Pick DeepSeek V4-Pro for open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable or 1m-token context with up to 384k output tokens. 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-Pro (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-Pro is deepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. 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 10× more — 10M (~15,000 pages) vs 1M (~1,500 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-Pro is the newer model by about 13 months (released April 24, 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-Pro
Llama 4 Scout
Provider
DeepSeek (China)
Meta (US)
Released
April 24, 2026
April 2025
Context window
1M (~1,500 pages)
10M (~15,000 pages)
Price (in/out)
$0.435/$0.87 per 1M tokens
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Open-weight 1.6T MoE (~49B active) — DeepSeek's largest, self-hostable: DeepSeek V4-Pro — DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices — and it is the newer of the two.
1M-token context with up to 384K output tokens: DeepSeek V4-Pro — Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Permanent low pricing at $0.435/$0.87 per million, set May 2026: DeepSeek V4-Pro — DeepSeek V4-Pro lists permanent low pricing at $0.435/$0.87 per million, set May 2026 among its strengths; Llama 4 Scout does not.
Largest advertised context (10M): Llama 4 Scout — Its 10M window holds about 10× more than DeepSeek V4-Pro's 1M 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-Pro 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-Pro's $0.435/$0.87 per 1M tokens.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 10× larger than DeepSeek V4-Pro's 1M, 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-Pro, 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 open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable: DeepSeek V4-Pro — 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-Pro — Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V4-Pro: where it fits
DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. Released April 24, 2026 by DeepSeek, it is built for open-weight 1.6T MoE (~49B active) — DeepSeek's largest, self-hostable, 1M-token context with up to 384K output tokens, permanent low pricing at $0.435/$0.87 per million, set May 2026, and sources place it near the top of open-weight coding, around 80 on SWE-Bench Verified.
Its trade-offs are real: independent SWE-Bench Verified placement is inconsistent across sources, pro and Pro-Max variants are quoted with different scores, text and code only — no image, audio or video, and overlaps DeepSeek V4 and V3.2 already in this comparison. At $0.435 in / $0.87 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-Pro (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-Pro 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-Pro leans toward open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable 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-Pro or Llama 4 Scout?
Llama 4 Scout is cheaper — $0.435/$0.87 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Llama 4 Scout — 10M vs 1M, about 10× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4-Pro and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Pro, 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-Pro or Llama 4 Scout?
DeepSeek V4-Pro — released April 24, 2026, about 13 months after Llama 4 Scout.
DeepSeek V4-Pro vs Llama 4 Scout
DeepSeek · China | Meta · US · Updated June 2026
Quick verdict
Pick DeepSeek V4-Pro for open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable or 1m-token context with up to 384k output tokens. 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-Pro (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-Pro is deepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. 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 10× more — 10M (~15,000 pages) vs 1M (~1,500 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-Pro is the newer model by about 13 months (released April 24, 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.
DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices — and it is the newer of the two.
1M-token context with up to 384K output tokens
DeepSeek V4-Pro
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Permanent low pricing at $0.435/$0.87 per million, set May 2026
DeepSeek V4-Pro
DeepSeek V4-Pro lists permanent low pricing at $0.435/$0.87 per million, set May 2026 among its strengths; Llama 4 Scout does not.
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 10× more than DeepSeek V4-Pro's 1M 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-Pro 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-Pro's $0.435/$0.87 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 10× larger than DeepSeek V4-Pro's 1M, 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-Pro, and on millions of tokens that margin decides the monthly bill.
Someone analysing very long documents or codebases
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-Pro
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V4-Pro: where it fits
DeepSeek's 1.6-trillion-parameter open-weight model at $0.435/$0.87 — strong open-weight coding and a 1M context at a fraction of flagship prices. Released April 24, 2026 by DeepSeek, it is built for open-weight 1.6T MoE (~49B active) — DeepSeek's largest, self-hostable, 1M-token context with up to 384K output tokens, permanent low pricing at $0.435/$0.87 per million, set May 2026, and sources place it near the top of open-weight coding, around 80 on SWE-Bench Verified.
Its trade-offs are real: independent SWE-Bench Verified placement is inconsistent across sources, pro and Pro-Max variants are quoted with different scores, text and code only — no image, audio or video, and overlaps DeepSeek V4 and V3.2 already in this comparison. At $0.435 in / $0.87 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-Pro (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-Pro 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-Pro 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-Pro leans toward open-weight 1.6t moe (~49b active) — deepseek's largest, self-hostable 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-Pro or Llama 4 Scout?
Llama 4 Scout is cheaper — $0.435/$0.87 per 1M tokens vs Open weight (self-host / free).
Which has the bigger context window?
Llama 4 Scout — 10M vs 1M, about 10× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both DeepSeek V4-Pro and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4-Pro, 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-Pro or Llama 4 Scout?
DeepSeek V4-Pro — released April 24, 2026, about 13 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.