LLMBenchmark.ai

Enter opens the highlighted result. Esc closes.

DeepSeek V3.2 Exp vs Llama 4 Scout

Updated October 21, 2026. 48 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V3.2 Exp Replace Llama 4 Scout
DeepSeek V3.2 ExpDeepSeek
Coding
55.0
Overall
16.6
Output $/M
$0.41
48
Llama 4 ScoutMeta Llama
Coding
9.1
Overall
6.5
Output $/M
$0.30
0
vs boards won of 48 shared

Decision reading

Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 9.1 for Llama 4 Scout.

Pick Llama 4 Scout when output tokens cost 1.4x less, or when the context window is larger, 1.31M against 164K tokens.

Model ADeepSeek V3.2 ExpDeepSeek55.0/1002 independent benchmarksNo cost per solved taskModel BLlama 4 ScoutMeta Llama9.1/1001 independent benchmark$1.276 per solved task
Shared results
48
DeepSeek V3.2 Exp only
9
Llama 4 Scout only
32
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
DeepSeek V3.2 Exp 55.0 from 2 benchmarks, Llama 4 Scout 9.1 from 1 benchmark.
Widest category
Math, 48.6 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: DeepSeek V3.2 Exp 87.7 against Llama 4 Scout 14.0, 73.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against Llama 4 Scout $0.30 per million output tokens, 1.4x apart.
Context window
DeepSeek V3.2 Exp 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
CategoryDeepSeek V3.2 ExpBarsLlama 4 ScoutTies
Coding40
Intelligence20
Agentic work10
Reasoning50
Math40
Knowledge50
Web design50
General220
All shared boards480
CodingLike-for-likeFour of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V3.2 Exp and Llama 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 4 ScoutLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
SWE-bench VerifiedNot measured9.1%Only Llama 4 Scout measured
LMArena WebDev1272Not measuredOnly DeepSeek V3.2 Exp 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)78.9%29.9%DeepSeek V3.2 Exp
SciCodeNot measured21.3%Only Llama 4 Scout measured
Terminal-Bench Hard (Artificial Analysis run)31.1%1.5%DeepSeek V3.2 Exp
WeirdML39.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text: coding14381286DeepSeek V3.2 Exp
LMArena Text: software and IT14381286DeepSeek V3.2 Exp
LMArena WebDev: HTML1281Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V3.2 Exp and Llama 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 4 ScoutLeader
AA Intelligence Index16.66.5DeepSeek V3.2 Exp
Epoch Capabilities Index144.9129.6DeepSeek V3.2 Exp
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 Exp and Llama 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 4 ScoutLeader
AA Agentic IndexNot measured0.5Only Llama 4 Scout measured
τ²-Bench Telecom33.9%15.5%DeepSeek V3.2 Exp
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 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 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)79.7%58.7%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%3.8%DeepSeek V3.2 Exp
GPQA Diamond (Epoch AI run)Not measured51.8%Only Llama 4 Scout measured
ARC-AGINot measured0.5%Only Llama 4 Scout measured
ARC-AGI-2Not measured0.0%Only Llama 4 Scout measured
LMArena Text: hard prompts14291266DeepSeek V3.2 Exp
LMArena Text: expert14221235DeepSeek V3.2 Exp
LMArena Text: hard prompts in English14441293DeepSeek V3.2 Exp
LMArena Vision: diagramsNot measured1125Only Llama 4 Scout measured
LMArena Vision: homeworkNot measured1148Only Llama 4 Scout measured
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V3.2 Exp and Llama 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 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 Index87.714.0DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%14.0%DeepSeek V3.2 Exp
OTIS Mock AIMENot measured7.8%Only Llama 4 Scout measured
MATH level 5Not measured62.3%Only Llama 4 Scout measured
LMArena Text: math14221287DeepSeek V3.2 Exp
LMArena Text: mathematical work14331286DeepSeek V3.2 Exp
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 DeepSeek V3.2 Exp and Llama 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 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)85.0%75.2%DeepSeek V3.2 Exp
LMArena Text: science14431278DeepSeek V3.2 Exp
LMArena Text: medicine and healthcare14681275DeepSeek V3.2 Exp
LMArena Text: legal and government14191285DeepSeek V3.2 Exp
LMArena Text: business and finance14121257DeepSeek V3.2 Exp
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 DeepSeek V3.2 Exp and Llama 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 4 ScoutLeader
Design Arena: websites1179750DeepSeek V3.2 Exp
Design Arena: UI components1166767DeepSeek V3.2 Exp
Design Arena: game dev1145773DeepSeek V3.2 Exp
Design Arena: data viz1152896DeepSeek V3.2 Exp
Design Arena: 3D1148Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: all code categories1171790DeepSeek V3.2 Exp
Design Arena: SVG1031Not 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 index220
General benchmarks for DeepSeek V3.2 Exp and Llama 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 4 ScoutLeader
SAGE (Vals AI)Not measured34.8%Only Llama 4 Scout measured
MedScribe (Vals AI)Not measured50.6%Only Llama 4 Scout measured
IFBench54.1%39.5%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%27.7%DeepSeek V3.2 Exp
DTBench87.7%57.9%DeepSeek V3.2 Exp
LMCA29.1%12.0%DeepSeek V3.2 Exp
Fiction.LiveBench83.3%36.0%DeepSeek V3.2 Exp
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251279DeepSeek V3.2 Exp
LMArena Text: instruction following14011248DeepSeek V3.2 Exp
LMArena Text: creative writing14041249DeepSeek V3.2 Exp
LMArena VisionNot measured1118Only Llama 4 Scout measured
LMArena Text: English14411304DeepSeek V3.2 Exp
LMArena Text: non-English14041252DeepSeek V3.2 Exp
LMArena Text: Chinese14611255DeepSeek V3.2 Exp
LMArena Text: German14401273DeepSeek V3.2 Exp
LMArena Text: Japanese13311206DeepSeek V3.2 Exp
LMArena Text: Korean13721207DeepSeek V3.2 Exp
LMArena Text: French14341283DeepSeek V3.2 Exp
LMArena Text: Spanish14411278DeepSeek V3.2 Exp
LMArena Text: Russian14121263DeepSeek V3.2 Exp
LMArena Text: Polish14351265DeepSeek V3.2 Exp
LMArena Text: multi-turn14221280DeepSeek V3.2 Exp
LMArena Text: longer queries14181265DeepSeek V3.2 Exp
LMArena Text: writing and language14071269DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071242DeepSeek V3.2 Exp
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 14 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 4 Scout
BenchmarkDeepSeek V3.2 ExpLlama 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

