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GPT-5.6 Luna vs Qwen3.8 27B

Qwen3.8 27B leads GPT-5.6 Luna on coding, 74.2 to 58.0 on the Vibe Coding Index, and GPT-5.6 Luna costs 2.1x less per output token ($1.20 vs $2.55 per million). Updated October 22, 2026; 84 benchmarks have a result for both.

Replace GPT-5.6 Luna Replace Qwen3.8 27B
GPT-5.6 LunaOpenAI
Coding
58.0
Overall
61.6
Output $/M
$1.20
48
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
34
vs boards won of 84 shared, 2 tied

GPT-5.6 Luna or Qwen3.8 27B: which to pick

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 4 benchmarks against 6.

Pick GPT-5.6 Luna for output tokens 2.1x cheaper ($1.20 vs $2.55 per million) and a lower cost per solved task ($0.04 against $1.26).

Model AGPT-5.6 LunaOpenAI58.0/1006 independent benchmarks$0.04 per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
84
GPT-5.6 Luna only
36
Qwen3.8 27B only
1
Like-for-like categories
8 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-5.6 Luna 58.0 from 6 benchmarks, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Math, 15.6 points apart on average over 3 shared benchmarks.
Widest single gap
ProofBench: GPT-5.6 Luna 60.0% against Qwen3.8 27B 16.0%, 44.0 points apart on a 0 to 100 scale.
Output price
GPT-5.6 Luna $1.20 against Qwen3.8 27B $2.55 per million output tokens, 2.1x apart.
Context window
GPT-5.6 Luna takes 1.05M 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-5.6 LunaBarsQwen3.8 27BTies
Coding971 tie
Intelligence20
Agentic work541 tie
Reasoning75
Math30
Knowledge63
Web design10
General1515
All shared boards48342 ties
CodingLike-for-likeSix of these boards feed the Vibe Coding Index97
Coding benchmarks for GPT-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 27BLeader
LiveBench coding73.6%86.8%Qwen3.8 27B
LMArena WebDev15181593Qwen3.8 27B
AA Coding Index71.468.1GPT-5.6 Luna
LiveCodeBench (Vals AI)Not measured84.0%Only Qwen3.8 27B measured
SWE-bench Verified (Vals AI)93.0%86.0%GPT-5.6 Luna
Terminal-Bench 4 (Vals AI)11.6%Not measuredOnly GPT-5.6 Luna measured
Vibe Code Bench (Vals AI)77.1%64.8%GPT-5.6 Luna
Code migration (Vals AI)44.5%14.2%GPT-5.6 Luna
ProgramBench (Vals AI)0.0%0.0%Tie
IOI (Vals AI)61.8%39.1%GPT-5.6 Luna
Terminal-Bench 2.1 (Vals AI)79.0%58.4%GPT-5.6 Luna
Vibe Code Bench 1-100 (Vals AI)22.6%Not measuredOnly GPT-5.6 Luna measured
SkillsBench (Vals AI)60.5%38.1%GPT-5.6 Luna
CyberBench (Vals AI)73.6%67.8%GPT-5.6 Luna
SciCode53.6%46.6%GPT-5.6 Luna
FrontierCode39.8%Not measuredOnly GPT-5.6 Luna measured
WeirdML60.9%Not measuredOnly GPT-5.6 Luna measured
ALE-Bench1667.4Not measuredOnly GPT-5.6 Luna measured
LMArena Text: coding14661483Qwen3.8 27B
LMArena Text: software and IT14631478Qwen3.8 27B
LMArena WebDev: React15081602Qwen3.8 27B
LMArena WebDev: HTML15201570Qwen3.8 27B
LMArena WebDev: from an image15171577Qwen3.8 27B
SciCode6.2%Not measuredOnly GPT-5.6 Luna measured
Neither model has a result on the other 19 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for GPT-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 27BLeader
AA Intelligence Index37.333.7GPT-5.6 Luna
Epoch Capabilities Index156.5151.7GPT-5.6 Luna
Agentic workLike-for-likePublic boards, shown as published, kept out of the index54
Agentic work benchmarks for GPT-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 27BLeader
AA Agentic Index42.145.8Qwen3.8 27B
LiveBench research51.0%59.4%Qwen3.8 27B
LiveBench browser68.0%32.0%GPT-5.6 Luna
LiveBench long agentic30.4%27.9%GPT-5.6 Luna
SREBench (Vals AI)1.5%Not measuredOnly GPT-5.6 Luna measured
Finance Agent (Vals AI)55.0%48.5%GPT-5.6 Luna
Tax Agent Bench (Vals AI)24.8%30.9%Qwen3.8 27B
Legal Research Bench (Vals AI)36.5%36.1%GPT-5.6 Luna
Excel Modeling Benchmark (Vals AI)67.1%59.7%GPT-5.6 Luna
BioMysteryBench (Vals AI)61.5%Not measuredOnly GPT-5.6 Luna measured
Terminal-Bench-Science (Vals AI)0.0%0.0%Tie
Vals Science Index25.3%Not measuredOnly GPT-5.6 Luna measured
APEX-Agents43.0%47.5%Qwen3.8 27B
BALROG45.6%Not measuredOnly GPT-5.6 Luna measured
Vending-Bench 2$4,095Not measuredOnly GPT-5.6 Luna measured
Neither model has a result on the other 27 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index75
Reasoning benchmarks for GPT-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 27BLeader
GPQA Diamond91.7%88.9%GPT-5.6 Luna
LiveBench reasoning85.6%80.0%GPT-5.6 Luna
MysteryMechanism (Vals AI)14.4%Not measuredOnly GPT-5.6 Luna measured
MMMU Pro (Vals AI)85.0%83.9%GPT-5.6 Luna
GPQA Diamond (Artificial Analysis run)91.1%90.5%GPT-5.6 Luna
