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Llama 4 Maverick vs Qwen3.6 27B

Qwen3.6 27B leads Llama 4 Maverick on the Artificial Analysis Intelligence Index, 21.4 to 9.3, and Llama 4 Maverick costs 2.7x less per output token ($1.00 vs $2.70 per million). Updated October 22, 2026; 17 benchmarks have a result for both.

Llama 4 MaverickMeta Llama
Coding
No data
Overall
21.8
Output $/M
$1.00
0
Qwen3.6 27BQwen
Coding
36.8
Overall
45.3
Output $/M
$2.70
17
vs boards won of 17 shared

Llama 4 Maverick or Qwen3.6 27B: which to pick

Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Llama 4 Maverick has none, so the two cannot be ranked on coding.

Pick Llama 4 Maverick for output tokens 2.7x cheaper ($1.00 vs $2.70 per million) and a larger context window (524K against 262K tokens).

Shared results
17
Llama 4 Maverick only
68
Qwen3.6 27B only
8
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
Llama 4 Maverick not benchmarked, Qwen3.6 27B 36.8 from 2 benchmarks.
Widest category
Agentic work, 47.1 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: Llama 4 Maverick 17.8% against Qwen3.6 27B 94.2%, 76.3 points apart on a 0 to 100 scale.
Output price
Llama 4 Maverick $1.00 against Qwen3.6 27B $2.70 per million output tokens, 2.7x apart.
Context window
Llama 4 Maverick takes 524K tokens, Qwen3.6 27B 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
CategoryLlama 4 MaverickBarsQwen3.6 27BTies
Coding03
Intelligence02
Agentic work02
Reasoning02
Knowledge03
General05
All shared boards017
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 27BLeader
Aider polyglot15.6%Not measuredOnly Llama 4 Maverick measured
SWE-bench Verified21.0%Not measuredOnly Llama 4 Maverick measured
AA Coding Index16.353.7Qwen3.6 27B
LiveCodeBench (Vals AI)47.3%Not measuredOnly Llama 4 Maverick measured
SWE-bench Verified (Vals AI)Not measured70.0%Only Qwen3.6 27B measured
Vibe Code Bench (Vals AI)Not measured11.9%Only Qwen3.6 27B measured
LiveCodeBench (Artificial Analysis run)39.7%Not measuredOnly Llama 4 Maverick measured
SciCode31.7%42.8%Qwen3.6 27B
Terminal-Bench Hard (Artificial Analysis run)6.8%34.8%Qwen3.6 27B
WeirdML24.5%Not measuredOnly Llama 4 Maverick measured
ALE-Bench173.0Not measuredOnly Llama 4 Maverick measured
LMArena Text: coding1301Not measuredOnly Llama 4 Maverick measured
LMArena Text: software and IT1299Not measuredOnly Llama 4 Maverick measured
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 27BLeader
AA Intelligence Index9.321.4Qwen3.6 27B
Epoch Capabilities Index132.2146.2Qwen3.6 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 27BLeader
AA Agentic Index0.618.5Qwen3.6 27B
τ²-Bench Telecom17.8%94.2%Qwen3.6 27B
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 27BLeader
GPQA Diamond69.4%Not measuredOnly Llama 4 Maverick measured
MMMU Pro (Vals AI)71.7%Not measuredOnly Llama 4 Maverick measured
GPQA Diamond (Artificial Analysis run)67.1%84.2%Qwen3.6 27B
Humanity's Last Exam (Artificial Analysis run)4.9%23.1%Qwen3.6 27B
GPQA Diamond (Epoch AI run)Not measured85.9%Only Qwen3.6 27B measured
ARC-AGI4.4%Not measuredOnly Llama 4 Maverick measured
ARC-AGI-20.0%Not measuredOnly Llama 4 Maverick measured
SimpleBench27.7%Not measuredOnly Llama 4 Maverick measured
Mystery game puzzlesNot measured7.0%Only Qwen3.6 27B measured
LMArena Text: hard prompts1281Not measuredOnly Llama 4 Maverick measured
LMArena Text: expert1259Not measuredOnly Llama 4 Maverick measured
LMArena Text: hard prompts in English1295Not measuredOnly Llama 4 Maverick measured
LMArena Vision: diagrams1134Not measuredOnly Llama 4 Maverick measured
LMArena Vision: homework1164Not measuredOnly Llama 4 Maverick measured
Kagi LLM Benchmark55.9%Not measuredOnly Llama 4 Maverick measured
Neither model has a result on the other 8 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
Math benchmarks for Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 27BLeader
AIME25.2%Not measuredOnly Llama 4 Maverick measured
MATH 500 (Vals AI)85.2%Not measuredOnly Llama 4 Maverick measured
MGSM (Vals AI)92.4%Not measuredOnly Llama 4 Maverick measured
AA Math Index19.3Not measuredOnly Llama 4 Maverick measured
AIME 2025 (Artificial Analysis run)19.3%Not measuredOnly Llama 4 Maverick measured