DeepSeek V3.2 Exp$0.0005Fits in one request
Llama 4 Scout$0.0003Fits in one request

Llama 4 Scout costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
Llama 4 Scout$0.0059Fits in one request

Llama 4 Scout costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds 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 DeepSeek V3.2 Exp and Llama 4 Scout
SpecificationDeepSeek V3.2 ExpMeta Llama 4 Scout
MakerDeepSeekMeta Llama
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025April 5, 2025
Context window164K1.31M
Input $/Mtok$0.27$0.10
Output $/Mtok$0.41$0.30
Coding index55.09.1
Independent measurements behind it21
Cost per solved taskNot computable$1.276
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than Llama 4 Scout for coding?

Pick DeepSeek V3.2 Exp for coding: it scores 55.0 on the Vibe Coding Index against 9.1 for Llama 4 Scout. Pick Llama 4 Scout when output tokens cost 1.4x less, or when the context window is larger, 1.31M against 164K tokens.

Which is cheaper, DeepSeek V3.2 Exp or Llama 4 Scout?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 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, DeepSeek V3.2 Exp or Llama 4 Scout?

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

How many benchmarks is the DeepSeek V3.2 Exp vs Llama 4 Scout comparison based on?

48 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 2 independent benchmarks and Llama 4 Scout's on 1. Where only one model has a result, the page says so instead of naming a winner.

Compare next