Humanity's Last Exam (Artificial Analysis run)39.5%33.9%GPT-5.6 Luna
GPQA Diamond (Epoch AI run)91.6%Not measuredOnly GPT-5.6 Luna measured
ARC-AGI88.0%87.5%GPT-5.6 Luna
ARC-AGI-259.5%42.4%GPT-5.6 Luna
SimpleBench46.8%Not measuredOnly GPT-5.6 Luna measured
Chess puzzles43.0%Not measuredOnly GPT-5.6 Luna measured
Mystery game puzzles21.0%Not measuredOnly GPT-5.6 Luna measured
LMArena Text: hard prompts14511459Qwen3.8 27B
LMArena Text: expert14781479Qwen3.8 27B
LMArena Text: hard prompts in English14601461Qwen3.8 27B
LMArena Vision: diagrams12731285Qwen3.8 27B
LMArena Vision: homework12821295Qwen3.8 27B
Kagi LLM Benchmark49.1%Not measuredOnly GPT-5.6 Luna measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for GPT-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 27BLeader
OTIS Mock AIME98.3%Not measuredOnly GPT-5.6 Luna measured
FrontierMath, tiers 1 to 382.1%Not measuredOnly GPT-5.6 Luna measured
FrontierMath, tier 461.0%Not measuredOnly GPT-5.6 Luna measured
ProofBench60.0%16.0%GPT-5.6 Luna
LMArena Text: math14591455GPT-5.6 Luna
LMArena Text: mathematical work14701458GPT-5.6 Luna
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index63
Knowledge benchmarks for GPT-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 27BLeader
MMLU Pro86.0%84.3%GPT-5.6 Luna
LegalBench (Vals AI)84.0%82.4%GPT-5.6 Luna
CorpFin (Vals AI)64.2%Not measuredOnly GPT-5.6 Luna measured
TaxEval (Vals AI)76.2%70.8%GPT-5.6 Luna
MortgageTax (Vals AI)67.3%64.9%GPT-5.6 Luna
MedCode (Vals AI)42.4%28.7%GPT-5.6 Luna
SimpleQA Verified41.0%Not measuredOnly GPT-5.6 Luna measured
LMArena Text: science14331467Qwen3.8 27B
LMArena Text: medicine and healthcare13991437Qwen3.8 27B
LMArena Text: legal and government14461447Qwen3.8 27B
LMArena Text: business and finance14391438GPT-5.6 Luna
FACTS Benchmark Suite35.0%Not measuredOnly GPT-5.6 Luna measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index10
Web design benchmarks for GPT-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 27BLeader
Design Arena: websites1255Not measuredOnly GPT-5.6 Luna measured
Design Arena: UI components1248Not measuredOnly GPT-5.6 Luna measured
Design Arena: game dev1195Not measuredOnly GPT-5.6 Luna measured
Design Arena: data viz1212Not measuredOnly GPT-5.6 Luna measured
Design Arena: 3D1121Not measuredOnly GPT-5.6 Luna measured
Design Arena: all code categories1230Not measuredOnly GPT-5.6 Luna measured
Design Arena: SVG1193Not measuredOnly GPT-5.6 Luna measured
Design Arena: ASCII art1142Not measuredOnly GPT-5.6 Luna measured
LiveBench design76.0%75.9%GPT-5.6 Luna
GeneralLike-for-likePublic boards, shown as published, kept out of the index1515
General benchmarks for GPT-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 27BLeader
LiveBench data analysis78.0%76.6%GPT-5.6 Luna
Public Benefits Bench (Vals AI)61.2%Not measuredOnly GPT-5.6 Luna measured
SAGE (Vals AI)44.2%52.4%Qwen3.8 27B
MedScribe (Vals AI)84.4%83.8%GPT-5.6 Luna
Vals Multimodal Index69.1%Not measuredOnly GPT-5.6 Luna measured
Long context reasoning (AA-LCR)83.7%82.0%GPT-5.6 Luna
DTBench89.1%88.0%GPT-5.6 Luna
LMCA48.5%41.4%GPT-5.6 Luna
Surface Evolver Bench61.9%45.0%GPT-5.6 Luna
Furniture assembly42.5%Not measuredOnly GPT-5.6 Luna measured
LMArena Text14311440Qwen3.8 27B
LMArena Text: instruction following14371436GPT-5.6 Luna
LMArena Text: creative writing13961383GPT-5.6 Luna
LMArena Vision12571269Qwen3.8 27B
LMArena Text: English14411448Qwen3.8 27B
LMArena Text: non-English14171428Qwen3.8 27B
LMArena Text: Chinese14701501Qwen3.8 27B
LMArena Text: German14551443GPT-5.6 Luna
LMArena Text: Japanese14151387GPT-5.6 Luna
LMArena Text: Korean14151384GPT-5.6 Luna
LMArena Text: French14581465Qwen3.8 27B
LMArena Text: Spanish14461449Qwen3.8 27B
LMArena Text: Russian14271415GPT-5.6 Luna
LMArena Text: Polish14051439Qwen3.8 27B
LMArena Text: multi-turn14341440Qwen3.8 27B
LMArena Text: longer queries14361448Qwen3.8 27B
LMArena Text: writing and language14191405GPT-5.6 Luna
LMArena Text: entertainment and media14021393GPT-5.6 Luna
LMArena Vision: English12631267Qwen3.8 27B
LMArena Vision: Chinese12941326Qwen3.8 27B
LMArena Vision: reading text12641281Qwen3.8 27B
LMArena Vision: humor11901235Qwen3.8 27B
LMArena Vision: creative writing12571237GPT-5.6 Luna
LMArena Document1457Not measuredOnly GPT-5.6 Luna measured
EQ-Bench 41156Not measuredOnly GPT-5.6 Luna 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-5.6 Luna and Qwen3.8 27B
BenchmarkGPT-5.6 LunaQwen3.8 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-5.6 Luna$0.0008Fits in one request
Qwen3.8 27B$0.0017Fits in one request

