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GPT-4o (2024-08-06) vs Qwen3 30B A3B Instruct 2507

GPT-4o (2024-08-06) leads Qwen3 30B A3B Instruct 2507 on the Artificial Analysis Intelligence Index, 7.7 to 7.5, and Qwen3 30B A3B Instruct 2507 costs 33.3x less per output token ($0.30 vs $10.00 per million). Updated October 23, 2026; 37 benchmarks have a result for both.

Replace GPT-4o (2024-08-06) Replace Qwen3 30B A3B Instruct 2507
GPT-4o (2024-08-06)OpenAI
Coding
No data
Overall
26.3
Output $/M
$10.00
3
Qwen3 30B A3B Instruct 2507Qwen
Coding
No data
Overall
41.5
Output $/M
$0.30
34
vs boards won of 37 shared

GPT-4o (2024-08-06) or Qwen3 30B A3B Instruct 2507: 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 LiveCodeBench (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 30B A3B Instruct 2507QwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
37
GPT-4o (2024-08-06) only
21
Qwen3 30B A3B Instruct 2507 only
8
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 30B A3B Instruct 2507 not benchmarked.
Widest category
Math, 31.8 points apart on average over 3 shared benchmarks.
Widest single gap
OTIS Mock AIME: GPT-4o (2024-08-06) 6.4% against Qwen3 30B A3B Instruct 2507 62.2%, 55.8 points apart on a 0 to 100 scale.
Output price
GPT-4o (2024-08-06) $10.00 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 33.3x apart.
Context window
GPT-4o (2024-08-06) takes 128K tokens, Qwen3 30B A3B Instruct 2507 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 30B A3B Instruct 2507Ties
Coding13
Intelligence10
Agentic work10
Reasoning06
Math03
Knowledge04
General018
All shared boards334
CodingLike-for-likeSix of these boards feed the Vibe Coding Index13
Coding benchmarks for GPT-4o (2024-08-06) and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
Aider polyglot23.1%Not measuredOnly GPT-4o (2024-08-06) measured
SWE-bench Lite21.7%Not measuredOnly GPT-4o (2024-08-06) measured
LiveCodeBench (Artificial Analysis run)31.7%70.7%Qwen3 30B A3B Instruct 2507
SciCodeNot measured33.0%Only Qwen3 30B A3B Instruct 2507 measured
Terminal-Bench Hard (Artificial Analysis run)8.3%6.1%GPT-4o (2024-08-06)
LMArena Text: coding12831416Qwen3 30B A3B Instruct 2507
LMArena Text: software and IT12781417Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 36 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-4o (2024-08-06) and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
AA Intelligence Index7.77.5GPT-4o (2024-08-06)
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GPT-4o (2024-08-06) and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
τ²-Bench Telecom28.9%28.1%GPT-4o (2024-08-06)
Berkeley Function Calling Leaderboard (BFCL)Not measured41.4%Only Qwen3 30B A3B Instruct 2507 measured
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for GPT-4o (2024-08-06) and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
MMMU Pro (Vals AI)64.0%Not measuredOnly GPT-4o (2024-08-06) measured
GPQA Diamond (Artificial Analysis run)52.1%70.7%Qwen3 30B A3B Instruct 2507
Humanity's Last Exam (Artificial Analysis run)2.3%10.3%Qwen3 30B A3B Instruct 2507
GPQA Diamond (Epoch AI run)49.2%55.6%Qwen3 30B A3B Instruct 2507
SimpleBench17.8%Not measuredOnly GPT-4o (2024-08-06) measured
Chess puzzles6.0%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: hard prompts12641398Qwen3 30B A3B Instruct 2507
LMArena Text: expert12411396Qwen3 30B A3B Instruct 2507
LMArena Text: hard prompts in English12771406Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for GPT-4o (2024-08-06) and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
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
AA Math IndexNot measured66.3Only Qwen3 30B A3B Instruct 2507 measured
AIME 2025 (Artificial Analysis run)Not measured66.3%Only Qwen3 30B A3B Instruct 2507 measured
OTIS Mock AIME6.4%62.2%Qwen3 30B A3B Instruct 2507
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: math12851395Qwen3 30B A3B Instruct 2507
LMArena Text: mathematical work12831412Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GPT-4o (2024-08-06) and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
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 measured80.5%Only Qwen3 30B A3B Instruct 2507 measured
SimpleQA Verified26.0%Not measuredOnly GPT-4o (2024-08-06) measured
MMLU84.3%Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: science12741393Qwen3 30B A3B Instruct 2507
LMArena Text: medicine and healthcare12521392Qwen3 30B A3B Instruct 2507
LMArena Text: legal and government12751387Qwen3 30B A3B Instruct 2507
LMArena Text: business and finance12321389Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 7 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 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for GPT-4o (2024-08-06) and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
IFBench36.0%50.7%Qwen3 30B A3B Instruct 2507
Long context reasoning (AA-LCR)41.0%61.3%Qwen3 30B A3B Instruct 2507
DTBenchNot measured67.2%Only Qwen3 30B A3B Instruct 2507 measured
LMCANot measured22.4%Only Qwen3 30B A3B Instruct 2507 measured
LMArena Text12831384Qwen3 30B A3B Instruct 2507
LMArena Text: instruction following12671363Qwen3 30B A3B Instruct 2507
LMArena Text: creative writing12741317Qwen3 30B A3B Instruct 2507
LMArena Vision1065Not measuredOnly GPT-4o (2024-08-06) measured
LMArena Text: English12971391Qwen3 30B A3B Instruct 2507
LMArena Text: non-English12621372Qwen3 30B A3B Instruct 2507
LMArena Text: Chinese12541433Qwen3 30B A3B Instruct 2507
LMArena Text: German12571379Qwen3 30B A3B Instruct 2507
LMArena Text: Japanese12341338Qwen3 30B A3B Instruct 2507
LMArena Text: Korean12181331Qwen3 30B A3B Instruct 2507
LMArena Text: French12631419Qwen3 30B A3B Instruct 2507
LMArena Text: Spanish12691404Qwen3 30B A3B Instruct 2507
LMArena Text: Russian12721369Qwen3 30B A3B Instruct 2507
LMArena Text: PolishNot measured1353Only Qwen3 30B A3B Instruct 2507 measured
LMArena Text: multi-turn12791378Qwen3 30B A3B Instruct 2507
LMArena Text: longer queries12831378Qwen3 30B A3B Instruct 2507
LMArena Text: writing and language12901336Qwen3 30B A3B Instruct 2507
LMArena Text: entertainment and media12671334Qwen3 30B A3B Instruct 2507
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 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-4o (2024-08-06) and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507Leader
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 30B A3B Instruct 2507$0.0002Fits in one request

