Pick DeepSeek V4.1 Flash for software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0) or 1m-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak). Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. On a tight budget at scale, Llama 4 Scout is the value pick.
DeepSeek V4.1 Flash (DeepSeek, China) and Llama 4 Scout (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. DeepSeek V4.1 Flash is deepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Llama 4 Scout holds 9.5× more — 10M (~15,000 pages) vs 1.05M tokens (~1,573 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: DeepSeek V4.1 Flash is the newer model by about 17 months (released September 10, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
DeepSeek V4.1 Flash
Llama 4 Scout
Provider
DeepSeek (China)
Meta (US)
Released
September 10, 2026
April 2025
Context window
1.05M tokens (~1,573 pages)
10M (~15,000 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Software engineering (DeepSWE v1.1: 74.2, essentially level with Claude Opus 5's 74.0): DeepSeek V4.1 Flash — DeepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens — and it is the newer of the two.
1M-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak): DeepSeek V4.1 Flash — Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Native multimodal vision, added over the text-only V4-Flash it replaces on most traffic: DeepSeek V4.1 Flash — DeepSeek V4.1 Flash lists native multimodal vision, added over the text-only V4-Flash it replaces on most traffic among its strengths; Llama 4 Scout does not.
Largest advertised context (10M): Llama 4 Scout — Its 10M window holds about 9.5× more than DeepSeek V4.1 Flash's 1.05M tokens in a single prompt.
Open weights, single-GPU friendly: Llama 4 Scout — The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller — and it carries the larger 10M context.
Self-hosted, data-private deployment: Llama 4 Scout — Llama 4 Scout lists self-hosted, data-private deployment among its strengths; DeepSeek V4.1 Flash does not.
Lowest cost at scale: Llama 4 Scout — Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V4.1 Flash's $0.15/$0.6 per 1M tokens.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 9.5× larger than DeepSeek V4.1 Flash's 1.05M tokens, fitting roughly 15,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume: Llama 4 Scout — At Open weight (self-host / free) it undercuts DeepSeek V4.1 Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases: Llama 4 Scout — Larger 10M window fits more in one prompt.
Anyone whose priority is software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0): DeepSeek V4.1 Flash — It is specifically built for that.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — That is its strongest area.
An enterprise with regional data-residency rules: Llama 4 Scout or DeepSeek V4.1 Flash — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V4.1 Flash: where it fits
DeepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens. Released September 10, 2026 by DeepSeek, it is built for software engineering (DeepSWE v1.1: 74.2, essentially level with Claude Opus 5's 74.0), 1M-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak), native multimodal vision, added over the text-only V4-Flash it replaces on most traffic, and mIT-licensed open weights, self-hostable.
Its trade-offs are real: the $0.15/$0.60 pricing is an off-peak rate — cost reportedly doubles during peak-demand hours, new Causal Encoder-Decoder architecture (8B active for input, 16B for output) is unproven at broader scale versus DeepSeek's mainline V4/V4-Pro line, and positioned as a cost/speed tier, not a frontier-reasoning flagship — DeepSeek kept V4-Pro running in parallel rather than replacing it outright. At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
Llama 4 Scout: where it fits
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Released April 2025 by Meta, it is built for largest advertised context (10M), open weights, single-GPU friendly, self-hosted, data-private deployment, and retrieval over very long inputs.
Its trade-offs: effective recall degrades far below 10M, and ~15% on long-context multi-needle reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V4.1 Flash (China) and Llama 4 Scout (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Llama 4 Scout is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Frequently asked questions
Is DeepSeek V4.1 Flash or Llama 4 Scout better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4.1 Flash leans toward software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0) while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4.1 Flash or Llama 4 Scout?
Llama 4 Scout is cheaper — $0.15/$0.6 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Llama 4 Scout — 10M vs 1.05M tokens, about 9.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4.1 Flash and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4.1 Flash, Llama 4 Scout and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek V4.1 Flash or Llama 4 Scout?
DeepSeek V4.1 Flash — released September 10, 2026, about 17 months after Llama 4 Scout.
DeepSeek V4.1 Flash vs Llama 4 Scout
DeepSeek · China | Meta · US · Updated June 2026
Quick verdict
Pick DeepSeek V4.1 Flash for software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0) or 1m-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak). Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. On a tight budget at scale, Llama 4 Scout is the value pick.
