DeepSeek V4 vs Llama 4 Scout
DeepSeek · China | Meta · US · Updated June 2026
Quick verdict
Pick DeepSeek V4 for near-frontier coding at ~1/12 the cost or open mit-licensed weights you can self-host. 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 (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 is china's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. 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 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.
Side-by-side specs
| Spec | DeepSeek V4 | 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) | $0.66/$1.98 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 | 80.6% | Not published |
| MRCR v2 @ 1M | Not published | 15% |
Who wins what
Near-frontier coding at ~1/12 the cost
DeepSeek V4
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost — and it is the newer of the two.
Open MIT-licensed weights you can self-host
DeepSeek V4
DeepSeek V4 lists open MIT-licensed weights you can self-host among its strengths; Llama 4 Scout does not.
No long-context surcharge
DeepSeek V4
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 10× more than DeepSeek V4'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 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's $0.66/$1.98 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 10× larger than DeepSeek V4'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, 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 near-frontier coding at ~1/12 the cost
→ DeepSeek V4
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
Origin (China vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
DeepSeek V4: where it fits
China's open-weight price earthquake — near-frontier capability at roughly a twelfth of GPT-5.5's cost. Released April 24, 2026 by DeepSeek, it is built for near-frontier coding at ~1/12 the cost, open MIT-licensed weights you can self-host, no long-context surcharge, and highest LiveCodeBench result.
Its trade-offs are real: this generic 'V4' entry mirrors DeepSeek V4-Pro's specs - see DeepSeek V4-Pro and DeepSeek V4-Flash for DeepSeek's actual named product tiers, trails the very best on hardest agentic coding, and text/code focused, less multimodal. At $0.66 in / $1.98 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 (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 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.
See pricingFrequently asked questions
Is DeepSeek V4 or Llama 4 Scout better for coding?
Public SWE-Bench figures are not available for Llama 4 Scout, so the honest test is your own repository — run an identical real bug through both. By design, DeepSeek V4 leans toward near-frontier coding at ~1/12 the cost 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 or Llama 4 Scout?
Llama 4 Scout is cheaper — $0.66/$1.98 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 and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you DeepSeek V4, 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 or Llama 4 Scout?
DeepSeek V4 — released April 24, 2026, about 13 months after Llama 4 Scout.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.