DeepSeek V4 Pro 0423 vs Llama 4 Scout
Updated October 21, 2026. 49 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V4 Pro 0423 for coding: it scores 35.3 on the Vibe Coding Index against 9.1 for Llama 4 Scout.
Pick Llama 4 Scout when output tokens cost 6.4x less, or when the context window is larger, 1.31M against 1.05M tokens.
- Shared results
- 49
- DeepSeek V4 Pro 0423 only
- 34
- Llama 4 Scout only
- 31
- 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.
Coding
Vibe Coding Index, four public coding benchmarks
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 scores 35.3 against 9.1.
One benchmark behind each score
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 26.3 against 0.5.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 89.4% 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 0423
DeepSeek V4 Pro 0423 leads, 87.3% against 69.6%.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 1235 against 750.
Same board, both measured
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 0423 $0.0019, Llama 4 Scout $0.0003 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.57, Llama 4 Scout $1.276.
Published attempt cost and pass rate
At a glance
- Coding index
- DeepSeek V4 Pro 0423 35.3 from 1 benchmark, Llama 4 Scout 9.1 from 1 benchmark.
- Widest category
- Web design, 53.8 points apart on average over 5 shared benchmarks.
- Widest single gap
- OTIS Mock AIME: DeepSeek V4 Pro 0423 96.7% against Llama 4 Scout 7.8%, 88.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $1.9105 against Llama 4 Scout $0.30 per million output tokens, 6.4x apart.
- Context window
- DeepSeek V4 Pro 0423 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 0423 | Bars | Llama 4 Scout | Ties |
|---|---|---|---|---|
| Coding | 4 | 0 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 5 | 0 | ||
| Math | 3 | 0 | ||
| Knowledge | 9 | 0 | ||
| Web design | 5 | 0 | ||
| General | 20 | 0 | ||
| All shared boards | 49 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index40
| Benchmark | DeepSeek V4 Pro 0423 | Llama 4 Scout | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 9.1% | Only Llama 4 Scout measured |
| LMArena WebDev | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| AA Coding Index | 59.4 | 8.2 | DeepSeek V4 Pro 0423 |
| LiveCodeBench (Vals AI) | 87.5% | 38.5% | DeepSeek V4 Pro 0423 |
| SWE-bench Verified (Vals AI) | 77.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Code migration (Vals AI) | 26.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 29.9% | Only Llama 4 Scout measured |
| SciCode | Not measured | 21.3% | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 1.5% | Only Llama 4 Scout measured |
| FrontierCode | 17.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| WeirdML | 48.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ALE-Bench | 1006.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: coding | 1470 | 1286 | DeepSeek V4 Pro 0423 |
| LMArena Text: software and IT | 1469 | 1286 | DeepSeek V4 Pro 0423 |
| LMArena WebDev: React | 1441 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: HTML | 1435 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 17 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0423 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 6.5 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | 129.6 | DeepSeek V4 Pro 0423 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Pro 0423 | Llama 4 Scout | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | 0.5 | DeepSeek V4 Pro 0423 |
| Design Arena: agents building web apps | 1000 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building full-stack apps | 948 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building Godot games | 1059 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Finance Agent (Vals AI) | 44.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Tax Agent Bench (Vals AI) | 25.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Legal Research Bench (Vals AI) | 23.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Excel Modeling Benchmark (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| τ²-Bench Telecom | Not measured | 15.5% | Only Llama 4 Scout measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4 Pro 0423 | Llama 4 Scout | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | 47.0% | DeepSeek V4 Pro 0423 |
| MMMU Pro (Vals AI) | Not measured | 58.8% | Only Llama 4 Scout measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 58.7% | Only Llama 4 Scout measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 3.8% | Only Llama 4 Scout measured |
| GPQA Diamond (Epoch AI run) | 90.9% | 51.8% | DeepSeek V4 Pro 0423 |
| ARC-AGI | Not measured | 0.5% | Only Llama 4 Scout measured |
| ARC-AGI-2 | Not measured | 0.0% | Only Llama 4 Scout measured |
| Chess puzzles | 19.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Mystery game puzzles | 17.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts | 1461 | 1266 | DeepSeek V4 Pro 0423 |
| LMArena Text: expert | 1463 | 1235 | DeepSeek V4 Pro 0423 |
| LMArena Text: hard prompts in English | 1466 | 1293 | DeepSeek V4 Pro 0423 |
| LMArena Vision: diagrams | Not measured | 1125 | Only Llama 4 Scout measured |
| LMArena Vision: homework | Not measured | 1148 | Only Llama 4 Scout measured |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4 Pro 0423 | 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 | 96.7% | 7.8% | DeepSeek V4 Pro 0423 |
