DeepSeek V4 Pro 0813 vs Llama 4 Scout
DeepSeek V4 Pro 0813 leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 36.0 to 6.5, and Llama 4 Scout costs 4.0x less per output token ($0.50 vs $1.98 per million). Updated October 22, 2026; 47 benchmarks have a result for both.
DeepSeek V4 Pro 0813 or Llama 4 Scout: which to pick
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; Llama 4 Scout has none, so the two cannot be ranked on coding.
Pick Llama 4 Scout for output tokens 4.0x cheaper ($0.50 vs $1.98 per million) and a larger context window (1.31M against 1.05M tokens).
- Shared results
- 47
- DeepSeek V4 Pro 0813 only
- 31
- Llama 4 Scout only
- 35
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only DeepSeek V4 Pro 0813 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 36.0 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 41.3 against 0.5.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 92.4% against 47.0%.
Same board, both measured
Math
AIME
No call
Only Llama 4 Scout has a AIME result, 19.0%.
Directional only
Knowledge
MMLU Pro
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 leads, 87.0% against 69.6%.
Same board, both measured
Web design
Design Arena: websites
No call
Only Llama 4 Scout has a Design Arena: websites result, 750.
Directional only
General
Vals Index
No call
Only DeepSeek V4 Pro 0813 has a Vals Index result, 53.0%.
Directional only
Long documents
Listed context window
Llama 4 Scout
Llama 4 Scout takes 1.31M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 4 Scout
DeepSeek V4 Pro 0813 $0.0016, Llama 4 Scout $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0813
DeepSeek V4 Pro 0813 $0.10, Llama 4 Scout $1.276.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0813 56.1 from 5 benchmarks, Llama 4 Scout not benchmarked.
- Widest category
- Math, 47.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: DeepSeek V4 Pro 0813 98.6% against Llama 4 Scout 7.8%, 90.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0813 $1.98 against Llama 4 Scout $0.50 per million output tokens, 4.0x apart.
- Context window
- DeepSeek V4 Pro 0813 takes 1.05M 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.
| Category | DeepSeek V4 Pro 0813 | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 9 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 9 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 47 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 9.1% | Only Llama 4 Scout measured |
| LMArena WebDev | 1582 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| AA Coding Index | 68.8 | 8.2 | DeepSeek V4 Pro 0813 |
| LiveCodeBench (Vals AI) | 87.5% | 38.5% | DeepSeek V4 Pro 0813 |
| SWE-bench Verified (Vals AI) | 96.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 4 (Vals AI) | 14.1% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench (Vals AI) | 82.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Code migration (Vals AI) | 41.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| IOI (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench 2.1 (Vals AI) | 54.7% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Vibe Code Bench 1-100 (Vals AI) | 17.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SkillsBench (Vals AI) | 53.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| CyberBench (Vals AI) | 72.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.9% | Only Llama 4 Scout measured |
| SciCode | 51.0% | 21.3% | DeepSeek V4 Pro 0813 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 1.5% | Only Llama 4 Scout measured |
| FrontierCode | 28.5% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| WeirdML | 66.2% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ALE-Bench | 1403.2 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: coding | 1469 | 1286 | DeepSeek V4 Pro 0813 |
| LMArena Text: software and IT | 1462 | 1286 | DeepSeek V4 Pro 0813 |
| LMArena WebDev: React | 1588 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena WebDev: HTML | 1566 | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 36.0 | 6.5 | DeepSeek V4 Pro 0813 |