DeepSeek V4.1 Flash (DeepSeek, China) and Llama 4 Scout (Meta, US) line up two different AI ecosystems against each other — a comparison that is as much about cost philosophy and openness as raw capability. DeepSeek V4.1 Flash is deepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences at a glance
▸Context window: Llama 4 Scout holds 9.5× more — 10M (~15,000 pages) vs 1.05M tokens (~1,573 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: DeepSeek V4.1 Flash is the newer model by about 17 months (released September 10, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a China-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
DeepSeek V4.1 Flash
Llama 4 Scout
Provider
DeepSeek (China)
Meta (US)
Released
September 10, 2026
April 2025
Context window
1.05M tokens (~1,573 pages)
10M (~15,000 pages)
Price (in/out)
$0.15/$0.6 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Software engineering (DeepSWE v1.1: 74.2, essentially level with Claude Opus 5's 74.0)
DeepSeek V4.1 Flash
DeepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens — and it is the newer of the two.
1M-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak)
DeepSeek V4.1 Flash
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Native multimodal vision, added over the text-only V4-Flash it replaces on most traffic
DeepSeek V4.1 Flash
DeepSeek V4.1 Flash lists native multimodal vision, added over the text-only V4-Flash it replaces on most traffic among its strengths; Llama 4 Scout does not.
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 9.5× more than DeepSeek V4.1 Flash's 1.05M tokens in a single prompt.
Open weights, single-GPU friendly
Llama 4 Scout
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller — and it carries the larger 10M context.
Self-hosted, data-private deployment
Llama 4 Scout
Llama 4 Scout lists self-hosted, data-private deployment among its strengths; DeepSeek V4.1 Flash does not.
Lowest cost at scale
Llama 4 Scout
Its weights are open, so at volume you pay for your own hardware instead of DeepSeek V4.1 Flash's $0.15/$0.6 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 9.5× larger than DeepSeek V4.1 Flash's 1.05M tokens, fitting roughly 15,000 pages in one prompt.
Which should you pick?
A cost-sensitive startup shipping high volume
→ Llama 4 Scout
At Open weight (self-host / free) it undercuts DeepSeek V4.1 Flash, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
→ Llama 4 Scout
Larger 10M window fits more in one prompt.
Anyone whose priority is software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0)
→ DeepSeek V4.1 Flash
It is specifically built for that.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
That is its strongest area.
An enterprise with regional data-residency rules
→ Llama 4 Scout or DeepSeek V4.1 Flash
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V4.1 Flash: where it fits
DeepSeek's September 2026 flash-tier update adds native vision and a 1M-token window to its cost-efficient V4 line, at $0.15/$0.60 per million tokens. Released September 10, 2026 by DeepSeek, it is built for software engineering (DeepSWE v1.1: 74.2, essentially level with Claude Opus 5's 74.0), 1M-token context window at a fraction of frontier pricing ($0.15/$0.60 per million tokens off-peak), native multimodal vision, added over the text-only V4-Flash it replaces on most traffic, and mIT-licensed open weights, self-hostable.
Its trade-offs are real: the $0.15/$0.60 pricing is an off-peak rate — cost reportedly doubles during peak-demand hours, new Causal Encoder-Decoder architecture (8B active for input, 16B for output) is unproven at broader scale versus DeepSeek's mainline V4/V4-Pro line, and positioned as a cost/speed tier, not a frontier-reasoning flagship — DeepSeek kept V4-Pro running in parallel rather than replacing it outright. At $0.15 in / $0.6 out per million tokens, it sits in the budget price band.
Llama 4 Scout: where it fits
The 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Released April 2025 by Meta, it is built for largest advertised context (10M), open weights, single-GPU friendly, self-hosted, data-private deployment, and retrieval over very long inputs.
Its trade-offs: effective recall degrades far below 10M, and ~15% on long-context multi-needle reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
The bottom line for this matchup
This is less "which is smarter" and more "which ecosystem fits." DeepSeek V4.1 Flash (China) and Llama 4 Scout (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. Llama 4 Scout is the cheaper option, which matters at volume. The pragmatic move is to run one real task through both and judge the outputs against your own constraints — including where your data is allowed to be processed.
Want both DeepSeek V4.1 Flash and Llama 4 Scout without two subscriptions? LumiChats gives you these plus 40+ models under one ₹69/day pass (about $1/day) — draft with one, cross-check with the other.
Is DeepSeek V4.1 Flash or Llama 4 Scout better for coding?
Public SWE-Bench figures are not available for either model, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4.1 Flash leans toward software engineering (deepswe v1.1: 74.2, essentially level with claude opus 5's 74.0) while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, DeepSeek V4.1 Flash or Llama 4 Scout?
Llama 4 Scout is cheaper — $0.15/$0.6 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Llama 4 Scout — 10M vs 1.05M tokens, about 9.5× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4.1 Flash and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4.1 Flash, Llama 4 Scout and 40+ others under one ₹69/day pass (about $1/day), so you can draft with one and cross-check with the other instead of buying two subscriptions.
Which is newer, DeepSeek V4.1 Flash or Llama 4 Scout?
DeepSeek V4.1 Flash — released September 10, 2026, about 17 months after Llama 4 Scout.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.