Model vs model
DeepSeek V4.1 Flash 1017 vs Llama 4 Scout
The widest gap is on the AA-LCR long-document test, where DeepSeek V4.1 Flash 1017 scores 82.7 against 27.7 for Llama 4 Scout. That makes DeepSeek the pick for long inputs, and its 1.05M context window is far larger than Llama 4 Scout's 128K. DeepSeek also leads on the Intelligence Index, 45.1 to 6.5, and costs less blended, $0.26 per 1M tokens against $0.29.
At a glance
Longer bar means the bigger number.
Blended price · per 1M tokens, lower is better
Intelligence Index · Artificial Analysis, higher is better
Context window · tokens it can read at once
Where they sit among all 44 models
DeepSeek V4.1 Flash 1017 ranks 10th of the 43 models with an Intelligence Index, Llama 4 Scout 43rd. Up and to the left is smarter for less money. Hover or tap a dot for its numbers, then click it to compare that model with DeepSeek V4.1 Flash 1017.
Which one to pick
Pick DeepSeek V4.1 Flash 1017 if
- you want the lower bill ($0.26 vs $0.29 per 1M tokens blended)
- you want the higher overall score (45.1 vs 6.5)
- you care about AA-LCR (long documents): 82.7 vs 27.7
- you care about Humanity's Last Exam (hard reasoning): 43.3 vs 3.8
How smart is each one?
Of the 10 scores both models have, DeepSeek V4.1 Flash 1017 is ahead on every one.
Every intelligence score we have
Out of 100, higher is better. DeepSeek V4.1 Flash 1017 Llama 4 Scout
Overall
Intelligence Index
45.16.5
By area
Coding
8.2DeepSeek V4.1 Flash 1017: not scored yet
Math
14DeepSeek V4.1 Flash 1017: not scored yet
Agentic work
0.5DeepSeek V4.1 Flash 1017: not scored yet
By field of work
Finance & accounting
48.36.6Legal
46.48.1Healthcare & medicine
43.7Llama 4 Scout: not scored yet
Economics
50.510.5Engineering
45.29.5Strategy & operations
52.54.9
Benchmarks
Factual knowledge · SimpleQA
42.5Llama 4 Scout: not scored yet
Hard reasoning · Humanity's Last Exam
43.33.8Science · GPQA Diamond
58.7DeepSeek V4.1 Flash 1017: not scored yet
Knowledge · MMLU-Pro
75.2DeepSeek V4.1 Flash 1017: not scored yet
Competitive coding · LiveCodeBench
29.9DeepSeek V4.1 Flash 1017: not scored yet
Scientific coding · SciCode
55.921.3Terminal tasks · Terminal-Bench 4.0
33.80Tool use · τ-Bench Banking
3.3DeepSeek V4.1 Flash 1017: not scored yet
Instruction following · IFBench
39.5DeepSeek V4.1 Flash 1017: not scored yet
Long documents · AA-LCR
82.727.7Competition math · AIME 2025
14DeepSeek V4.1 Flash 1017: not scored yet
Competition math · AIME 2024
28.3DeepSeek V4.1 Flash 1017: not scored yet
Math · MATH-500
84.4DeepSeek V4.1 Flash 1017: not scored yet
The specs
| Spec | DeepSeek V4.1 Flash 1017 | Llama 4 Scout |
|---|---|---|
| Input price | $0.15 per 1M | $0.17 per 1M |
| Output price | $0.60 per 1M | $0.66 per 1M |
| Cost per test task | $0.2620 | Not tested |
| Context window | 1.05M | 128K |
| Max output | 393K | 8K |
| Accepts | Text, Image | Text, Image |
| Open weights | Yes | Yes |
| Lab | DeepSeek | Meta |
| Released | Oct 2026 | Apr 2025 |
| Knowledge cutoff | May 2025 | Aug 2024 |
Blended price weighs 3 parts input to 1 part output. List prices from models.dev. Cost per test task from Artificial Analysis.
Questions
Is DeepSeek V4.1 Flash 1017 or Llama 4 Scout cheaper?
DeepSeek V4.1 Flash 1017 is cheaper. It charges $0.15 per 1M input tokens and $0.60 per 1M output tokens, against $0.17 and $0.66 for Llama 4 Scout. Blended 3 parts input to 1 part output, that is $0.26 against $0.29 per 1M tokens.
Which is smarter, DeepSeek V4.1 Flash 1017 or Llama 4 Scout?
DeepSeek V4.1 Flash 1017 scores higher on the Artificial Analysis Intelligence Index, 45.1 against 6.5 for Llama 4 Scout. The biggest differences are on AA-LCR (82.7 vs 27.7) and Humanity's Last Exam (43.3 vs 3.8).
Which has the longer context window?
DeepSeek V4.1 Flash 1017 accepts 1.05M tokens, against 128K for Llama 4 Scout.
Can I compare DeepSeek V4.1 Flash 1017 and Llama 4 Scout on my own prompt?
Yes. ModelCompare sends one prompt to both models at once and shows each answer side by side with what it cost. You can try it free without an account.
Related comparisons
Where the numbers come from
Intelligence Index and benchmark scores are from Artificial Analysis, at each model’s highest effort, except SimpleQA, which is from Epoch AI, and the Uncensored score, which is from the UGI Leaderboard. The hallucination test is ours: questions with one exact answer, fake evidence pushed against right answers and requests that tempt a model to name packages that don’t exist. Prices are list prices from models.dev. Example answers come from our own test runs. Updated October 25, 2026.