OTIS Mock AIMENot measured91.1%Only Qwen3.6 27B measured
FrontierMath, tiers 1 to 3Not measured35.1%Only Qwen3.6 27B measured
LMArena Text: math1299Not measuredOnly Llama 4 Maverick measured
LMArena Text: mathematical work1295Not measuredOnly Llama 4 Maverick measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index03
Knowledge benchmarks for Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 27BLeader
MMLU Pro79.4%Not measuredOnly Llama 4 Maverick measured
LegalBench (Vals AI)77.8%Not measuredOnly Llama 4 Maverick measured
CaseLaw (Vals AI)Not measured53.2%Only Qwen3.6 27B measured
CorpFin (Vals AI)49.7%62.3%Qwen3.6 27B
TaxEval (Vals AI)66.6%71.3%Qwen3.6 27B
MortgageTax (Vals AI)58.5%68.3%Qwen3.6 27B
MedQA (Vals AI)43.3%Not measuredOnly Llama 4 Maverick measured
MedCode (Vals AI)36.5%Not measuredOnly Llama 4 Maverick measured
MMLU Pro (Artificial Analysis run)80.9%Not measuredOnly Llama 4 Maverick measured
GeoBench52.0%Not measuredOnly Llama 4 Maverick measured
LMArena Text: science1288Not measuredOnly Llama 4 Maverick measured
LMArena Text: medicine and healthcare1275Not measuredOnly Llama 4 Maverick measured
LMArena Text: legal and government1286Not measuredOnly Llama 4 Maverick measured
LMArena Text: business and finance1260Not measuredOnly Llama 4 Maverick measured
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 27BLeader
Design Arena: websites871Not measuredOnly Llama 4 Maverick measured
Design Arena: UI components897Not measuredOnly Llama 4 Maverick measured
Design Arena: game dev837Not measuredOnly Llama 4 Maverick measured
Design Arena: data viz882Not measuredOnly Llama 4 Maverick measured
Design Arena: 3D900Not measuredOnly Llama 4 Maverick measured
Design Arena: all code categories879Not measuredOnly Llama 4 Maverick measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index05
General benchmarks for Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 27BLeader
SAGE (Vals AI)35.7%45.6%Qwen3.6 27B
MedScribe (Vals AI)54.2%Not measuredOnly Llama 4 Maverick measured
IFBench43.0%67.6%Qwen3.6 27B
Long context reasoning (AA-LCR)50.0%77.3%Qwen3.6 27B
DTBench61.9%78.1%Qwen3.6 27B
LMCA15.9%34.5%Qwen3.6 27B
Fiction.LiveBench46.2%Not measuredOnly Llama 4 Maverick measured
Creative writing (Lech Mazur)62.0%Not measuredOnly Llama 4 Maverick measured
LMArena Text1287Not measuredOnly Llama 4 Maverick measured
LMArena Text: instruction following1267Not measuredOnly Llama 4 Maverick measured
LMArena Text: creative writing1266Not measuredOnly Llama 4 Maverick measured
LMArena Vision1142Not measuredOnly Llama 4 Maverick measured
LMArena Text: English1303Not measuredOnly Llama 4 Maverick measured
LMArena Text: non-English1269Not measuredOnly Llama 4 Maverick measured
LMArena Text: Chinese1277Not measuredOnly Llama 4 Maverick measured
LMArena Text: German1291Not measuredOnly Llama 4 Maverick measured
LMArena Text: Japanese1206Not measuredOnly Llama 4 Maverick measured
LMArena Text: Korean1203Not measuredOnly Llama 4 Maverick measured
LMArena Text: French1259Not measuredOnly Llama 4 Maverick measured
LMArena Text: Spanish1293Not measuredOnly Llama 4 Maverick measured
LMArena Text: Russian1286Not measuredOnly Llama 4 Maverick measured
LMArena Text: Polish1275Not measuredOnly Llama 4 Maverick measured
LMArena Text: multi-turn1289Not measuredOnly Llama 4 Maverick measured
LMArena Text: longer queries1280Not measuredOnly Llama 4 Maverick measured
LMArena Text: writing and language1277Not measuredOnly Llama 4 Maverick measured
LMArena Text: entertainment and media1250Not measuredOnly Llama 4 Maverick measured
LMArena Vision: English1150Not measuredOnly Llama 4 Maverick measured
LMArena Vision: Chinese1141Not measuredOnly Llama 4 Maverick measured
LMArena Vision: reading text1143Not measuredOnly Llama 4 Maverick measured
LMArena Vision: humor1100Not measuredOnly Llama 4 Maverick measured
LMArena Vision: creative writing1099Not measuredOnly Llama 4 Maverick measured
EQ-Bench 4Not measured1026Only Qwen3.6 27B 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 Llama 4 Maverick and Qwen3.6 27B
BenchmarkLlama 4 MaverickQwen3.6 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

