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GPT-5.1 vs Qwen3 30B A3B Instruct 2507

GPT-5.1 leads Qwen3 30B A3B Instruct 2507 on the Artificial Analysis Intelligence Index, 24.7 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 22, 2026; 43 benchmarks have a result for both.

Replace GPT-5.1 Replace Qwen3 30B A3B Instruct 2507
GPT-5.1OpenAI
Coding
40.4
Overall
55.0
Output $/M
$10.00
43
Qwen3 30B A3B Instruct 2507Qwen
Coding
No data
Overall
41.5
Output $/M
$0.30
0
vs boards won of 43 shared

GPT-5.1 or Qwen3 30B A3B Instruct 2507: which to pick

Only GPT-5.1 has a coding score, 40.4 on the Vibe Coding Index; Qwen3 30B A3B Instruct 2507 has none, so the two cannot be ranked on coding.

Pick Qwen3 30B A3B Instruct 2507 for output tokens 33.3x cheaper ($0.30 vs $10.00 per million).

Model AGPT-5.1OpenAI40.4/1003 independent benchmarks$1.02 per solved taskModel BQwen3 30B A3B Instruct 2507QwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
43
GPT-5.1 only
63
Qwen3 30B A3B Instruct 2507 only
2
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-5.1 40.4 from 3 benchmarks, Qwen3 30B A3B Instruct 2507 not benchmarked.
Widest category
Agentic work, 53.8 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GPT-5.1 81.9% against Qwen3 30B A3B Instruct 2507 28.1%, 53.8 points apart on a 0 to 100 scale.
Output price
GPT-5.1 $10.00 against Qwen3 30B A3B Instruct 2507 $0.30 per million output tokens, 33.3x apart.
Context window
GPT-5.1 takes 400K 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-5.1BarsQwen3 30B A3B Instruct 2507Ties
Coding40
Intelligence10
Agentic work10
Reasoning60
Math50
Knowledge50
General210
All shared boards430
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 30B A3B Instruct 2507Leader
SWE-bench Verified66.0%Not measuredOnly GPT-5.1 measured
LMArena WebDev1396Not measuredOnly GPT-5.1 measured
AA Coding Index49.4Not measuredOnly GPT-5.1 measured
LiveCodeBench (Vals AI)86.5%Not measuredOnly GPT-5.1 measured
SWE-bench Verified (Vals AI)69.8%Not measuredOnly GPT-5.1 measured
Vibe Code Bench (Vals AI)24.6%Not measuredOnly GPT-5.1 measured
LiveCodeBench (Artificial Analysis run)86.8%70.7%GPT-5.1
SciCodeNot measured33.0%Only Qwen3 30B A3B Instruct 2507 measured
Terminal-Bench Hard (Artificial Analysis run)45.5%6.1%GPT-5.1
Terminal-Bench47.6%Not measuredOnly GPT-5.1 measured
GSO-Bench13.7%Not measuredOnly GPT-5.1 measured
WeirdML60.8%Not measuredOnly GPT-5.1 measured
LMArena Text: coding14531416GPT-5.1
LMArena Text: software and IT14541417GPT-5.1
LMArena WebDev: React1307Not measuredOnly GPT-5.1 measured
LMArena WebDev: HTML1405Not measuredOnly GPT-5.1 measured
LMArena WebDev: from an image1421Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 30B A3B Instruct 2507Leader
AA Intelligence Index24.77.5GPT-5.1
Epoch Capabilities Index149.8Not measuredOnly GPT-5.1 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 30B A3B Instruct 2507Leader
τ²-Bench Telecom81.9%28.1%GPT-5.1
DeepResearch Bench42.8%Not measuredOnly GPT-5.1 measured
Vending-Bench 2$1,473Not measuredOnly GPT-5.1 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured41.4%Only Qwen3 30B A3B Instruct 2507 measured
MCP Atlas50.1%Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 30B A3B Instruct 2507Leader
GPQA Diamond86.6%Not measuredOnly GPT-5.1 measured
MMMU Pro (Vals AI)83.2%Not measuredOnly GPT-5.1 measured
GPQA Diamond (Artificial Analysis run)87.3%70.7%GPT-5.1
Humanity's Last Exam (Artificial Analysis run)28.5%10.3%GPT-5.1
GPQA Diamond (Epoch AI run)87.6%55.6%GPT-5.1
ARC-AGI72.8%Not measuredOnly GPT-5.1 measured
ARC-AGI-217.6%Not measuredOnly GPT-5.1 measured
SimpleBench53.2%Not measuredOnly GPT-5.1 measured
Chess puzzles42.0%Not measuredOnly GPT-5.1 measured
Mystery game puzzles19.0%Not measuredOnly GPT-5.1 measured
VPCT58.7%Not measuredOnly GPT-5.1 measured
LMArena Text: hard prompts14571398GPT-5.1
LMArena Text: expert14701397GPT-5.1
LMArena Text: hard prompts in English14621406GPT-5.1
LMArena Vision: diagrams1264Not measuredOnly GPT-5.1 measured
LMArena Vision: homework1272Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 7 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index50
Math benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 30B A3B Instruct 2507Leader
AIME93.3%Not measuredOnly GPT-5.1 measured
MGSM (Vals AI)93.0%Not measuredOnly GPT-5.1 measured
AA Math Index94.066.3GPT-5.1
AIME 2025 (Artificial Analysis run)94.0%66.3%GPT-5.1
OTIS Mock AIME88.6%62.2%GPT-5.1
LMArena Text: math14461394GPT-5.1
LMArena Text: mathematical work14491411GPT-5.1
MathArena17.3%Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 30B A3B Instruct 2507Leader
MMLU Pro86.4%Not measuredOnly GPT-5.1 measured
LegalBench (Vals AI)85.7%Not measuredOnly GPT-5.1 measured