Qwen3 30B A3B Instruct 2507 costs less

Repository review

50K input + 3K output tokens

GPT-4o (2024-08-06)$0.155Fits in one request
Qwen3 30B A3B Instruct 2507$0.0054Fits in one request

Qwen3 30B A3B Instruct 2507 costs less

Agent loop

200K input + 10K output tokens

GPT-4o (2024-08-06)$0.600Exceeds the context window
Qwen3 30B A3B Instruct 2507$0.021Fits in one request

Qwen3 30B A3B Instruct 2507 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 30B A3B Instruct 2507
SpecificationOpenAI GPT-4o (2024-08-06)Qwen3 30B A3B Instruct 2507
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedAugust 6, 2024July 29, 2025
Context window128K262K
Input $/Mtok$2.50$0.09
Output $/Mtok$10.00$0.30
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 Qwen3 30B A3B Instruct 2507 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 LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, GPT-4o (2024-08-06) or Qwen3 30B A3B Instruct 2507?

GPT-4o (2024-08-06) costs $2.50 per million input tokens and $10.00 per million output tokens; Qwen3 30B A3B Instruct 2507 costs $0.09 and $0.30. Cost per solved task is known for neither of them.

Which has the bigger context window, GPT-4o (2024-08-06) or Qwen3 30B A3B Instruct 2507?

Qwen3 30B A3B Instruct 2507, with 262K tokens against 128K.

How many benchmarks is the GPT-4o (2024-08-06) vs Qwen3 30B A3B Instruct 2507 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 30B A3B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.

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