GPT-5.6 Luna costs less

Repository review

50K input + 3K output tokens

GPT-5.6 Luna$0.014Fits in one request
Qwen3.8 27B$0.029Fits in one request

GPT-5.6 Luna costs less

Agent loop

200K input + 10K output tokens

GPT-5.6 Luna$0.052Fits in one request
Qwen3.8 27B$0.111Fits in one request

GPT-5.6 Luna 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-5.6 Luna and Qwen3.8 27B
SpecificationOpenAI GPT-5.6 LunaQwen3.8 27B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedJuly 9, 2026August 14, 2026
Context window1.05M1M
Input $/Mtok$0.20$0.425
Output $/Mtok$1.20$2.55
Coding index58.074.2
Independent measurements behind it64
Cost per solved task$0.04$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is GPT-5.6 Luna better than Qwen3.8 27B for coding?

Pick Qwen3.8 27B for coding: it scores 74.2 on the Vibe Coding Index against 58.0 for GPT-5.6 Luna, though on 4 benchmarks against 6. Pick GPT-5.6 Luna for output tokens 2.1x cheaper ($1.20 vs $2.55 per million) and a lower cost per solved task ($0.04 against $1.26).

Which is cheaper, GPT-5.6 Luna or Qwen3.8 27B?

GPT-5.6 Luna costs $0.20 per million input tokens and $1.20 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Per solved SWE-bench Verified task, GPT-5.6 Luna costs $0.04 and Qwen3.8 27B $1.26.

Which has the bigger context window, GPT-5.6 Luna or Qwen3.8 27B?

GPT-5.6 Luna, with 1.05M tokens against 1M.

How many benchmarks is the GPT-5.6 Luna vs Qwen3.8 27B comparison based on?

84 benchmarks have a result for both models as of October 22, 2026. GPT-5.6 Luna's coding index rests on 6 independent benchmarks and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.

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