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GPT-4.1 vs Qwen3 30B A3B Instruct 2507

GPT-4.1 leads Qwen3 30B A3B Instruct 2507 on the Artificial Analysis Intelligence Index, 12.7 to 7.5, and Qwen3 30B A3B Instruct 2507 costs 26.7x less per output token ($0.30 vs $8.00 per million). Updated October 22, 2026; 44 benchmarks have a result for both.

Replace GPT-4.1 Replace Qwen3 30B A3B Instruct 2507
GPT-4.1OpenAI
Coding
No data
Overall
36.5
Output $/M
$8.00
21
Qwen3 30B A3B Instruct 2507Qwen
Coding
No data
Overall
41.5
Output $/M
$0.30
23
vs boards won of 44 shared

GPT-4.1 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-4.1 for a larger context window (1.05M against 262K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model AGPT-4.1OpenAINot benchmarkedNo coding benchmark$0.37 per solved taskModel BQwen3 30B A3B Instruct 2507QwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
44
GPT-4.1 only
48
Qwen3 30B A3B Instruct 2507 only
1
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-4.1 not benchmarked, Qwen3 30B A3B Instruct 2507 not benchmarked.
Widest category
Math, 20.0 points apart on average over 5 shared benchmarks.
Widest single gap
AIME 2025 (Artificial Analysis run): GPT-4.1 34.7% against Qwen3 30B A3B Instruct 2507 66.3%, 31.7 points apart on a 0 to 100 scale.
Output price
GPT-4.1 $8.00 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 26.7x apart.
Context window
GPT-4.1 takes 1.05M 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-4.1BarsQwen3 30B A3B Instruct 2507Ties
Coding13
Intelligence10
Agentic work20
Reasoning15
Math05
Knowledge23
General147
All shared boards2123
CodingLike-for-likeSix of these boards feed the Vibe Coding Index13
Coding benchmarks for GPT-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 30B A3B Instruct 2507Leader
Aider polyglot52.4%Not measuredOnly GPT-4.1 measured
SWE-bench Verified39.6%Not measuredOnly GPT-4.1 measured
SWE-bench Multimodal31.1%Not measuredOnly GPT-4.1 measured
LiveCodeBench (Vals AI)54.7%Not measuredOnly GPT-4.1 measured
LiveCodeBench (Artificial Analysis run)45.7%70.7%Qwen3 30B A3B Instruct 2507
SciCodeNot measured33.0%Only Qwen3 30B A3B Instruct 2507 measured
Terminal-Bench Hard (Artificial Analysis run)13.6%6.1%GPT-4.1
WeirdML39.0%Not measuredOnly GPT-4.1 measured
ALE-Bench558.1Not measuredOnly GPT-4.1 measured
LMArena Text: coding13901416Qwen3 30B A3B Instruct 2507
LMArena Text: software and IT13911417Qwen3 30B A3B Instruct 2507
SciCode4.6%Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 30B A3B Instruct 2507Leader
AA Intelligence Index12.77.5GPT-4.1
Epoch Capabilities Index136.7Not measuredOnly GPT-4.1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for GPT-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 30B A3B Instruct 2507Leader
τ²-Bench Telecom47.1%28.1%GPT-4.1
Berkeley Function Calling Leaderboard (BFCL)54.0%41.4%GPT-4.1
BrowseComp-Plus36.4%Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index15
Reasoning benchmarks for GPT-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 30B A3B Instruct 2507Leader
GPQA Diamond65.4%Not measuredOnly GPT-4.1 measured
MMMU Pro (Vals AI)72.4%Not measuredOnly GPT-4.1 measured
GPQA Diamond (Artificial Analysis run)66.6%70.7%Qwen3 30B A3B Instruct 2507
Humanity's Last Exam (Artificial Analysis run)4.2%10.3%Qwen3 30B A3B Instruct 2507
GPQA Diamond (Epoch AI run)66.9%55.6%GPT-4.1
ARC-AGI5.5%Not measuredOnly GPT-4.1 measured
ARC-AGI-20.4%Not measuredOnly GPT-4.1 measured
SimpleBench27.0%Not measuredOnly GPT-4.1 measured
Chess puzzles3.0%Not measuredOnly GPT-4.1 measured
LMArena Text: hard prompts13841398Qwen3 30B A3B Instruct 2507
LMArena Text: expert13641397Qwen3 30B A3B Instruct 2507
LMArena Text: hard prompts in English13931406Qwen3 30B A3B Instruct 2507
LMArena Vision: diagrams1215Not measuredOnly GPT-4.1 measured
LMArena Vision: homework1248Not measuredOnly GPT-4.1 measured
Kagi LLM Benchmark52.3%Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index05
Math benchmarks for GPT-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 30B A3B Instruct 2507Leader
AIME39.6%Not measuredOnly GPT-4.1 measured
MATH 500 (Vals AI)87.2%Not measuredOnly GPT-4.1 measured
MGSM (Vals AI)87.7%Not measuredOnly GPT-4.1 measured
AA Math Index34.766.3Qwen3 30B A3B Instruct 2507
AIME 2025 (Artificial Analysis run)34.7%66.3%Qwen3 30B A3B Instruct 2507
OTIS Mock AIME38.3%62.2%Qwen3 30B A3B Instruct 2507
FrontierMath, tiers 1 to 36.0%Not measuredOnly GPT-4.1 measured
MATH level 583.0%Not measuredOnly GPT-4.1 measured
LMArena Text: math13691394Qwen3 30B A3B Instruct 2507
LMArena Text: mathematical work13601411Qwen3 30B A3B Instruct 2507
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index23
