Llama 4 Scout vs Mistral Large 2407
Mistral Large 2407 leads Llama 4 Scout on the Artificial Analysis Intelligence Index, 7.6 to 6.5, and Llama 4 Scout costs 12.0x less per output token ($0.50 vs $6.00 per million). Updated October 21, 2026; 14 benchmarks have a result for both.
Llama 4 Scout or Mistral Large 2407: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Llama 4 Scout for output tokens 12.0x cheaper ($0.50 vs $6.00 per million) and a larger context window (1.31M against 131K tokens).
- Shared results
- 14
- Llama 4 Scout only
- 68
- Mistral Large 2407 only
- 3
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Mistral Large 2407
Mistral Large 2407 leads, 7.6 against 6.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Llama 4 Scout has a AA Agentic Index result, 0.5.
Directional only
Reasoning
GPQA Diamond
No call
Only Llama 4 Scout has a GPQA Diamond result, 47.0%.
Directional only
Math
AIME
No call
Only Llama 4 Scout has a AIME result, 19.0%.
Directional only
Knowledge
MMLU Pro
No call
Only Llama 4 Scout has a MMLU Pro result, 69.6%.
Directional only
Web design
Design Arena: websites
No call
Only Llama 4 Scout has a Design Arena: websites result, 750.
Directional only
Long documents
Listed context window
Llama 4 Scout
Llama 4 Scout takes 1.31M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Llama 4 Scout
Llama 4 Scout $0.0004, Mistral Large 2407 $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Llama 4 Scout has the number, $1.276.
Directional only
At a glance
- Coding index
- Llama 4 Scout not benchmarked, Mistral Large 2407 not benchmarked.
- Widest category
- Agentic work, 17.5 points apart on average over 1 shared benchmark.
- Widest single gap
- Long context reasoning (AA-LCR): Llama 4 Scout 27.7% against Mistral Large 2407 2.0%, 25.7 points apart on a 0 to 100 scale.
- Output price
- Llama 4 Scout $0.50 against Mistral Large 2407 $6.00 per million output tokens, 12.0x apart.
- Context window
- Llama 4 Scout takes 1.31M tokens, Mistral Large 2407 131K.
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 | Llama 4 Scout | Bars | Mistral Large 2407 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 3 | 0 | ||
| Math | 3 | 1 | ||
| Knowledge | 1 | 0 | ||
| General | 2 | 1 | ||
| All shared boards | 10 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Llama 4 Scout | Mistral Large 2407 | Leader |
|---|---|---|---|
| SWE-bench Verified | 9.1% | Not measured | Only Llama 4 Scout measured |
| AA Coding Index | 8.2 | Not measured | Only Llama 4 Scout measured |
| LiveCodeBench (Vals AI) | 38.5% | Not measured | Only Llama 4 Scout measured |
| LiveCodeBench (Artificial Analysis run) | 29.9% | 26.7% | Llama 4 Scout |
| SciCode | 21.3% | Not measured | Only Llama 4 Scout measured |
| Terminal-Bench Hard (Artificial Analysis run) | 1.5% | Not measured | Only Llama 4 Scout measured |
| LMArena Text: coding | 1286 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: software and IT | 1286 | Not measured | Only Llama 4 Scout measured |
| Neither model has a result on the other 31 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 4 Scout | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Intelligence Index | 6.5 | 7.6 | Mistral Large 2407 |
| Epoch Capabilities Index | 129.6 | Not measured | Only Llama 4 Scout measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | Llama 4 Scout | Mistral Large 2407 | Leader |
|---|---|---|---|
| AA Agentic Index | 0.5 | Not measured | Only Llama 4 Scout measured |
| τ²-Bench Telecom | 15.5% | 33.0% | Mistral Large 2407 |
| Berkeley Function Calling Leaderboard (BFCL) | 28.1% | Not measured | Only Llama 4 Scout measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Llama 4 Scout | Mistral Large 2407 | Leader |
|---|---|---|---|
| GPQA Diamond | 47.0% | Not measured | Only Llama 4 Scout measured |
| MMMU Pro (Vals AI) | 58.8% | Not measured | Only Llama 4 Scout measured |
| GPQA Diamond (Artificial Analysis run) | 58.7% | 47.2% | Llama 4 Scout |
| Humanity's Last Exam (Artificial Analysis run) | 3.8% | 2.9% | Llama 4 Scout |
| GPQA Diamond (Epoch AI run) | 51.8% | 49.0% | Llama 4 Scout |
| ARC-AGI | 0.5% | Not measured | Only Llama 4 Scout measured |
| ARC-AGI-2 | 0.0% | Not measured | Only Llama 4 Scout measured |
| SimpleBench | Not measured | 22.5% | Only Mistral Large 2407 measured |
| LMArena Text: hard prompts | 1266 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: expert | 1235 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: hard prompts in English | 1293 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: diagrams | 1125 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: homework | 1148 | Not measured | Only Llama 4 Scout measured |
| Kagi LLM Benchmark | 36.9% | Not measured | 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 index31
| Benchmark | Llama 4 Scout | Mistral Large 2407 | Leader |
|---|---|---|---|
| AIME | 19.0% | Not measured | Only Llama 4 Scout measured |
| MATH 500 (Vals AI) | 79.2% | Not measured | Only Llama 4 Scout measured |
| MGSM (Vals AI) | 88.0% | Not measured | Only Llama 4 Scout measured |
| AA Math Index | 14.0 | 0.0 | Llama 4 Scout |
| AIME 2025 (Artificial Analysis run) | 14.0% | 0.0% | Llama 4 Scout |
| OTIS Mock AIME | 7.8% | 8.5% | Mistral Large 2407 |
| MATH level 5 | 62.3% | 44.8% | Llama 4 Scout |
| LMArena Text: math | 1287 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: mathematical work | 1286 | Not measured | Only Llama 4 Scout measured |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Llama 4 Scout | Mistral Large 2407 | Leader |
