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GPT-5.1 vs Qwen3.8 27B

Updated October 21, 2026. 62 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GPT-5.1 Replace Qwen3.8 27B
GPT-5.1OpenAI
Coding
53.3
Overall
24.7
Output $/M
$10.00
25
Qwen3.8 27BQwen
Coding
73.6
Overall
33.7
Output $/M
$1.35
37
vs boards won of 62 shared

Decision reading

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 53.3 for GPT-5.1.

Pick GPT-5.1 when a solved task costs $1.02 against $1.26, or when what matters is LiveCodeBench (Vals AI) or LMArena Text: hard prompts in English, where it leads.

Model AGPT-5.1OpenAI53.3/1002 independent benchmarks$1.02 per solved taskModel BQwen3.8 27BQwen73.6/1002 independent benchmarks$1.26 per solved task
Shared results
62
GPT-5.1 only
38
Qwen3.8 27B only
23
Like-for-like categories
6 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 53.3 from 2 benchmarks, Qwen3.8 27B 73.6 from 2 benchmarks.
Widest category
Coding, 21.7 points apart on average over 10 shared benchmarks.
Widest single gap
LMArena WebDev: React: GPT-5.1 1307 against Qwen3.8 27B 1602, 48.9 points apart on a 0 to 100 scale.
Output price
GPT-5.1 $10.00 against Qwen3.8 27B $1.35 per million output tokens, 7.4x apart.
Context window
GPT-5.1 takes 400K tokens, Qwen3.8 27B 1M.

