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R1 0528 vs Llama 4 Scout

Updated October 21, 2026. 50 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace R1 0528 Replace Llama 4 Scout
R1 0528DeepSeek
Coding
71.4
Overall
13.1
Output $/M
$2.15
50
Llama 4 ScoutMeta Llama
Coding
9.1
Overall
6.5
Output $/M
$0.30
0
vs boards won of 50 shared

Decision reading

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 9.1 for Llama 4 Scout.

Pick Llama 4 Scout when output tokens cost 7.2x less, or when the context window is larger, 1.31M against 164K tokens.

Model AR1 0528DeepSeek71.4/1001 independent benchmarkNo cost per solved taskModel BLlama 4 ScoutMeta Llama9.1/1001 independent benchmark$1.276 per solved task
Shared results
50
R1 0528 only
9
Llama 4 Scout only
30
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 71.4 from 1 benchmark, Llama 4 Scout 9.1 from 1 benchmark.
Widest category
Math, 42.9 points apart on average over 6 shared benchmarks.
Widest single gap
AA Math Index: R1 0528 76.0 against Llama 4 Scout 14.0, 62.0 points apart on a 0 to 100 scale.
Output price
R1 0528 $2.15 against Llama 4 Scout $0.30 per million output tokens, 7.2x apart.
Context window
R1 0528 takes 164K tokens, Llama 4 Scout 1.31M.

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 4 ScoutTies
Coding40
Intelligence10
Agentic work10
Reasoning80
Math60
Knowledge50
Web design50
General200
All shared boards500
CodingLike-for-likeFour of these boards feed the Vibe Coding Index40
Coding benchmarks for R1 0528 and Llama 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
Aider polyglot71.4%Not measuredOnly R1 0528 measured
SWE-bench VerifiedNot measured9.1%Only Llama 4 Scout measured
AA Coding IndexNot measured8.2Only Llama 4 Scout measured
LiveCodeBench (Vals AI)Not measured38.5%Only Llama 4 Scout measured
LiveCodeBench (Artificial Analysis run)77.0%29.9%R1 0528
SciCodeNot measured21.3%Only Llama 4 Scout measured
Terminal-Bench Hard (Artificial Analysis run)15.9%1.5%R1 0528
WeirdML41.6%Not measuredOnly R1 0528 measured
ALE-Bench804.1Not measuredOnly R1 0528 measured
LMArena Text: coding14271286R1 0528
LMArena Text: software and IT14351286R1 0528
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for R1 0528 and Llama 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
AA Intelligence Index13.16.5R1 0528
Epoch Capabilities IndexNot measured129.6Only Llama 4 Scout measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for R1 0528 and Llama 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
AA Agentic IndexNot measured0.5Only Llama 4 Scout measured
τ²-Bench Telecom36.5%15.5%R1 0528
METR time horizons53.8%Not measuredOnly R1 0528 measured
DeepResearch Bench35.1%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 index80
Reasoning benchmarks for R1 0528 and Llama 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
GPQA DiamondNot measured47.0%Only Llama 4 Scout measured
MMMU Pro (Vals AI)Not measured58.8%Only Llama 4 Scout measured
GPQA Diamond (Artificial Analysis run)81.3%58.7%R1 0528
Humanity's Last Exam (Artificial Analysis run)15.8%3.8%R1 0528
GPQA Diamond (Epoch AI run)76.3%51.8%R1 0528
ARC-AGI21.2%0.5%R1 0528
ARC-AGI-21.1%0.0%R1 0528
SimpleBench40.8%Not measuredOnly R1 0528 measured
LMArena Text: hard prompts14151266R1 0528
LMArena Text: expert13941235R1 0528
LMArena Text: hard prompts in English14221293R1 0528
LMArena Vision: diagramsNot measured1125Only Llama 4 Scout measured
LMArena Vision: homeworkNot measured1148Only Llama 4 Scout measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index60
Math benchmarks for R1 0528 and Llama 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
AIMENot measured19.0%Only Llama 4 Scout measured
MATH 500 (Vals AI)Not measured79.2%Only Llama 4 Scout measured
MGSM (Vals AI)Not measured88.0%Only Llama 4 Scout measured
AA Math Index76.014.0R1 0528
AIME 2025 (Artificial Analysis run)76.0%14.0%R1 0528
OTIS Mock AIME66.4%7.8%R1 0528
MATH level 596.6%62.3%R1 0528
LMArena Text: math14001287R1 0528
LMArena Text: mathematical work14151286R1 0528
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for R1 0528 and Llama 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
MMLU ProNot measured69.6%Only Llama 4 Scout measured
LegalBench (Vals AI)Not measured72.0%Only Llama 4 Scout measured
CorpFin (Vals AI)Not measured46.8%Only Llama 4 Scout measured
TaxEval (Vals AI)Not measured55.2%Only Llama 4 Scout measured
MortgageTax (Vals AI)Not measured57.8%Only Llama 4 Scout measured
MedQA (Vals AI)Not measured50.9%Only Llama 4 Scout measured
MedCode (Vals AI)Not measured23.3%Only Llama 4 Scout measured
MMLU Pro (Artificial Analysis run)84.9%75.2%R1 0528
LMArena Text: science14301278R1 0528
LMArena Text: medicine and healthcare14451275R1 0528
LMArena Text: legal and government14191285R1 0528
LMArena Text: business and finance14021257R1 0528
Neither model has a result on the other 5 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for R1 0528 and Llama 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
Design Arena: websites1149750R1 0528
Design Arena: UI components1102767R1 0528
Design Arena: game dev1098773R1 0528
Design Arena: data viz1182896R1 0528
Design Arena: 3D1111Not measuredOnly R1 0528 measured
Design Arena: all code categories1139790R1 0528
Design Arena: SVG1038Not 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 index200
General benchmarks for R1 0528 and Llama 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
SAGE (Vals AI)Not measured34.8%Only Llama 4 Scout measured
MedScribe (Vals AI)Not measured50.6%Only Llama 4 Scout measured
IFBench39.6%39.5%R1 0528
Long context reasoning (AA-LCR)55.7%27.7%R1 0528
DTBenchNot measured57.9%Only Llama 4 Scout measured
LMCANot measured12.0%Only Llama 4 Scout measured
Fiction.LiveBench75.0%36.0%R1 0528
Creative writing (Lech Mazur)81.9%Not measuredOnly R1 0528 measured
LMArena Text14281279R1 0528
LMArena Text: instruction following13821248R1 0528
LMArena Text: creative writing14051249R1 0528
LMArena VisionNot measured1118Only Llama 4 Scout measured
LMArena Text: English14371304R1 0528
LMArena Text: non-English14121252R1 0528
LMArena Text: Chinese14421255R1 0528
LMArena Text: German14041273R1 0528
LMArena Text: Japanese13911206R1 0528
LMArena Text: Korean13601207R1 0528
LMArena Text: French14171283R1 0528
LMArena Text: Spanish14111278R1 0528
LMArena Text: Russian14231263R1 0528
LMArena Text: Polish14281265R1 0528
LMArena Text: multi-turn14051280R1 0528
LMArena Text: longer queries13911265R1 0528
LMArena Text: writing and language14021269R1 0528
LMArena Text: entertainment and media14001242R1 0528
LMArena Vision: EnglishNot measured1143Only Llama 4 Scout measured
LMArena Vision: ChineseNot measured1106Only Llama 4 Scout measured
LMArena Vision: reading textNot measured1117Only Llama 4 Scout measured
LMArena Vision: humorNot measured1102Only Llama 4 Scout measured
LMArena Vision: creative writingNot measured1126Only Llama 4 Scout measured
Neither model has a result on the other 15 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 4 Scout
BenchmarkR1 0528Llama 4 ScoutLeader
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 4 Scout$0.0003Fits in one request

