Enter opens the highlighted result. Esc closes.

Llama 3.2 1B Instruct vs Llama 4 Maverick

Llama 4 Maverick leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 9.3 to 4.8, and Llama 3.2 1B Instruct costs 5.0x less per output token ($0.201 vs $1.00 per million). Updated October 21, 2026; 34 benchmarks have a result for both.

Llama 3.2 1B InstructMeta Llama
Coding
No data
Overall
9.6
Output $/M
$0.201
1
Llama 4 MaverickMeta Llama
Coding
No data
Overall
21.8
Output $/M
$1.00
33
vs boards won of 34 shared

Llama 3.2 1B Instruct or Llama 4 Maverick: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Llama 3.2 1B Instruct for output tokens 5.0x cheaper ($0.201 vs $1.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Shared results
34
Llama 3.2 1B Instruct only
4
Llama 4 Maverick only
51
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
Llama 3.2 1B Instruct not benchmarked, Llama 4 Maverick not benchmarked.
Widest category
Knowledge, 44.7 points apart on average over 5 shared benchmarks.
Widest single gap
MMLU Pro (Artificial Analysis run): Llama 3.2 1B Instruct 20.0% against Llama 4 Maverick 80.9%, 60.9 points apart on a 0 to 100 scale.
Output price
Llama 3.2 1B Instruct $0.201 against Llama 4 Maverick $1.00 per million output tokens, 5.0x apart.
Context window
Llama 3.2 1B Instruct takes 60K tokens, Llama 4 Maverick 524K.

