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GPT-4 vs Qwen3 30B A3B Instruct 2507

Qwen3 30B A3B Instruct 2507 leads GPT-4 on the Artificial Analysis Intelligence Index, 7.5 to 6.7, and Qwen3 30B A3B Instruct 2507 costs 200.0x less per output token ($0.30 vs $60.00 per million). Updated October 22, 2026; 34 benchmarks have a result for both.

Replace GPT-4 Replace Qwen3 30B A3B Instruct 2507
GPT-4OpenAI
Coding
No data
Overall
No data
Output $/M
$60.00
0
Qwen3 30B A3B Instruct 2507Qwen
Coding
No data
Overall
41.5
Output $/M
$0.30
34
vs boards won of 34 shared

GPT-4 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-4 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Model AGPT-4OpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 30B A3B Instruct 2507QwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
34
GPT-4 only
11
Qwen3 30B A3B Instruct 2507 only
11
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-4 not benchmarked, Qwen3 30B A3B Instruct 2507 not benchmarked.
Widest category
Coding, 35.8 points apart on average over 2 shared benchmarks.
Widest single gap
LMArena Text: Chinese: GPT-4 1184 against Qwen3 30B A3B Instruct 2507 1432, 41.4 points apart on a 0 to 100 scale.
Output price
GPT-4 $60.00 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 200.0x apart.
Context window
GPT-4 takes 8K 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-4BarsQwen3 30B A3B Instruct 2507Ties
Coding02
Intelligence01
Reasoning04
Math02
Knowledge05
General020
All shared boards034
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for GPT-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 30B A3B Instruct 2507Leader
SWE-bench Verified22.4%Not measuredOnly GPT-4 measured
SWE-bench Lite39.7%Not measuredOnly GPT-4 measured
SWE-bench (full test set)12.5%Not measuredOnly GPT-4 measured
AA Coding Index13.1Not measuredOnly GPT-4 measured
LiveCodeBench (Artificial Analysis run)Not measured70.7%Only Qwen3 30B A3B Instruct 2507 measured
SciCodeNot measured33.0%Only Qwen3 30B A3B Instruct 2507 measured
Terminal-Bench Hard (Artificial Analysis run)Not measured6.1%Only Qwen3 30B A3B Instruct 2507 measured
LMArena Text: coding12091416Qwen3 30B A3B Instruct 2507
LMArena Text: software and IT11951417Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GPT-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 30B A3B Instruct 2507Leader
AA Intelligence Index6.77.5Qwen3 30B A3B Instruct 2507
Epoch Capabilities Index125.7Not measuredOnly GPT-4 measured
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Agentic work benchmarks for GPT-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 30B A3B Instruct 2507Leader
τ²-Bench TelecomNot measured28.1%Only Qwen3 30B A3B Instruct 2507 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured41.4%Only Qwen3 30B A3B Instruct 2507 measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for GPT-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 30B A3B Instruct 2507Leader
GPQA Diamond (Artificial Analysis run)34.9%70.7%Qwen3 30B A3B Instruct 2507
Humanity's Last Exam (Artificial Analysis run)Not measured10.3%Only Qwen3 30B A3B Instruct 2507 measured
GPQA Diamond (Epoch AI run)Not measured55.6%Only Qwen3 30B A3B Instruct 2507 measured
BIG-Bench Hard75.1%Not measuredOnly GPT-4 measured
LMArena Text: hard prompts12001398Qwen3 30B A3B Instruct 2507
LMArena Text: expert11491397Qwen3 30B A3B Instruct 2507
LMArena Text: hard prompts in English12091406Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 30B A3B Instruct 2507Leader
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 AIMENot measured62.2%Only Qwen3 30B A3B Instruct 2507 measured
GSM8K92.0%Not measuredOnly GPT-4 measured
LMArena Text: math12301394Qwen3 30B A3B Instruct 2507
LMArena Text: mathematical work12281411Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GPT-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 30B A3B Instruct 2507Leader
MMLU Pro (Artificial Analysis run)56.2%80.5%Qwen3 30B A3B Instruct 2507
MMLU86.4%Not measuredOnly GPT-4 measured
AI2 Reasoning Challenge96.3%Not measuredOnly GPT-4 measured
LMArena Text: science11771393Qwen3 30B A3B Instruct 2507
LMArena Text: medicine and healthcare11451392Qwen3 30B A3B Instruct 2507
LMArena Text: legal and government11911387Qwen3 30B A3B Instruct 2507
LMArena Text: business and finance11451389Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 11 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for GPT-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 30B A3B Instruct 2507Leader
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-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 30B A3B Instruct 2507Leader
IFBench33.2%50.7%Qwen3 30B A3B Instruct 2507
Long context reasoning (AA-LCR)Not measured61.3%Only Qwen3 30B A3B Instruct 2507 measured
DTBench62.7%67.2%Qwen3 30B A3B Instruct 2507
LMCA17.1%22.4%Qwen3 30B A3B Instruct 2507
HellaSwag95.3%Not measuredOnly GPT-4 measured
WinoGrande87.5%Not measuredOnly GPT-4 measured
LMArena Text12061384Qwen3 30B A3B Instruct 2507
LMArena Text: instruction following12011363Qwen3 30B A3B Instruct 2507
LMArena Text: creative writing11911317Qwen3 30B A3B Instruct 2507
LMArena Text: English12201391Qwen3 30B A3B Instruct 2507
LMArena Text: non-English11941372Qwen3 30B A3B Instruct 2507
LMArena Text: Chinese11841432Qwen3 30B A3B Instruct 2507
LMArena Text: German11981379Qwen3 30B A3B Instruct 2507
LMArena Text: Japanese11371337Qwen3 30B A3B Instruct 2507
LMArena Text: Korean10871330Qwen3 30B A3B Instruct 2507
LMArena Text: French12201419Qwen3 30B A3B Instruct 2507
LMArena Text: Spanish11971404Qwen3 30B A3B Instruct 2507
LMArena Text: Russian11951369Qwen3 30B A3B Instruct 2507
LMArena Text: Polish12651353Qwen3 30B A3B Instruct 2507
LMArena Text: multi-turn12061378Qwen3 30B A3B Instruct 2507
LMArena Text: longer queries11901378Qwen3 30B A3B Instruct 2507
LMArena Text: writing and language12191336Qwen3 30B A3B Instruct 2507
LMArena Text: entertainment and media11841334Qwen3 30B A3B Instruct 2507
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-4 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4Qwen3 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-4$0.060Fits 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-4$1.68Exceeds the context window
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-4$6.60Exceeds 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-4 and Qwen3 30B A3B Instruct 2507
SpecificationOpenAI GPT-4Qwen3 30B A3B Instruct 2507
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedMay 28, 2023July 29, 2025
Context window8K262K
Input $/Mtok$30.00$0.09
Output $/Mtok$60.00$0.30
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4 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-4 for work measured by LMArena Text: coding or LMArena Text: software and IT, where it leads.

Which is cheaper, GPT-4 or Qwen3 30B A3B Instruct 2507?

GPT-4 costs $30.00 per million input tokens and $60.00 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-4 or Qwen3 30B A3B Instruct 2507?

Qwen3 30B A3B Instruct 2507, with 262K tokens against 8K.

How many benchmarks is the GPT-4 vs Qwen3 30B A3B Instruct 2507 comparison based on?

34 benchmarks have a result for both models as of October 22, 2026. GPT-4'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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