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Llama 3.2 1B Instruct vs Qwen3 32B

Qwen3 32B leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 7.3 to 4.8, and Llama 3.2 1B Instruct costs 2.0x less per output token ($0.201 vs $0.40 per million). Updated October 21, 2026; 37 benchmarks have a result for both.

Replace Llama 3.2 1B Instruct Replace Qwen3 32B
Llama 3.2 1B InstructMeta Llama
Coding
No data
Overall
9.6
Output $/M
$0.201
1
Qwen3 32BQwen
Coding
No data
Overall
40.8
Output $/M
$0.40
36
vs boards won of 37 shared

Llama 3.2 1B 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 1B Instruct for output tokens 2.0x cheaper ($0.201 vs $0.40 per million) and work measured by LiveCodeBench (Artificial Analysis run) or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model ALlama 3.2 1B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3 32BQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
37
Llama 3.2 1B Instruct only
1
Qwen3 32B only
8
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, Qwen3 32B not benchmarked.
Widest category
Math, 63.6 points apart on average over 5 shared benchmarks.
Widest single gap
AA Math Index: Llama 3.2 1B Instruct 0.0 against Qwen3 32B 73.0, 73.0 points apart on a 0 to 100 scale.
Output price
Llama 3.2 1B Instruct $0.201 against Qwen3 32B $0.40 per million output tokens, 2.0x apart.
Context window
Llama 3.2 1B Instruct takes 60K tokens, Qwen3 32B 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 InstructBarsQwen3 32BTies
Coding04
Intelligence01
Agentic work02
Reasoning06
Math05
Knowledge05
General113
All shared boards136
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for Llama 3.2 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B InstructQwen3 32BLeader
Aider polyglotNot measured40.0%Only Qwen3 32B measured
LiveCodeBench (Artificial Analysis run)1.9%54.6%Qwen3 32B
SciCodeNot measured36.0%Only Qwen3 32B measured
Terminal-Bench Hard (Artificial Analysis run)0.0%3.0%Qwen3 32B
LMArena Text: coding10701357Qwen3 32B
LMArena Text: software and IT10561366Qwen3 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 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B InstructQwen3 32BLeader
AA Intelligence Index4.87.3Qwen3 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 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B InstructQwen3 32BLeader
τ²-Bench Telecom0.0%29.8%Qwen3 32B
BALROG6.6%Not measuredOnly Llama 3.2 1B Instruct measured
Berkeley Function Calling Leaderboard (BFCL)10.8%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 index06
Reasoning benchmarks for Llama 3.2 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B InstructQwen3 32BLeader
GPQA Diamond (Artificial Analysis run)19.6%66.8%Qwen3 32B
Humanity's Last Exam (Artificial Analysis run)5.5%7.4%Qwen3 32B
GPQA Diamond (Epoch AI run)23.9%65.7%Qwen3 32B
LMArena Text: hard prompts10441334Qwen3 32B
LMArena Text: expert10071362Qwen3 32B
LMArena Text: hard prompts in English10871350Qwen3 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 index05
Math benchmarks for Llama 3.2 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B InstructQwen3 32BLeader
AA Math Index0.073.0Qwen3 32B
AIME 2025 (Artificial Analysis run)0.0%73.0%Qwen3 32B
OTIS Mock AIME0.6%66.9%Qwen3 32B
LMArena Text: math10861399Qwen3 32B
LMArena Text: mathematical work10981420Qwen3 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 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B InstructQwen3 32BLeader
MMLU Pro (Artificial Analysis run)20.0%79.8%Qwen3 32B
LMArena Text: science10551372Qwen3 32B
LMArena Text: medicine and healthcare10091368Qwen3 32B
LMArena Text: legal and government10711324Qwen3 32B
LMArena Text: business and finance10001318Qwen3 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 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B 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 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B InstructQwen3 32BLeader
IFBench22.8%36.3%Qwen3 32B
Long context reasoning (AA-LCR)6.7%0.0%Llama 3.2 1B Instruct
DTBenchNot measured67.5%Only Qwen3 32B measured
LMCANot measured17.3%Only Qwen3 32B measured
Fiction.LiveBenchNot measured74.2%Only Qwen3 32B measured
LMArena Text10551340Qwen3 32B
LMArena Text: instruction following10311305Qwen3 32B
LMArena Text: creative writing10331297Qwen3 32B
LMArena Text: English11171359Qwen3 32B
LMArena Text: non-English9731317Qwen3 32B
LMArena Text: Chinese9591357Qwen3 32B
LMArena Text: German10141341Qwen3 32B
LMArena Text: Russian9411311Qwen3 32B
LMArena Text: multi-turn10301331Qwen3 32B
LMArena Text: longer queries10501327Qwen3 32B
LMArena Text: writing and language10301298Qwen3 32B
LMArena Text: entertainment and media10251279Qwen3 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 1B Instruct and Qwen3 32B
BenchmarkLlama 3.2 1B 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 1B Instruct$0.0001Fits in one request
Qwen3 32B$0.0003Fits 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
Qwen3 32B$0.0082Fits 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
Qwen3 32B$0.032Exceeds 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 Qwen3 32B
SpecificationMeta Llama 3.2 1B InstructQwen3 32B
MakerMeta LlamaQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 25, 2024April 28, 2025
Context window60K131K
Input $/Mtok$0.027$0.14
Output $/Mtok$0.201$0.40
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 Qwen3 32B 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 2.0x cheaper ($0.201 vs $0.40 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 Qwen3 32B?

Llama 3.2 1B Instruct costs $0.027 per million input tokens and $0.201 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 1B Instruct or Qwen3 32B?

Qwen3 32B, with 131K tokens against 60K.

How many benchmarks is the Llama 3.2 1B Instruct vs Qwen3 32B comparison based on?

37 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 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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