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R1 0528 vs Llama 3.2 1B Instruct

R1 0528 leads Llama 3.2 1B Instruct on the Artificial Analysis Intelligence Index, 13.1 to 4.8, and Llama 3.2 1B Instruct costs 10.8x less per output token ($0.201 vs $2.18 per million). Updated October 21, 2026; 36 benchmarks have a result for both.

Replace R1 0528 Replace Llama 3.2 1B Instruct
R1 0528DeepSeek
Coding
No data
Overall
46.8
Output $/M
$2.18
36
Llama 3.2 1B InstructMeta Llama
Coding
No data
Overall
9.6
Output $/M
$0.201
0
vs boards won of 36 shared

R1 0528 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 R1 0528 for a larger context window (164K against 60K tokens).

Model AR1 0528DeepSeekNot benchmarkedNo coding benchmarkNo cost per solved taskModel BLlama 3.2 1B InstructMeta LlamaNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
36
R1 0528 only
24
Llama 3.2 1B Instruct only
2
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
R1 0528 not benchmarked, Llama 3.2 1B Instruct not benchmarked.
Widest category
Knowledge, 65.0 points apart on average over 5 shared benchmarks.
Widest single gap
AA Math Index: R1 0528 76.0 against Llama 3.2 1B Instruct 0.0, 76.0 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.18 against Llama 3.2 1B Instruct $0.201 per million output tokens, 10.8x apart.
Context window
R1 0528 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
CategoryR1 0528BarsLlama 3.2 1B InstructTies
Coding40
Intelligence10
Agentic work10
Reasoning60
Math50
Knowledge50
General140
All shared boards360
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 3.2 1B InstructLeader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
LiveCodeBench (Artificial Analysis run)77.0%1.9%R1 0528
Terminal-Bench Hard (Artificial Analysis run)15.9%0.0%R1 0528
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1Not measuredOnly R1 0528 measured
LMArena Text: coding14271070R1 0528
LMArena Text: software and IT14351056R1 0528
Neither model has a result on the other 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 3.2 1B InstructLeader
AA Intelligence Index13.14.8R1 0528
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 3.2 1B InstructLeader
τ²-Bench Telecom36.5%0.0%R1 0528
METR time horizons53.8%Not measuredOnly R1 0528 measured
BALROGNot measured6.6%Only Llama 3.2 1B Instruct measured
DeepResearch Bench35.1%Not measuredOnly R1 0528 measured
Berkeley Function Calling Leaderboard (BFCL)Not measured10.8%Only Llama 3.2 1B Instruct measured
BrowseComp-Plus16.4%Not measuredOnly R1 0528 measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 3.2 1B InstructLeader
GPQA Diamond (Artificial Analysis run)81.3%19.6%R1 0528
Humanity's Last Exam (Artificial Analysis run)15.8%5.5%R1 0528
GPQA Diamond (Epoch AI run)76.3%23.9%R1 0528
ARC-AGI21.2%Not measuredOnly R1 0528 measured
ARC-AGI-21.1%Not measuredOnly R1 0528 measured
SimpleBench40.8%Not measuredOnly R1 0528 measured
LMArena Text: hard prompts14151044R1 0528
LMArena Text: expert13941007R1 0528
LMArena Text: hard prompts in English14221087R1 0528
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index50
Math benchmarks for R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 3.2 1B InstructLeader
AA Math Index76.00.0R1 0528
AIME 2025 (Artificial Analysis run)76.0%0.0%R1 0528
OTIS Mock AIME66.4%0.6%R1 0528
MATH level 596.6%Not measuredOnly R1 0528 measured
LMArena Text: math14001086R1 0528
LMArena Text: mathematical work14151098R1 0528
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 3.2 1B InstructLeader
MMLU Pro (Artificial Analysis run)84.9%20.0%R1 0528
LMArena Text: science14301055R1 0528
LMArena Text: medicine and healthcare14451009R1 0528
LMArena Text: legal and government14191071R1 0528
LMArena Text: business and finance14021000R1 0528
Neither model has a result on the other 13 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 3.2 1B InstructLeader
Design Arena: websites1149Not measuredOnly R1 0528 measured
Design Arena: UI components1103Not measuredOnly R1 0528 measured
Design Arena: game dev1097Not measuredOnly R1 0528 measured
Design Arena: data viz1182Not measuredOnly R1 0528 measured
Design Arena: 3D1111Not measuredOnly R1 0528 measured
Design Arena: all code categories1139Not measuredOnly R1 0528 measured
Design Arena: SVG1039Not measuredOnly R1 0528 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 R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 3.2 1B InstructLeader
IFBench39.6%22.8%R1 0528
Long context reasoning (AA-LCR)55.7%6.7%R1 0528
Fiction.LiveBench75.0%Not measuredOnly R1 0528 measured
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
LMArena Text14281055R1 0528
LMArena Text: instruction following13821031R1 0528
LMArena Text: creative writing14051033R1 0528
LMArena Text: English14371117R1 0528
LMArena Text: non-English1412973R1 0528
LMArena Text: Chinese1442959R1 0528
LMArena Text: German14041014R1 0528
LMArena Text: Japanese1391Not measuredOnly R1 0528 measured
LMArena Text: Korean1360Not measuredOnly R1 0528 measured
LMArena Text: French1417Not measuredOnly R1 0528 measured
LMArena Text: Spanish1411Not measuredOnly R1 0528 measured
LMArena Text: Russian1423941R1 0528
LMArena Text: Polish1428Not measuredOnly R1 0528 measured
LMArena Text: multi-turn14051030R1 0528
LMArena Text: longer queries13911050R1 0528
LMArena Text: writing and language14021030R1 0528
LMArena Text: entertainment and media14001025R1 0528
Neither model has a result on the other 25 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for R1 0528 and Llama 3.2 1B Instruct
BenchmarkR1 0528Llama 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

R1 0528$0.0016Fits 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

R1 0528$0.032Fits 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

R1 0528$0.122Exceeds 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 R1 0528 and Llama 3.2 1B Instruct
SpecificationDeepSeek R1 0528Meta Llama 3.2 1B Instruct
MakerDeepSeekMeta Llama
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025September 25, 2024
Context window164K60K
Input $/Mtok$0.50$0.027
Output $/Mtok$2.18$0.201
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it00
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 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 R1 0528 for a larger context window (164K against 60K tokens).

Which is cheaper, R1 0528 or Llama 3.2 1B Instruct?

R1 0528 costs $0.50 per million input tokens and $2.18 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, R1 0528 or Llama 3.2 1B Instruct?

R1 0528, with 164K tokens against 60K.

How many benchmarks is the R1 0528 vs Llama 3.2 1B Instruct comparison based on?

36 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 0 independent benchmarks 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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