Knowledge benchmarks for GPT-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 30B A3B Instruct 2507Leader
MMLU Pro80.5%Not measuredOnly GPT-4.1 measured
LegalBench (Vals AI)83.1%Not measuredOnly GPT-4.1 measured
CaseLaw (Vals AI)69.9%Not measuredOnly GPT-4.1 measured
CorpFin (Vals AI)63.0%Not measuredOnly GPT-4.1 measured
TaxEval (Vals AI)75.1%Not measuredOnly GPT-4.1 measured
MortgageTax (Vals AI)65.9%Not measuredOnly GPT-4.1 measured
MedQA (Vals AI)91.2%Not measuredOnly GPT-4.1 measured
MMLU Pro (Artificial Analysis run)80.6%80.5%GPT-4.1
SimpleQA Verified31.1%Not measuredOnly GPT-4.1 measured
GeoBench72.0%Not measuredOnly GPT-4.1 measured
LMArena Text: science13821393Qwen3 30B A3B Instruct 2507
LMArena Text: medicine and healthcare13791392Qwen3 30B A3B Instruct 2507
LMArena Text: legal and government13991387GPT-4.1
LMArena Text: business and finance13691389Qwen3 30B A3B Instruct 2507
FACTS Benchmark Suite45.8%Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 3 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for GPT-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 30B A3B Instruct 2507Leader
Design Arena: websites1038Not measuredOnly GPT-4.1 measured
Design Arena: UI components997Not measuredOnly GPT-4.1 measured
Design Arena: game dev1079Not measuredOnly GPT-4.1 measured
Design Arena: data viz1104Not measuredOnly GPT-4.1 measured
Design Arena: 3D849Not measuredOnly GPT-4.1 measured
Design Arena: all code categories1025Not measuredOnly GPT-4.1 measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index147
General benchmarks for GPT-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 30B A3B Instruct 2507Leader
IFBench43.0%50.7%Qwen3 30B A3B Instruct 2507
Long context reasoning (AA-LCR)68.3%61.3%GPT-4.1
DTBench68.3%67.2%GPT-4.1
LMCA25.6%22.4%GPT-4.1
Fiction.LiveBench63.9%Not measuredOnly GPT-4.1 measured
LMArena Text13831384Qwen3 30B A3B Instruct 2507
LMArena Text: instruction following13671363GPT-4.1
LMArena Text: creative writing13631317GPT-4.1
LMArena Vision1210Not measuredOnly GPT-4.1 measured
LMArena Text: English13921391GPT-4.1
LMArena Text: non-English13701372Qwen3 30B A3B Instruct 2507
LMArena Text: Chinese13821432Qwen3 30B A3B Instruct 2507
LMArena Text: German13811379GPT-4.1
LMArena Text: Japanese13191337Qwen3 30B A3B Instruct 2507
LMArena Text: Korean13381330GPT-4.1
LMArena Text: French13821419Qwen3 30B A3B Instruct 2507
LMArena Text: Spanish13761404Qwen3 30B A3B Instruct 2507
LMArena Text: Russian13771369GPT-4.1
LMArena Text: Polish13891353GPT-4.1
LMArena Text: multi-turn13971378GPT-4.1
LMArena Text: longer queries13851378GPT-4.1
LMArena Text: writing and language13711336GPT-4.1
LMArena Text: entertainment and media13651334GPT-4.1
LMArena Vision: English1209Not measuredOnly GPT-4.1 measured
LMArena Vision: Chinese1226Not measuredOnly GPT-4.1 measured
LMArena Vision: reading text1218Not measuredOnly GPT-4.1 measured
LMArena Vision: humor1196Not measuredOnly GPT-4.1 measured
LMArena Vision: captioning1207Not measuredOnly GPT-4.1 measured
LMArena Vision: recognizing things1208Not measuredOnly GPT-4.1 measured
LMArena Vision: creative writing1210Not measuredOnly GPT-4.1 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-4.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-4.1Qwen3 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-4.1$0.0060Fits 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$0.124Fits 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-4.1$0.480Fits in one request
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-4.1 and Qwen3 30B A3B Instruct 2507
SpecificationOpenAI GPT-4.1Qwen3 30B A3B Instruct 2507
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 14, 2025July 29, 2025
Context window1.05M262K
Input $/Mtok$2.00$0.09
Output $/Mtok$8.00$0.30
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved task$0.37Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-4.1 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-4.1 for a larger context window (1.05M against 262K tokens) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, GPT-4.1 or Qwen3 30B A3B Instruct 2507?

GPT-4.1 costs $2.00 per million input tokens and $8.00 per million output tokens; Qwen3 30B A3B Instruct 2507 costs $0.09 and $0.30. Cost per solved task is known for GPT-4.1 only.

Which has the bigger context window, GPT-4.1 or Qwen3 30B A3B Instruct 2507?

GPT-4.1, with 1.05M tokens against 262K.

How many benchmarks is the GPT-4.1 vs Qwen3 30B A3B Instruct 2507 comparison based on?

44 benchmarks have a result for both models as of October 22, 2026. GPT-4.1'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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