entertainment and media12671150GPT-4o (2024-08-06)
LMArena Vision: English1072Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Vision: Chinese1009Not measuredOnly GPT-4o (2024-08-06) measured
Neither model has a result on the other 23 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 Qwen2.5 Coder 32B Instruct
BenchmarkGPT-4o (2024-08-06)Qwen2.5 Coder 32B InstructLeader
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
Qwen2.5 Coder 32B Instruct$0.0012Fits in one request

Qwen2.5 Coder 32B Instruct costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
Qwen2.5 Coder 32B Instruct$0.036Exceeds the context window

Qwen2.5 Coder 32B Instruct costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
Qwen2.5 Coder 32B Instruct$0.142Exceeds the context window

Qwen2.5 Coder 32B Instruct 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 Qwen2.5 Coder 32B Instruct
SpecificationOpenAI GPT-4o (2024-08-06)Qwen2.5 Coder 32B Instruct
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 6, 2024November 11, 2024
Context window128K33K
Input $/Mtok$2.50$0.66
Output $/Mtok$10.00$1.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4o (2024-08-06) better than Qwen2.5 Coder 32B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-4o (2024-08-06) for a larger context window (128K against 33K tokens) and work measured by Humanity's Last Exam (Artificial Analysis run), where it leads.

Which is cheaper, GPT-4o (2024-08-06) or Qwen2.5 Coder 32B Instruct?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen2.5 Coder 32B Instruct costs $0.66 and $1.00. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4o (2024-08-06) or Qwen2.5 Coder 32B Instruct?

GPT-4o (2024-08-06), with 128K tokens against 33K.

How many benchmarks is the GPT-4o (2024-08-06) vs Qwen2.5 Coder 32B Instruct comparison based on?

28 benchmarks have a result for both models as of October 22, 2026. GPT-4o (2024-08-06)'s coding index rests on 0 independent benchmarks and Qwen2.5 Coder 32B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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