Llama 4 Scout costs less

Repository review

50K input + 3K output tokens

R1 0528$0.031Fits in one request
Llama 4 Scout$0.0059Fits in one request

Llama 4 Scout costs less

Agent loop

200K input + 10K output tokens

R1 0528$0.121Exceeds the context window
Llama 4 Scout$0.023Fits in one request

Llama 4 Scout 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 4 Scout
SpecificationDeepSeek R1 0528Meta Llama 4 Scout
MakerDeepSeekMeta Llama
WeightsOpen weightsOpen weights
ReleasedMay 28, 2025April 5, 2025
Context window164K1.31M
Input $/Mtok$0.50$0.10
Output $/Mtok$2.15$0.30
Coding index71.49.1
Independent measurements behind it11
Cost per solved taskNot computable$1.276
SourceOpenRouter listingOpenRouter listing

Questions

Is R1 0528 better than Llama 4 Scout for coding?

Pick R1 0528 for coding: it scores 71.4 on the Vibe Coding Index against 9.1 for Llama 4 Scout. Pick Llama 4 Scout when output tokens cost 7.2x less, or when the context window is larger, 1.31M against 164K tokens.

Which is cheaper, R1 0528 or Llama 4 Scout?

R1 0528 costs $0.50 per million input tokens and $2.15 per million output tokens; Llama 4 Scout costs $0.10 and $0.30. Cost per solved task is known for Llama 4 Scout only.

Which has the bigger context window, R1 0528 or Llama 4 Scout?

Llama 4 Scout, with 1.31M tokens against 164K.

How many benchmarks is the R1 0528 vs Llama 4 Scout comparison based on?

50 benchmarks have a result for both models as of October 21, 2026. R1 0528's coding index rests on 1 independent benchmark and Llama 4 Scout's on 1. Where only one model has a result, the page says so instead of naming a winner.

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