| Epoch Capabilities Index | 155.6 | 129.6 | DeepSeek V4 Pro 0813 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | 41.3 | 0.5 | DeepSeek V4 Pro 0813 |
| SREBench (Vals AI) | 1.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Finance Agent (Vals AI) | 50.4% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Tax Agent Bench (Vals AI) | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Legal Research Bench (Vals AI) | 40.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 10.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Excel Modeling Benchmark (Vals AI) | 52.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Terminal-Bench-Science (Vals AI) | 4.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| τ²-Bench Telecom | Not measured | 15.5% | Only Llama 4 Scout measured |
| APEX-Agents | 47.3% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 28.1% | Only Llama 4 Scout measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | 92.4% | 47.0% | DeepSeek V4 Pro 0813 |
| MMMU Pro (Vals AI) | Not measured | 58.8% | Only Llama 4 Scout measured |
| GPQA Diamond (Artificial Analysis run) | 92.8% | 58.7% | DeepSeek V4 Pro 0813 |
| Humanity's Last Exam (Artificial Analysis run) | 41.0% | 3.8% | DeepSeek V4 Pro 0813 |
| GPQA Diamond (Epoch AI run) | 91.7% | 51.8% | DeepSeek V4 Pro 0813 |
| ARC-AGI | 90.5% | 0.5% | DeepSeek V4 Pro 0813 |
| ARC-AGI-2 | 61.3% | 0.0% | DeepSeek V4 Pro 0813 |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| Mystery game puzzles | 43.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: hard prompts | 1458 | 1266 | DeepSeek V4 Pro 0813 |
| LMArena Text: expert | 1459 | 1235 | DeepSeek V4 Pro 0813 |
| LMArena Text: hard prompts in English | 1463 | 1293 | DeepSeek V4 Pro 0813 |
| LMArena Vision: diagrams | Not measured | 1125 | Only Llama 4 Scout measured |
| LMArena Vision: homework | Not measured | 1148 | Only Llama 4 Scout measured |
| Kagi LLM Benchmark | Not measured | 36.9% | Only 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 index30
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| AIME | Not measured | 19.0% | Only Llama 4 Scout measured |
| MATH 500 (Vals AI) | Not measured | 79.2% | Only Llama 4 Scout measured |
| MGSM (Vals AI) | Not measured | 88.0% | Only Llama 4 Scout measured |
| AA Math Index | Not measured | 14.0 | Only Llama 4 Scout measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 14.0% | Only Llama 4 Scout measured |
| OTIS Mock AIME | 98.6% | 7.8% | DeepSeek V4 Pro 0813 |
| FrontierMath, tiers 1 to 3 | 64.6% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| FrontierMath, tier 4 | 26.8% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| ProofBench | 50.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| MATH level 5 | Not measured | 62.3% | Only Llama 4 Scout measured |
| LMArena Text: math | 1441 | 1287 | DeepSeek V4 Pro 0813 |
| LMArena Text: mathematical work | 1443 | 1286 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | 87.0% | 69.6% | DeepSeek V4 Pro 0813 |
| LegalBench (Vals AI) | 82.4% | 72.0% | DeepSeek V4 Pro 0813 |
| CorpFin (Vals AI) | 65.4% | 46.8% | DeepSeek V4 Pro 0813 |
| TaxEval (Vals AI) | 73.1% | 55.2% | DeepSeek V4 Pro 0813 |
| MortgageTax (Vals AI) | Not measured | 57.8% | Only Llama 4 Scout measured |
| MedQA (Vals AI) | Not measured | 50.9% | Only Llama 4 Scout measured |
| MedCode (Vals AI) | 42.5% | 23.3% | DeepSeek V4 Pro 0813 |
| MMLU Pro (Artificial Analysis run) | Not measured | 75.2% | Only Llama 4 Scout measured |
| SimpleQA Verified | 52.9% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| LMArena Text: science | 1457 | 1278 | DeepSeek V4 Pro 0813 |
| LMArena Text: medicine and healthcare | 1446 | 1275 | DeepSeek V4 Pro 0813 |
| LMArena Text: legal and government | 1459 | 1285 | DeepSeek V4 Pro 0813 |
| LMArena Text: business and finance | 1442 | 1257 | DeepSeek V4 Pro 0813 |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 750 | Only Llama 4 Scout measured |
| Design Arena: UI components | Not measured | 765 | Only Llama 4 Scout measured |
| Design Arena: game dev | Not measured | 772 | Only Llama 4 Scout measured |
| Design Arena: data viz | Not measured | 896 | Only Llama 4 Scout measured |