CaseLaw (Vals AI)73.4%Not measuredOnly GPT-5.1 measured
CorpFin (Vals AI)63.8%Not measuredOnly GPT-5.1 measured
TaxEval (Vals AI)74.9%Not measuredOnly GPT-5.1 measured
MortgageTax (Vals AI)61.4%Not measuredOnly GPT-5.1 measured
MedQA (Vals AI)96.4%Not measuredOnly GPT-5.1 measured
MedCode (Vals AI)52.7%Not measuredOnly GPT-5.1 measured
MMLU Pro (Artificial Analysis run)87.0%80.5%GPT-5.1
SimpleQA Verified48.0%Not measuredOnly GPT-5.1 measured
LMArena Text: science14591393GPT-5.1
LMArena Text: medicine and healthcare14561392GPT-5.1
LMArena Text: legal and government14591387GPT-5.1
LMArena Text: business and finance14311389GPT-5.1
FACTS Benchmark Suite45.0%Not measuredOnly GPT-5.1 measured
PRBench Finance48.0%Not measuredOnly GPT-5.1 measured
PRBench Legal49.3%Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 3 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 30B A3B Instruct 2507Leader
Design Arena: websites1187Not measuredOnly GPT-5.1 measured
Design Arena: UI components1164Not measuredOnly GPT-5.1 measured
Design Arena: game dev1178Not measuredOnly GPT-5.1 measured
Design Arena: data viz1205Not measuredOnly GPT-5.1 measured
Design Arena: 3D1060Not measuredOnly GPT-5.1 measured
Design Arena: all code categories1170Not measuredOnly GPT-5.1 measured
Design Arena: SVG1148Not measuredOnly GPT-5.1 measured
Design Arena: ASCII art1118Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index210
General benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 30B A3B Instruct 2507Leader
SAGE (Vals AI)43.2%Not measuredOnly GPT-5.1 measured
MedScribe (Vals AI)88.1%Not measuredOnly GPT-5.1 measured
IFBench72.9%50.7%GPT-5.1
Long context reasoning (AA-LCR)80.0%61.3%GPT-5.1
DTBench90.1%67.2%GPT-5.1
LMCA43.9%22.4%GPT-5.1
CL-bench23.7%Not measuredOnly GPT-5.1 measured
CL-bench Life17.3%Not measuredOnly GPT-5.1 measured
LMArena Text14431384GPT-5.1
LMArena Text: instruction following14431363GPT-5.1
LMArena Text: creative writing14271317GPT-5.1
LMArena Vision1250Not measuredOnly GPT-5.1 measured
LMArena Text: English14511391GPT-5.1
LMArena Text: non-English14311372GPT-5.1
LMArena Text: Chinese14951432GPT-5.1
LMArena Text: German14381379GPT-5.1
LMArena Text: Japanese14531337GPT-5.1
LMArena Text: Korean14011330GPT-5.1
LMArena Text: French14501419GPT-5.1
LMArena Text: Spanish14331404GPT-5.1
LMArena Text: Russian14351369GPT-5.1
LMArena Text: Polish14401353GPT-5.1
LMArena Text: multi-turn14501378GPT-5.1
LMArena Text: longer queries14481378GPT-5.1
LMArena Text: writing and language14281336GPT-5.1
LMArena Text: entertainment and media14151334GPT-5.1
LMArena Vision: English1252Not measuredOnly GPT-5.1 measured
LMArena Vision: Chinese1270Not measuredOnly GPT-5.1 measured
LMArena Vision: reading text1255Not measuredOnly GPT-5.1 measured
LMArena Vision: humor1263Not measuredOnly GPT-5.1 measured
LMArena Vision: captioning1252Not measuredOnly GPT-5.1 measured
LMArena Vision: recognizing things1232Not measuredOnly GPT-5.1 measured
LMArena Vision: creative writing1266Not measuredOnly GPT-5.1 measured
LMArena Document1403Not measuredOnly GPT-5.1 measured
MultiChallenge63.4%Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GPT-5.1 and Qwen3 30B A3B Instruct 2507
BenchmarkGPT-5.1Qwen3 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-5.1$0.0063Fits 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-5.1$0.092Fits 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-5.1$0.350Fits 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-5.1 and Qwen3 30B A3B Instruct 2507
SpecificationOpenAI GPT-5.1Qwen3 30B A3B Instruct 2507
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedNovember 13, 2025July 29, 2025
Context window400K262K
Input $/Mtok$1.25$0.09
Output $/Mtok$10.00$0.30
Coding index40.4Not benchmarked
Independent measurements behind it30
Cost per solved task$1.02Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.1 better than Qwen3 30B A3B Instruct 2507 for coding?

Only GPT-5.1 has a coding score, 40.4 on the Vibe Coding Index; Qwen3 30B A3B Instruct 2507 has none, so the two cannot be ranked on coding. Pick Qwen3 30B A3B Instruct 2507 for output tokens 33.3x cheaper ($0.30 vs $10.00 per million).

Which is cheaper, GPT-5.1 or Qwen3 30B A3B Instruct 2507?

GPT-5.1 costs $1.25 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 GPT-5.1 only.

Which has the bigger context window, GPT-5.1 or Qwen3 30B A3B Instruct 2507?

GPT-5.1, with 400K tokens against 262K.

How many benchmarks is the GPT-5.1 vs Qwen3 30B A3B Instruct 2507 comparison based on?

43 benchmarks have a result for both models as of October 22, 2026. GPT-5.1's coding index rests on 3 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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