Llama 4 Maverick$0.0008Fits in one request
Qwen3.6 27B$0.0018Fits in one request

Llama 4 Maverick costs less

Repository review

50K input + 3K output tokens

Llama 4 Maverick$0.021Fits in one request
Qwen3.6 27B$0.031Fits in one request

Llama 4 Maverick costs less

Agent loop

200K input + 10K output tokens

Llama 4 Maverick$0.080Fits in one request
Qwen3.6 27B$0.117Fits in one request

Llama 4 Maverick 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 Llama 4 Maverick and Qwen3.6 27B
SpecificationMeta Llama 4 MaverickQwen3.6 27B
MakerMeta LlamaQwen
WeightsOpen weightsOpen weights
ReleasedApril 5, 2025April 27, 2026
Context window524K262K
Input $/Mtok$0.35$0.45
Output $/Mtok$1.00$2.70
Coding indexNot benchmarked36.8
Independent measurements behind it02
Cost per solved task$1.491$1.30
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 4 Maverick better than Qwen3.6 27B for coding?

Only Qwen3.6 27B has a coding score, 36.8 on the Vibe Coding Index; Llama 4 Maverick has none, so the two cannot be ranked on coding. Pick Llama 4 Maverick for output tokens 2.7x cheaper ($1.00 vs $2.70 per million) and a larger context window (524K against 262K tokens).

Which is cheaper, Llama 4 Maverick or Qwen3.6 27B?

Llama 4 Maverick costs $0.35 per million input tokens and $1.00 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Per solved SWE-bench Verified task, Llama 4 Maverick costs $1.491 and Qwen3.6 27B $1.30.

Which has the bigger context window, Llama 4 Maverick or Qwen3.6 27B?

Llama 4 Maverick, with 524K tokens against 262K.

How many benchmarks is the Llama 4 Maverick vs Qwen3.6 27B comparison based on?

17 benchmarks have a result for both models as of October 22, 2026. Llama 4 Maverick's coding index rests on 0 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.

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