| Design Arena: all code categories | Not measured | 789 | Only Llama 4 Scout measured |
| Neither model has a result on the other 4 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| Vals Index | 53.0% | Not measured | Only DeepSeek V4 Pro 0813 measured |
| SAGE (Vals AI) | Not measured | 34.8% | Only Llama 4 Scout measured |
| MedScribe (Vals AI) | 80.2% | 50.6% | DeepSeek V4 Pro 0813 |
| IFBench | Not measured | 39.5% | Only Llama 4 Scout measured |
| Long context reasoning (AA-LCR) | 80.3% | 27.7% | DeepSeek V4 Pro 0813 |
| DTBench | 93.9% | 57.9% | DeepSeek V4 Pro 0813 |
| LMCA | 45.5% | 12.0% | DeepSeek V4 Pro 0813 |
| Fiction.LiveBench | Not measured | 36.0% | Only Llama 4 Scout measured |
| LMArena Text | 1446 | 1279 | DeepSeek V4 Pro 0813 |
| LMArena Text: instruction following | 1448 | 1248 | DeepSeek V4 Pro 0813 |
| LMArena Text: creative writing | 1439 | 1249 | DeepSeek V4 Pro 0813 |
| LMArena Vision | Not measured | 1118 | Only Llama 4 Scout measured |
| LMArena Text: English | 1457 | 1304 | DeepSeek V4 Pro 0813 |
| LMArena Text: non-English | 1431 | 1252 | DeepSeek V4 Pro 0813 |
| LMArena Text: Chinese | 1481 | 1255 | DeepSeek V4 Pro 0813 |
| LMArena Text: German | Not measured | 1273 | Only Llama 4 Scout measured |
| LMArena Text: Japanese | Not measured | 1206 | Only Llama 4 Scout measured |
| LMArena Text: Korean | 1449 | 1207 | DeepSeek V4 Pro 0813 |
| LMArena Text: French | 1446 | 1283 | DeepSeek V4 Pro 0813 |
| LMArena Text: Spanish | 1435 | 1278 | DeepSeek V4 Pro 0813 |
| LMArena Text: Russian | 1448 | 1263 | DeepSeek V4 Pro 0813 |
| LMArena Text: Polish | Not measured | 1265 | Only Llama 4 Scout measured |
| LMArena Text: multi-turn | 1454 | 1280 | DeepSeek V4 Pro 0813 |
| LMArena Text: longer queries | 1458 | 1265 | DeepSeek V4 Pro 0813 |
| LMArena Text: writing and language | 1443 | 1269 | DeepSeek V4 Pro 0813 |
| LMArena Text: entertainment and media | 1424 | 1242 | DeepSeek V4 Pro 0813 |
| LMArena Vision: English | Not measured | 1143 | Only Llama 4 Scout measured |
| LMArena Vision: Chinese | Not measured | 1106 | Only Llama 4 Scout measured |
| LMArena Vision: reading text | Not measured | 1117 | Only Llama 4 Scout measured |
| LMArena Vision: humor | Not measured | 1102 | Only Llama 4 Scout measured |
| LMArena Vision: creative writing | Not measured | 1126 | Only Llama 4 Scout measured |
| Neither model has a result on the other 17 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0813 | Llama 4 Scout | Leader |
|---|---|---|---|
| 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 4 Scout costs less
Repository review
50K input + 3K output tokens
Llama 4 Scout costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | DeepSeek V4 Pro 0813 | Meta Llama 4 Scout |
|---|---|---|
| Maker | DeepSeek | Meta Llama |
| Weights | Open weights | Open weights |
| Released | August 12, 2026 | April 5, 2025 |
| Context window | 1.05M | 1.31M |
| Input $/Mtok | $0.66 | $0.175 |
| Output $/Mtok | $1.98 | $0.50 |
| Coding index | 56.1 | Not benchmarked |
| Independent measurements behind it | 5 | 0 |
| Cost per solved task | $0.10 | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0813 better than Llama 4 Scout for coding?
Only DeepSeek V4 Pro 0813 has a coding score, 56.1 on the Vibe Coding Index; Llama 4 Scout has none, so the two cannot be ranked on coding. Pick Llama 4 Scout for output tokens 4.0x cheaper ($0.50 vs $1.98 per million) and a larger context window (1.31M against 1.05M tokens).
Which is cheaper, DeepSeek V4 Pro 0813 or Llama 4 Scout?
DeepSeek V4 Pro 0813 costs $0.66 per million input tokens and $1.98 per million output tokens; Llama 4 Scout costs $0.175 and $0.50. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0813 costs $0.10 and Llama 4 Scout $1.276.
Which has the bigger context window, DeepSeek V4 Pro 0813 or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 1.05M.
How many benchmarks is the DeepSeek V4 Pro 0813 vs Llama 4 Scout comparison based on?
47 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0813's coding index rests on 5 independent benchmarks and Llama 4 Scout's on 0. Where only one model has a result, the page says so instead of naming a winner.