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DeepSeek V3.2 Exp vs Llama 3.2 1B Instruct

DeepSeek V3.2 Exp leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 16.6 to 4.8, and Llama 3.2 1B Instruct costs 2.0x less per output token ($0.201 vs $0.41 per million). Updated October 21, 2026; 34 benchmarks have a result for both.

Replace DeepSeek V3.2 Exp Replace Llama 3.2 1B Instruct
DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
16.6
Output $/M
$0.41
34
Llama 3.2 1B InstructMeta Llama
Coding
No data
Overall
4.8
Output $/M
$0.201
0
vs boards won of 34 shared

DeepSeek V3.2 Exp or Llama 3.2 1B Instruct: which to pick

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

Pick DeepSeek V3.2 Exp for a larger context window (164K against 60K tokens).

Model ADeepSeek V3.2 ExpDeepSeekNot benchmarked1 independent benchmarkNo cost per solved taskModel BLlama 3.2 1B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
34
DeepSeek V3.2 Exp only
23
Llama 3.2 1B Instruct only
3
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
DeepSeek V3.2 Exp not benchmarked, Llama 3.2 1B Instruct not benchmarked.
Widest category
Math, 71.8 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: DeepSeek V3.2 Exp 87.7 against Llama 3.2 1B Instruct 0.0, 87.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against Llama 3.2 1B Instruct $0.201 per million output tokens, 2.0x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, Llama 3.2 1B Instruct 60K.

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
CategoryDeepSeek V3.2 ExpBarsLlama 3.2 1B InstructTies
Coding40
Intelligence10
Agentic work10
Reasoning50
Math40
Knowledge50
General140
All shared boards340
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B InstructLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp measured
LiveCodeBench (Artificial Analysis run)78.9%1.9%DeepSeek V3.2 Exp
Terminal-Bench Hard (Artificial Analysis run)31.1%0.0%DeepSeek V3.2 Exp
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: coding14381070DeepSeek V3.2 Exp
LMArena Text: software and IT14381056DeepSeek V3.2 Exp
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B InstructLeader
AA Intelligence Index16.64.8DeepSeek V3.2 Exp
Epoch Capabilities Index144.9Not measuredOnly DeepSeek V3.2 Exp measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B InstructLeader
τ²-Bench Telecom33.9%0.0%DeepSeek V3.2 Exp
BALROGNot measured6.6%Only Llama 3.2 1B Instruct measured
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B InstructLeader
GPQA Diamond (Artificial Analysis run)79.7%19.6%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%5.5%DeepSeek V3.2 Exp
GPQA Diamond (Epoch AI run)Not measured23.9%Only Llama 3.2 1B Instruct measured
LMArena Text: hard prompts14291044DeepSeek V3.2 Exp
LMArena Text: expert14221007DeepSeek V3.2 Exp
LMArena Text: hard prompts in English14441087DeepSeek V3.2 Exp
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B InstructLeader
AA Math Index87.70.0DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%0.0%DeepSeek V3.2 Exp
OTIS Mock AIMENot measured0.6%Only Llama 3.2 1B Instruct measured
LMArena Text: math14221086DeepSeek V3.2 Exp
LMArena Text: mathematical work14331098DeepSeek V3.2 Exp
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B InstructLeader
MMLU Pro (Artificial Analysis run)85.0%20.0%DeepSeek V3.2 Exp
LMArena Text: science14431055DeepSeek V3.2 Exp
LMArena Text: medicine and healthcare14681009DeepSeek V3.2 Exp
LMArena Text: legal and government14191071DeepSeek V3.2 Exp
LMArena Text: business and finance14121000DeepSeek V3.2 Exp
Neither model has a result on the other 12 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B InstructLeader
Design Arena: websites1179Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: UI components1167Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: game dev1144Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: data viz1152Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: 3D1148Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: all code categories1171Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: SVG1032Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index140
General benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B InstructLeader
IFBench54.1%22.8%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%6.7%DeepSeek V3.2 Exp
DTBench87.7%Not measuredOnly DeepSeek V3.2 Exp measured
LMCA29.1%Not measuredOnly DeepSeek V3.2 Exp measured
Fiction.LiveBench83.3%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251055DeepSeek V3.2 Exp
LMArena Text: instruction following14011031DeepSeek V3.2 Exp
LMArena Text: creative writing14041033DeepSeek V3.2 Exp
LMArena Text: English14411117DeepSeek V3.2 Exp
LMArena Text: non-English1404973DeepSeek V3.2 Exp
LMArena Text: Chinese1461959DeepSeek V3.2 Exp
LMArena Text: German14401014DeepSeek V3.2 Exp
LMArena Text: Japanese1331Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Korean1372Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: French1434Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Spanish1441Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: Russian1412941DeepSeek V3.2 Exp
LMArena Text: Polish1435Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: multi-turn14221030DeepSeek V3.2 Exp
LMArena Text: longer queries14181050DeepSeek V3.2 Exp
LMArena Text: writing and language14071030DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071025DeepSeek V3.2 Exp
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
BenchmarkDeepSeek V3.2 ExpLlama 3.2 1B 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

DeepSeek V3.2 Exp$0.0005Fits in one request
Llama 3.2 1B Instruct$0.0001Fits in one request

Llama 3.2 1B Instruct costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
Llama 3.2 1B Instruct$0.0020Fits in one request

Llama 3.2 1B Instruct costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
Llama 3.2 1B Instruct$0.0074Exceeds 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 DeepSeek V3.2 Exp and Llama 3.2 1B Instruct
SpecificationDeepSeek V3.2 ExpMeta Llama 3.2 1B Instruct
MakerDeepSeekMeta Llama
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025September 25, 2024
Context window164K60K
Input $/Mtok$0.27$0.027
Output $/Mtok$0.41$0.201
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it10
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than Llama 3.2 1B Instruct for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for a larger context window (164K against 60K tokens).

Which is cheaper, DeepSeek V3.2 Exp or Llama 3.2 1B Instruct?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Llama 3.2 1B Instruct costs $0.027 and $0.201. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.2 Exp or Llama 3.2 1B Instruct?

DeepSeek V3.2 Exp, with 164K tokens against 60K.

How many benchmarks is the DeepSeek V3.2 Exp vs Llama 3.2 1B Instruct comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Llama 3.2 1B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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