|---|---|---|---|
| MMLU Pro | 69.6% | Not measured | Only Llama 4 Scout measured |
| LegalBench (Vals AI) | 72.0% | Not measured | Only Llama 4 Scout measured |
| CorpFin (Vals AI) | 46.8% | Not measured | Only Llama 4 Scout measured |
| TaxEval (Vals AI) | 55.2% | Not measured | Only Llama 4 Scout measured |
| MortgageTax (Vals AI) | 57.8% | Not measured | Only Llama 4 Scout measured |
| MedQA (Vals AI) | 50.9% | Not measured | Only Llama 4 Scout measured |
| MedCode (Vals AI) | 23.3% | Not measured | Only Llama 4 Scout measured |
| MMLU Pro (Artificial Analysis run) | 75.2% | 68.3% | Llama 4 Scout |
| MMLU | Not measured | 80.0% | Only Mistral Large 2407 measured |
| LMArena Text: science | 1278 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: medicine and healthcare | 1275 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: legal and government | 1285 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: business and finance | 1257 | Not measured | Only Llama 4 Scout measured |
| Neither model has a result on the other 5 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | Llama 4 Scout | Mistral Large 2407 | Leader |
|---|---|---|---|
| Design Arena: websites | 750 | Not measured | Only Llama 4 Scout measured |
| Design Arena: UI components | 768 | Not measured | Only Llama 4 Scout measured |
| Design Arena: game dev | 773 | Not measured | Only Llama 4 Scout measured |
| Design Arena: data viz | 896 | Not measured | Only Llama 4 Scout measured |
| Design Arena: all code categories | 790 | Not measured | 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 index21
| Benchmark | Llama 4 Scout | Mistral Large 2407 | Leader |
|---|---|---|---|
| SAGE (Vals AI) | 34.8% | Not measured | Only Llama 4 Scout measured |
| MedScribe (Vals AI) | 50.6% | Not measured | Only Llama 4 Scout measured |
| IFBench | 39.5% | 31.6% | Llama 4 Scout |
| Long context reasoning (AA-LCR) | 27.7% | 2.0% | Llama 4 Scout |
| DTBench | 57.9% | 61.2% | Mistral Large 2407 |
| LMCA | 12.0% | Not measured | Only Llama 4 Scout measured |
| Fiction.LiveBench | 36.0% | Not measured | Only Llama 4 Scout measured |
| Creative writing (Lech Mazur) | Not measured | 69.0% | Only Mistral Large 2407 measured |
| LMArena Text | 1279 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: instruction following | 1248 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: creative writing | 1249 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision | 1118 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: English | 1304 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: non-English | 1252 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Chinese | 1255 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: German | 1273 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Japanese | 1206 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Korean | 1207 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: French | 1283 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Spanish | 1278 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Russian | 1263 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: Polish | 1265 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: multi-turn | 1280 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: longer queries | 1265 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: writing and language | 1269 | Not measured | Only Llama 4 Scout measured |
| LMArena Text: entertainment and media | 1242 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: English | 1143 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: Chinese | 1106 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: reading text | 1117 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: humor | 1102 | Not measured | Only Llama 4 Scout measured |
| LMArena Vision: creative writing | 1126 | Not measured | Only 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
| Benchmark | Llama 4 Scout | Mistral Large 2407 | 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 | Meta Llama 4 Scout | Mistral Large 2407 |
|---|---|---|
| Maker | Meta Llama | Mistral AI |
| Weights | Open weights | No public weights listed |
| Released | April 5, 2025 | November 19, 2024 |
| Context window | 1.31M | 131K |
| Input $/Mtok | $0.175 | $2.00 |
| Output $/Mtok | $0.50 | $6.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | $1.276 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Llama 4 Scout better than Mistral Large 2407 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Llama 4 Scout for output tokens 12.0x cheaper ($0.50 vs $6.00 per million) and a larger context window (1.31M against 131K tokens).
Which is cheaper, Llama 4 Scout or Mistral Large 2407?
Llama 4 Scout costs $0.175 per million input tokens and $0.50 per million output tokens; Mistral Large 2407 costs $2.00 and $6.00. Cost per solved task is known for Llama 4 Scout only.
Which has the bigger context window, Llama 4 Scout or Mistral Large 2407?
Llama 4 Scout, with 1.31M tokens against 131K.
How many benchmarks is the Llama 4 Scout vs Mistral Large 2407 comparison based on?
14 benchmarks have a result for both models as of October 21, 2026. Llama 4 Scout's coding index rests on 0 independent benchmarks and Mistral Large 2407's on 0. Where only one model has a result, the page says so instead of naming a winner.