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 3.2 1B InstructBarsLlama 4 MaverickTies
Coding04
Intelligence01
Agentic work01
Reasoning14
Math04
Knowledge05
General014
All shared boards133
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
Aider polyglotNot measured15.6%Only Llama 4 Maverick measured
SWE-bench VerifiedNot measured21.0%Only Llama 4 Maverick measured
AA Coding IndexNot measured16.3Only Llama 4 Maverick measured
LiveCodeBench (Vals AI)Not measured47.3%Only Llama 4 Maverick measured
LiveCodeBench (Artificial Analysis run)1.9%39.7%Llama 4 Maverick
SciCodeNot measured31.7%Only Llama 4 Maverick measured
Terminal-Bench Hard (Artificial Analysis run)0.0%6.8%Llama 4 Maverick
WeirdMLNot measured24.5%Only Llama 4 Maverick measured
ALE-BenchNot measured173.0Only Llama 4 Maverick measured
LMArena Text: coding10701301Llama 4 Maverick
LMArena Text: software and IT10561299Llama 4 Maverick
Neither model has a result on the other 28 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
AA Intelligence Index4.89.3Llama 4 Maverick
Epoch Capabilities IndexNot measured132.2Only Llama 4 Maverick measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
AA Agentic IndexNot measured0.6Only Llama 4 Maverick measured
τ²-Bench Telecom0.0%17.8%Llama 4 Maverick
BALROG6.6%Not measuredOnly Llama 3.2 1B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)10.8%Not measuredOnly Llama 3.2 1B Instruct measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
Reasoning benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
GPQA DiamondNot measured69.4%Only Llama 4 Maverick measured
MMMU Pro (Vals AI)Not measured71.7%Only Llama 4 Maverick measured
GPQA Diamond (Artificial Analysis run)19.6%67.1%Llama 4 Maverick
Humanity's Last Exam (Artificial Analysis run)5.5%4.9%Llama 3.2 1B Instruct
GPQA Diamond (Epoch AI run)23.9%Not measuredOnly Llama 3.2 1B Instruct measured
ARC-AGINot measured4.4%Only Llama 4 Maverick measured
ARC-AGI-2Not measured0.0%Only Llama 4 Maverick measured
SimpleBenchNot measured27.7%Only Llama 4 Maverick measured
LMArena Text: hard prompts10441281Llama 4 Maverick
LMArena Text: expert10071259Llama 4 Maverick
LMArena Text: hard prompts in English10871295Llama 4 Maverick
LMArena Vision: diagramsNot measured1134Only Llama 4 Maverick measured
LMArena Vision: homeworkNot measured1164Only Llama 4 Maverick measured
Kagi LLM BenchmarkNot measured55.9%Only Llama 4 Maverick measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
AIMENot measured25.2%Only Llama 4 Maverick measured
MATH 500 (Vals AI)Not measured85.2%Only Llama 4 Maverick measured
MGSM (Vals AI)Not measured92.4%Only Llama 4 Maverick measured
AA Math Index0.019.3Llama 4 Maverick
AIME 2025 (Artificial Analysis run)0.0%19.3%Llama 4 Maverick
OTIS Mock AIME0.6%Not measuredOnly Llama 3.2 1B Instruct measured
LMArena Text: math10861299Llama 4 Maverick
LMArena Text: mathematical work10981295Llama 4 Maverick
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
MMLU ProNot measured79.4%Only Llama 4 Maverick measured
LegalBench (Vals AI)Not measured77.8%Only Llama 4 Maverick measured
CorpFin (Vals AI)Not measured49.7%Only Llama 4 Maverick measured
TaxEval (Vals AI)Not measured66.6%Only Llama 4 Maverick measured
MortgageTax (Vals AI)Not measured58.5%Only Llama 4 Maverick measured
MedQA (Vals AI)Not measured43.3%Only Llama 4 Maverick measured
MedCode (Vals AI)Not measured36.5%Only Llama 4 Maverick measured
MMLU Pro (Artificial Analysis run)20.0%80.9%Llama 4 Maverick
GeoBenchNot measured52.0%Only Llama 4 Maverick measured
LMArena Text: science10551288Llama 4 Maverick
LMArena Text: medicine and healthcare10091275Llama 4 Maverick
LMArena Text: legal and government10711286Llama 4 Maverick
LMArena Text: business and finance10001260Llama 4 Maverick
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
Design Arena: websitesNot measured871Only Llama 4 Maverick measured
Design Arena: UI componentsNot measured899Only Llama 4 Maverick measured
Design Arena: game devNot measured838Only Llama 4 Maverick measured
Design Arena: data vizNot measured882Only Llama 4 Maverick measured
Design Arena: 3DNot measured901Only Llama 4 Maverick measured
Design Arena: all code categoriesNot measured880Only 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 index014
General benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
SAGE (Vals AI)Not measured35.7%Only Llama 4 Maverick measured
MedScribe (Vals AI)Not measured54.2%Only Llama 4 Maverick measured
IFBench22.8%43.0%Llama 4 Maverick
Long context reasoning (AA-LCR)6.7%50.0%Llama 4 Maverick
DTBenchNot measured61.9%Only Llama 4 Maverick measured
LMCANot measured15.9%Only Llama 4 Maverick measured
Fiction.LiveBenchNot measured46.2%Only Llama 4 Maverick measured
Creative writing (Lech Mazur)Not measured62.0%Only Llama 4 Maverick measured
LMArena Text10551287Llama 4 Maverick
LMArena Text: instruction following10311267Llama 4 Maverick
LMArena Text: creative writing10331266Llama 4 Maverick
LMArena VisionNot measured1142Only Llama 4 Maverick measured
LMArena Text: English11171303Llama 4 Maverick
LMArena Text: non-English9731269Llama 4 Maverick
LMArena Text: Chinese9591277Llama 4 Maverick
LMArena Text: German10141291Llama 4 Maverick
LMArena Text: JapaneseNot measured1206Only Llama 4 Maverick measured
LMArena Text: KoreanNot measured1203Only Llama 4 Maverick measured
LMArena Text: FrenchNot measured1259Only Llama 4 Maverick measured
LMArena Text: SpanishNot measured1293Only Llama 4 Maverick measured
LMArena Text: Russian9411286Llama 4 Maverick
LMArena Text: PolishNot measured1275Only Llama 4 Maverick measured
LMArena Text: multi-turn10301289Llama 4 Maverick
LMArena Text: longer queries10501280Llama 4 Maverick
LMArena Text: writing and language10301277Llama 4 Maverick
LMArena Text: entertainment and media10251250Llama 4 Maverick
LMArena Vision: EnglishNot measured1150Only Llama 4 Maverick measured
LMArena Vision: ChineseNot measured1141Only Llama 4 Maverick measured
LMArena Vision: reading textNot measured1143Only Llama 4 Maverick measured
LMArena Vision: humorNot measured1100Only Llama 4 Maverick measured
LMArena Vision: creative writingNot measured1099Only Llama 4 Maverick measured
Neither model has a result on the other 15 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Llama 3.2 1B Instruct and Llama 4 Maverick
BenchmarkLlama 3.2 1B InstructLlama 4 MaverickLeader
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 3.2 1B Instruct$0.0001Fits in one request
Llama 4 Maverick$0.0008Fits in one request

Llama 3.2 1B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.2 1B Instruct$0.0020Fits in one request
Llama 4 Maverick$0.021Fits in one request

Llama 3.2 1B Instruct costs less

Agent loop

200K input + 10K output tokens

Llama 3.2 1B Instruct$0.0074Exceeds the context window
Llama 4 Maverick$0.080Fits in one request

Llama 3.2 1B Instruct 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 3.2 1B Instruct and Llama 4 Maverick
SpecificationMeta Llama 3.2 1B InstructMeta Llama 4 Maverick
MakerMeta LlamaMeta Llama
WeightsOpen weightsOpen weights
ReleasedSeptember 25, 2024April 5, 2025
Context window60K524K
Input $/Mtok$0.027$0.35
Output $/Mtok$0.201$1.00
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computable$1.491
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.2 1B Instruct better than Llama 4 Maverick for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 1B Instruct for output tokens 5.0x cheaper ($0.201 vs $1.00 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, Llama 3.2 1B Instruct or Llama 4 Maverick?

Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; Llama 4 Maverick costs $0.35 and $1.00. Cost per solved task is known for Llama 4 Maverick only.

Which has the bigger context window, Llama 3.2 1B Instruct or Llama 4 Maverick?

Llama 4 Maverick, with 524K tokens against 60K.

How many benchmarks is the Llama 3.2 1B Instruct vs Llama 4 Maverick comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. Llama 3.2 1B Instruct's coding index rests on 0 independent benchmarks and Llama 4 Maverick's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next