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-5.1BarsQwen3.8 27BTies
Coding19
Intelligence02
Reasoning110
Math02
Knowledge63
General1711
All shared boards2537
CodingLike-for-likeFour of these boards feed the Vibe Coding Index19
Coding benchmarks for GPT-5.1 and Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
SWE-bench Verified66.0%Not measuredOnly GPT-5.1 measured
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev13961593Qwen3.8 27B
AA Coding Index49.468.1Qwen3.8 27B
LiveCodeBench (Vals AI)86.5%84.0%GPT-5.1
SWE-bench Verified (Vals AI)69.8%86.0%Qwen3.8 27B
Vibe Code Bench (Vals AI)24.6%64.8%Qwen3.8 27B
Code migration (Vals AI)Not measured14.2%Only Qwen3.8 27B measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
LiveCodeBench (Artificial Analysis run)86.8%Not measuredOnly GPT-5.1 measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
Terminal-Bench Hard (Artificial Analysis run)45.5%Not measuredOnly GPT-5.1 measured
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: coding14531483Qwen3.8 27B
LMArena Text: software and IT14541478Qwen3.8 27B
LMArena WebDev: React13071602Qwen3.8 27B
LMArena WebDev: HTML14051570Qwen3.8 27B
LMArena WebDev: from an image14211577Qwen3.8 27B
Neither model has a result on the other 14 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GPT-5.1 and Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
AA Intelligence Index24.733.7Qwen3.8 27B
Epoch Capabilities Index149.8151.7Qwen3.8 27B
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index13 boards, none shared
Agentic work benchmarks for GPT-5.1 and Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B measured
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)Not measured48.5%Only Qwen3.8 27B measured
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)Not measured36.1%Only Qwen3.8 27B measured
Excel Modeling Benchmark (Vals AI)Not measured59.7%Only Qwen3.8 27B measured
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
τ²-Bench Telecom81.9%Not measuredOnly GPT-5.1 measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
DeepResearch Bench42.8%Not measuredOnly GPT-5.1 measured
Vending-Bench 2$1,473Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 26 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
Reasoning benchmarks for GPT-5.1 and Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
GPQA Diamond86.6%88.9%Qwen3.8 27B
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)83.2%83.9%Qwen3.8 27B
GPQA Diamond (Artificial Analysis run)87.3%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)28.5%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)87.6%Not measuredOnly GPT-5.1 measured
ARC-AGI72.8%87.5%Qwen3.8 27B
ARC-AGI-217.6%42.4%Qwen3.8 27B
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 prompts14571459Qwen3.8 27B
LMArena Text: expert14701479Qwen3.8 27B
LMArena Text: hard prompts in English14621461GPT-5.1
LMArena Vision: diagrams12641285Qwen3.8 27B
LMArena Vision: homework12721295Qwen3.8 27B
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GPT-5.1 and Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
AIME93.3%Not measuredOnly GPT-5.1 measured
MGSM (Vals AI)93.0%Not measuredOnly GPT-5.1 measured
AA Math Index94.0Not measuredOnly GPT-5.1 measured
AIME 2025 (Artificial Analysis run)94.0%Not measuredOnly GPT-5.1 measured
OTIS Mock AIME88.6%Not measuredOnly GPT-5.1 measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math14461455Qwen3.8 27B
LMArena Text: mathematical work14491458Qwen3.8 27B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index63
Knowledge benchmarks for GPT-5.1 and Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
MMLU Pro86.4%84.3%GPT-5.1
LegalBench (Vals AI)85.7%82.4%GPT-5.1
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%70.8%GPT-5.1
MortgageTax (Vals AI)61.4%64.9%Qwen3.8 27B
MedQA (Vals AI)96.4%Not measuredOnly GPT-5.1 measured
MedCode (Vals AI)52.7%28.7%GPT-5.1
MMLU Pro (Artificial Analysis run)87.0%Not measuredOnly GPT-5.1 measured
SimpleQA Verified48.0%Not measuredOnly GPT-5.1 measured
LMArena Text: science14591467Qwen3.8 27B
LMArena Text: medicine and healthcare14561437GPT-5.1
LMArena Text: legal and government14591447GPT-5.1
LMArena Text: business and finance14311438Qwen3.8 27B
Neither model has a result on the other 3 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Web design benchmarks for GPT-5.1 and Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
Design Arena: websites1187Not measuredOnly GPT-5.1 measured
Design Arena: UI components1166Not measuredOnly GPT-5.1 measured
Design Arena: game dev1179Not measuredOnly GPT-5.1 measured
Design Arena: data viz1204Not measuredOnly GPT-5.1 measured
Design Arena: 3D1061Not measuredOnly GPT-5.1 measured
Design Arena: all code categories1171Not measuredOnly GPT-5.1 measured
Design Arena: SVG1149Not measuredOnly GPT-5.1 measured
Design Arena: ASCII art1118Not measuredOnly GPT-5.1 measured
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index1711
General benchmarks for GPT-5.1 and Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)43.2%52.4%Qwen3.8 27B
MedScribe (Vals AI)88.1%83.8%GPT-5.1
IFBench72.9%Not measuredOnly GPT-5.1 measured
Long context reasoning (AA-LCR)80.0%82.0%Qwen3.8 27B
DTBench90.1%88.0%GPT-5.1
LMCA43.9%41.4%GPT-5.1
CL-bench23.7%Not measuredOnly GPT-5.1 measured
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
CL-bench Life17.3%Not measuredOnly GPT-5.1 measured
LMArena Text14431440GPT-5.1
LMArena Text: instruction following14431436GPT-5.1
LMArena Text: creative writing14271383GPT-5.1
LMArena Vision12501269Qwen3.8 27B
LMArena Text: English14511448GPT-5.1
LMArena Text: non-English14311428GPT-5.1
LMArena Text: Chinese14951501Qwen3.8 27B
LMArena Text: German14381443Qwen3.8 27B
LMArena Text: Japanese14531387GPT-5.1
LMArena Text: Korean14011384GPT-5.1
LMArena Text: French14501465Qwen3.8 27B
LMArena Text: Spanish14331449Qwen3.8 27B
LMArena Text: Russian14351415GPT-5.1
LMArena Text: Polish14401439GPT-5.1
LMArena Text: multi-turn14501440GPT-5.1
LMArena Text: longer queries14481448Qwen3.8 27B
LMArena Text: writing and language14281405GPT-5.1
LMArena Text: entertainment and media14151393GPT-5.1
LMArena Vision: English12521267Qwen3.8 27B
LMArena Vision: Chinese12701326Qwen3.8 27B
LMArena Vision: reading text12551281Qwen3.8 27B
LMArena Vision: humor12631235GPT-5.1
LMArena Vision: captioning1252Not measuredOnly GPT-5.1 measured
LMArena Vision: recognizing things1232Not measuredOnly GPT-5.1 measured
LMArena Vision: creative writing12661237GPT-5.1
LMArena Document1403Not measuredOnly GPT-5.1 measured
Neither model has a result on the other 10 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 Qwen3.8 27B
BenchmarkGPT-5.1Qwen3.8 27BLeader
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
Qwen3.8 27B$0.0007Fits in one request

Qwen3.8 27B costs less

Repository review

50K input + 3K output tokens

GPT-5.1$0.092Fits in one request
Qwen3.8 27B$0.0060Fits in one request

Qwen3.8 27B costs less

Agent loop

200K input + 10K output tokens

GPT-5.1$0.350Fits in one request
Qwen3.8 27B$0.021Fits in one request

Qwen3.8 27B 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 Qwen3.8 27B
SpecificationOpenAI GPT-5.1Qwen3.8 27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedNovember 13, 2025August 14, 2026
Context window400K1M
Input $/Mtok$1.25$0.04
Output $/Mtok$10.00$1.35
Coding index53.373.6
Independent measurements behind it22
Cost per solved task$1.02$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.1 better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 53.3 for GPT-5.1. Pick GPT-5.1 when a solved task costs $1.02 against $1.26, or when what matters is LiveCodeBench (Vals AI) or LMArena Text: hard prompts in English, where it leads.

Which is cheaper, GPT-5.1 or Qwen3.8 27B?

GPT-5.1 costs $1.25 per million input tokens and $10.00 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Per solved SWE-bench Verified task, GPT-5.1 costs $1.02 and Qwen3.8 27B $1.26.

Which has the bigger context window, GPT-5.1 or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 400K.

How many benchmarks is the GPT-5.1 vs Qwen3.8 27B comparison based on?

62 benchmarks have a result for both models as of October 21, 2026. GPT-5.1's coding index rests on 2 independent benchmarks and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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