Enter opens the highlighted result. Esc closes.

Llama 3.2 1B Instruct vs Llama 3.2 3B Instruct

Llama 3.2 3B Instruct leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 5.7 to 4.8, and Llama 3.2 1B Instruct costs 1.7x less per output token ($0.201 vs $0.3325 per million). Updated October 21, 2026; 35 benchmarks have a result for both.

Replace Llama 3.2 1B Instruct Replace Llama 3.2 3B Instruct
Llama 3.2 1B InstructMeta Llama
Coding
No data
Overall
9.6
Output $/M
$0.201
2
Llama 3.2 3B InstructMeta Llama
Coding
No data
Overall
No data
Output $/M
$0.3325
33
vs boards won of 35 shared

Llama 3.2 1B Instruct or Llama 3.2 3B Instruct: 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 1.7x cheaper ($0.201 vs $0.3325 per million) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model ALlama 3.2 1B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel BLlama 3.2 3B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
35
Llama 3.2 1B Instruct only
3
Llama 3.2 3B Instruct only
0
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 3.2 3B Instruct not benchmarked.
Widest category
Agentic work, 11.9 points apart on average over 3 shared benchmarks.
Widest single gap
τ²-Bench Telecom: Llama 3.2 1B Instruct 0.0% against Llama 3.2 3B Instruct 21.1%, 21.1 points apart on a 0 to 100 scale.
Output price
Llama 3.2 1B Instruct $0.201 against Llama 3.2 3B Instruct $0.3325 per million output tokens, 1.7x apart.
Context window
Llama 3.2 1B Instruct takes 60K tokens, Llama 3.2 3B Instruct 131K.

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 3.2 3B InstructTies
Coding03
Intelligence01
Agentic work03
Reasoning14
Math04
Knowledge05
General113
All shared boards233
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Llama 3.2 1B Instruct and Llama 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
LiveCodeBench (Artificial Analysis run)1.9%8.3%Llama 3.2 3B Instruct
Terminal-Bench Hard (Artificial Analysis run)0.0%Not measuredOnly Llama 3.2 1B Instruct measured
LMArena Text: coding10701098Llama 3.2 3B Instruct
LMArena Text: software and IT10561103Llama 3.2 3B Instruct
Neither model has a result on the other 35 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Llama 3.2 1B Instruct and Llama 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
AA Intelligence Index4.85.7Llama 3.2 3B Instruct
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index03
Agentic work benchmarks for Llama 3.2 1B Instruct and Llama 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
τ²-Bench Telecom0.0%21.1%Llama 3.2 3B Instruct
BALROG6.6%10.1%Llama 3.2 3B Instruct
Berkeley Function Calling Leaderboard (BFCL)10.8%21.9%Llama 3.2 3B Instruct
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index14
Reasoning benchmarks for Llama 3.2 1B Instruct and Llama 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
GPQA Diamond (Artificial Analysis run)19.6%25.5%Llama 3.2 3B Instruct
Humanity's Last Exam (Artificial Analysis run)5.5%5.3%Llama 3.2 1B Instruct
GPQA Diamond (Epoch AI run)23.9%Not measuredOnly Llama 3.2 1B Instruct measured
LMArena Text: hard prompts10441095Llama 3.2 3B Instruct
LMArena Text: expert10071090Llama 3.2 3B Instruct
LMArena Text: hard prompts in English10871141Llama 3.2 3B Instruct
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for Llama 3.2 1B Instruct and Llama 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
AA Math Index0.03.3Llama 3.2 3B Instruct
AIME 2025 (Artificial Analysis run)0.0%3.3%Llama 3.2 3B Instruct
OTIS Mock AIME0.6%Not measuredOnly Llama 3.2 1B Instruct measured
LMArena Text: math10861126Llama 3.2 3B Instruct
LMArena Text: mathematical work10981128Llama 3.2 3B Instruct
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for Llama 3.2 1B Instruct and Llama 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
MMLU Pro (Artificial Analysis run)20.0%34.7%Llama 3.2 3B Instruct
LMArena Text: science10551106Llama 3.2 3B Instruct
LMArena Text: medicine and healthcare10091104Llama 3.2 3B Instruct
LMArena Text: legal and government10711115Llama 3.2 3B Instruct
LMArena Text: business and finance10001074Llama 3.2 3B Instruct
Neither model has a result on the other 13 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Llama 3.2 1B Instruct and Llama 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index113
General benchmarks for Llama 3.2 1B Instruct and Llama 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
IFBench22.8%26.2%Llama 3.2 3B Instruct
Long context reasoning (AA-LCR)6.7%4.3%Llama 3.2 1B Instruct
LMArena Text10551110Llama 3.2 3B Instruct
LMArena Text: instruction following10311089Llama 3.2 3B Instruct
LMArena Text: creative writing10331094Llama 3.2 3B Instruct
LMArena Text: English11171180Llama 3.2 3B Instruct
LMArena Text: non-English9731019Llama 3.2 3B Instruct
LMArena Text: Chinese9591017Llama 3.2 3B Instruct
LMArena Text: German10141056Llama 3.2 3B Instruct
LMArena Text: Russian941949Llama 3.2 3B Instruct
LMArena Text: multi-turn10301105Llama 3.2 3B Instruct
LMArena Text: longer queries10501100Llama 3.2 3B Instruct
LMArena Text: writing and language10301096Llama 3.2 3B Instruct
LMArena Text: entertainment and media10251083Llama 3.2 3B Instruct
Neither model has a result on the other 32 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 3.2 3B Instruct
BenchmarkLlama 3.2 1B InstructLlama 3.2 3B InstructLeader
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 3.2 3B Instruct$0.0002Fits 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 3.2 3B Instruct$0.0035Fits 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 3.2 3B Instruct$0.013Exceeds the context window

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 3.2 3B Instruct
SpecificationMeta Llama 3.2 1B InstructMeta Llama 3.2 3B Instruct
MakerMeta LlamaMeta Llama
WeightsOpen weightsOpen weights
ReleasedSeptember 25, 2024September 25, 2024
Context window60K131K
Input $/Mtok$0.027$0.0505
Output $/Mtok$0.201$0.3325
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.2 1B Instruct better than Llama 3.2 3B Instruct 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 1.7x cheaper ($0.201 vs $0.3325 per million) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, Llama 3.2 1B Instruct or Llama 3.2 3B Instruct?

Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 per million output tokens; Llama 3.2 3B Instruct costs $0.0505 and $0.3325. Cost per solved task is known for neither of them.

Which has the bigger context window, Llama 3.2 1B Instruct or Llama 3.2 3B Instruct?

Llama 3.2 3B Instruct, with 131K tokens against 60K.

How many benchmarks is the Llama 3.2 1B Instruct vs Llama 3.2 3B Instruct comparison based on?

35 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 3.2 3B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

Compare next