| FrontierMath, tiers 1 to 3 | 45.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProofBench | 16.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MATH level 5 | Not measured | 62.3% | Only Llama 4 Scout measured |
| LMArena Text: math | 1443 | 1287 | DeepSeek V4 Pro 0423 |
| LMArena Text: mathematical work | 1455 | 1286 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 1 board here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index90
| Benchmark | DeepSeek V4 Pro 0423 | Llama 4 Scout | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | 69.6% | DeepSeek V4 Pro 0423 |
| LegalBench (Vals AI) | 80.3% | 72.0% | DeepSeek V4 Pro 0423 |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | 46.8% | DeepSeek V4 Pro 0423 |
| TaxEval (Vals AI) | 72.1% | 55.2% | DeepSeek V4 Pro 0423 |
| 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) | 40.5% | 23.3% | DeepSeek V4 Pro 0423 |
| MMLU Pro (Artificial Analysis run) | Not measured | 75.2% | Only Llama 4 Scout measured |
| SimpleQA Verified | 47.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: science | 1470 | 1278 | DeepSeek V4 Pro 0423 |
| LMArena Text: medicine and healthcare | 1466 | 1275 | DeepSeek V4 Pro 0423 |
| LMArena Text: legal and government | 1464 | 1285 | DeepSeek V4 Pro 0423 |
| LMArena Text: business and finance | 1443 | 1257 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 3 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4 Pro 0423 | Llama 4 Scout | Leader |
|---|---|---|---|
| Design Arena: websites | 1235 | 750 | DeepSeek V4 Pro 0423 |
| Design Arena: UI components | 1221 | 767 | DeepSeek V4 Pro 0423 |
| Design Arena: game dev | 1229 | 773 | DeepSeek V4 Pro 0423 |
| Design Arena: data viz | 1203 | 896 | DeepSeek V4 Pro 0423 |
| Design Arena: 3D | 1254 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: all code categories | 1240 | 790 | DeepSeek V4 Pro 0423 |
| Design Arena: SVG | 1139 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: ASCII art | 1148 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
| Benchmark | DeepSeek V4 Pro 0423 | Llama 4 Scout | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 62.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SAGE (Vals AI) | Not measured | 34.8% | Only Llama 4 Scout measured |
| MedScribe (Vals AI) | 75.1% | 50.6% | DeepSeek V4 Pro 0423 |
| IFBench | Not measured | 39.5% | Only Llama 4 Scout measured |
| Long context reasoning (AA-LCR) | Not measured | 27.7% | Only Llama 4 Scout measured |
| DTBench | 90.7% | 57.9% | DeepSeek V4 Pro 0423 |
| LMCA | 41.2% | 12.0% | DeepSeek V4 Pro 0423 |
| Fiction.LiveBench | Not measured | 36.0% | Only Llama 4 Scout measured |
| Surface Evolver Bench | 40.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CL-bench Life | 13.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text | 1451 | 1279 | DeepSeek V4 Pro 0423 |
| LMArena Text: instruction following | 1445 | 1248 | DeepSeek V4 Pro 0423 |
| LMArena Text: creative writing | 1446 | 1249 | DeepSeek V4 Pro 0423 |
| LMArena Vision | Not measured | 1118 | Only Llama 4 Scout measured |
| LMArena Text: English | 1459 | 1304 | DeepSeek V4 Pro 0423 |
| LMArena Text: non-English | 1439 | 1252 | DeepSeek V4 Pro 0423 |
| LMArena Text: Chinese | 1481 | 1255 | DeepSeek V4 Pro 0423 |
| LMArena Text: German | 1458 | 1273 | DeepSeek V4 Pro 0423 |
| LMArena Text: Japanese | 1446 | 1206 | DeepSeek V4 Pro 0423 |
| LMArena Text: Korean | 1409 | 1207 | DeepSeek V4 Pro 0423 |
| LMArena Text: French | 1473 | 1283 | DeepSeek V4 Pro 0423 |
| LMArena Text: Spanish | 1457 | 1278 | DeepSeek V4 Pro 0423 |
| LMArena Text: Russian | 1453 | 1263 | DeepSeek V4 Pro 0423 |
| LMArena Text: Polish | 1461 | 1265 | DeepSeek V4 Pro 0423 |
| LMArena Text: multi-turn | 1468 | 1280 | DeepSeek V4 Pro 0423 |
| LMArena Text: longer queries | 1458 | 1265 | DeepSeek V4 Pro 0423 |
| LMArena Text: writing and language | 1447 | 1269 | DeepSeek V4 Pro 0423 |
| LMArena Text: entertainment and media | 1431 | 1242 | DeepSeek V4 Pro 0423 |
| 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 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | 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 0423 | Meta Llama 4 Scout |
|---|---|---|
| Maker | DeepSeek | Meta Llama |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | April 5, 2025 |
| Context window | 1.05M | 1.31M |
| Input $/Mtok | $0.9553 | $0.10 |
| Output $/Mtok | $1.9105 | $0.30 |
| Coding index | 35.3 | 9.1 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.57 | $1.276 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than Llama 4 Scout for coding?
Pick DeepSeek V4 Pro 0423 for coding: it scores 35.3 on the Vibe Coding Index against 9.1 for Llama 4 Scout. Pick Llama 4 Scout when output tokens cost 6.4x less, or when the context window is larger, 1.31M against 1.05M tokens.
Which is cheaper, DeepSeek V4 Pro 0423 or Llama 4 Scout?
DeepSeek V4 Pro 0423 costs $0.9553 per million input tokens and $1.9105 per million output tokens; Llama 4 Scout costs $0.10 and $0.30. Per solved SWE-bench Verified task, DeepSeek V4 Pro 0423 costs $0.57 and Llama 4 Scout $1.276.
Which has the bigger context window, DeepSeek V4 Pro 0423 or Llama 4 Scout?
Llama 4 Scout, with 1.31M tokens against 1.05M.
How many benchmarks is the DeepSeek V4 Pro 0423 vs Llama 4 Scout comparison based on?
49 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0423'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.