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Llama 3.2 3B Instruct vs Qwen3 32B

Qwen3 32B leads Llama 3.2 3B Instruct on the Artificial Analysis Intelligence Index, 7.3 to 5.7, and Llama 3.2 3B Instruct costs 1.2x less per output token ($0.3325 vs $0.40 per million). Updated October 21, 2026; 34 benchmarks have a result for both.

Replace Llama 3.2 3B Instruct Replace Qwen3 32B
Llama 3.2 3B InstructMeta Llama
Coding
No data
Overall
No data
Output $/M
$0.3325
1
Qwen3 32BQwen
Coding
No data
Overall
40.8
Output $/M
$0.40
33
vs boards won of 34 shared

Llama 3.2 3B Instruct or Qwen3 32B: which to pick

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

Pick Llama 3.2 3B Instruct for output tokens 1.2x cheaper ($0.3325 vs $0.40 per million) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model ALlama 3.2 3B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 32BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
34
Llama 3.2 3B Instruct only
1
Qwen3 32B only
11
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 3B Instruct not benchmarked, Qwen3 32B not benchmarked.
Widest category
Math, 58.3 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: Llama 3.2 3B Instruct 3.3 against Qwen3 32B 73.0, 69.7 points apart on a 0 to 100 scale.
Output price
Llama 3.2 3B Instruct $0.3325 against Qwen3 32B $0.40 per million output tokens, 1.2x apart.
Context window
Both take 131K tokens.

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 3B InstructBarsQwen3 32BTies
Coding03
Intelligence01
Agentic work02
Reasoning05
Math04
Knowledge05
General113
All shared boards133
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Llama 3.2 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
Aider polyglotNot measured40.0%Only Qwen3 32B measured
LiveCodeBench (Artificial Analysis run)8.3%54.6%Qwen3 32B
SciCodeNot measured36.0%Only Qwen3 32B measured
Terminal-Bench Hard (Artificial Analysis run)Not measured3.0%Only Qwen3 32B measured
LMArena Text: coding10981357Qwen3 32B
LMArena Text: software and IT11031366Qwen3 32B
Neither model has a result on the other 33 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Llama 3.2 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
AA Intelligence Index5.77.3Qwen3 32B
Epoch Capabilities IndexNot measured138.4Only Qwen3 32B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for Llama 3.2 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
τ²-Bench Telecom21.1%29.8%Qwen3 32B
BALROG10.1%Not measuredOnly Llama 3.2 3B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)21.9%48.7%Qwen3 32B
BrowseComp-PlusNot measured10.7%Only Qwen3 32B measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Llama 3.2 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
GPQA Diamond (Artificial Analysis run)25.5%66.8%Qwen3 32B
Humanity's Last Exam (Artificial Analysis run)5.3%7.4%Qwen3 32B
GPQA Diamond (Epoch AI run)Not measured65.7%Only Qwen3 32B measured
LMArena Text: hard prompts10951334Qwen3 32B
LMArena Text: expert10901362Qwen3 32B
LMArena Text: hard prompts in English11411350Qwen3 32B
Kagi LLM BenchmarkNot measured48.7%Only Qwen3 32B measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for Llama 3.2 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
AA Math Index3.373.0Qwen3 32B
AIME 2025 (Artificial Analysis run)3.3%73.0%Qwen3 32B
OTIS Mock AIMENot measured66.9%Only Qwen3 32B measured
LMArena Text: math11261399Qwen3 32B
LMArena Text: mathematical work11281420Qwen3 32B
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 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
MMLU Pro (Artificial Analysis run)34.7%79.8%Qwen3 32B
LMArena Text: science11061372Qwen3 32B
LMArena Text: medicine and healthcare11041368Qwen3 32B
LMArena Text: legal and government11151324Qwen3 32B
LMArena Text: business and finance10741318Qwen3 32B
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 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
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 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
IFBench26.2%36.3%Qwen3 32B
Long context reasoning (AA-LCR)4.3%0.0%Llama 3.2 3B Instruct
DTBenchNot measured67.5%Only Qwen3 32B measured
LMCANot measured17.3%Only Qwen3 32B measured
Fiction.LiveBenchNot measured74.2%Only Qwen3 32B measured
LMArena Text11101340Qwen3 32B
LMArena Text: instruction following10891305Qwen3 32B
LMArena Text: creative writing10941297Qwen3 32B
LMArena Text: English11801359Qwen3 32B
LMArena Text: non-English10191317Qwen3 32B
LMArena Text: Chinese10171357Qwen3 32B
LMArena Text: German10561341Qwen3 32B
LMArena Text: Russian9491311Qwen3 32B
LMArena Text: multi-turn11051331Qwen3 32B
LMArena Text: longer queries11001327Qwen3 32B
LMArena Text: writing and language10961298Qwen3 32B
LMArena Text: entertainment and media10831279Qwen3 32B
Neither model has a result on the other 29 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Llama 3.2 3B Instruct and Qwen3 32B
BenchmarkLlama 3.2 3B InstructQwen3 32BLeader
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 3B Instruct$0.0002Fits in one request
Qwen3 32B$0.0003Fits in one request

Llama 3.2 3B Instruct costs less

Repository review

50K input + 3K output tokens

Llama 3.2 3B Instruct$0.0035Fits in one request
Qwen3 32B$0.0082Fits in one request

Llama 3.2 3B Instruct costs less

Agent loop

200K input + 10K output tokens

Llama 3.2 3B Instruct$0.013Exceeds the context window
Qwen3 32B$0.032Exceeds the context window

Llama 3.2 3B 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 3B Instruct and Qwen3 32B
SpecificationMeta Llama 3.2 3B InstructQwen3 32B
MakerMeta LlamaQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 25, 2024April 28, 2025
Context window131K131K
Input $/Mtok$0.0505$0.14
Output $/Mtok$0.3325$0.40
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Llama 3.2 3B Instruct better than Qwen3 32B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 3.2 3B Instruct for output tokens 1.2x cheaper ($0.3325 vs $0.40 per million) and work measured by LiveCodeBench (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, Llama 3.2 3B Instruct or Qwen3 32B?

Llama 3.2 3B Instruct costs $0.0505 per million input tokens and $0.3325 per million output tokens; Qwen3 32B costs $0.14 and $0.40. Cost per solved task is known for neither of them.

Which has the bigger context window, Llama 3.2 3B Instruct or Qwen3 32B?

Neither: both take 131K tokens.

How many benchmarks is the Llama 3.2 3B Instruct vs Qwen3 32B comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. Llama 3.2 3B Instruct's coding index rests on 0 independent benchmarks and Qwen3 32B's on 0. Where only one model has a result, the page says so instead of naming a winner.

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