Both are Meta models. Llama 4 Scout is the newer, generally stronger default; reach for Llama 4 Maverick when its lower price or specific profile matters more than the latest capabilities.
Llama 4 Maverick and Llama 4 Scout are both Meta models, so the real question is not which lab to trust but which tier fits your workload and budget. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
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.
Specifications
Spec
Llama 4 Maverick
Llama 4 Scout
Provider
Meta (US)
Meta (US)
Released
April 2025
April 2025
Context window
1M (~1,500 pages)
10M (~15,000 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Open weights, 1M context: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Strong image + text understanding: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Self-hostable: Llama 4 Maverick — A core design strength of Llama 4 Maverick.
Largest advertised context (10M): Llama 4 Scout — A core design strength of Llama 4 Scout.
Open weights, single-GPU friendly: Llama 4 Scout — A core design strength of Llama 4 Scout.
Self-hosted, data-private deployment: Llama 4 Scout — A core design strength of Llama 4 Scout.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 10× larger, fitting roughly 15,000 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases: Llama 4 Scout — Larger 10M window fits more in one prompt.
Anyone whose priority is open weights, 1m context: Llama 4 Maverick — It is specifically built for that.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — That is its strongest area.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs are real: needs serious hardware to self-host, and trails closed frontier on reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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
Because Llama 4 Maverick and Llama 4 Scout come from the same lab (Meta), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Llama 4 Scout is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Llama 4 Scout and drop down only with a concrete reason.
Frequently asked questions
Is Llama 4 Maverick 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, Llama 4 Maverick leans toward open weights, 1m context while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Maverick or Llama 4 Scout?
They are priced almost identically, so cost will not decide between them.
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.
Should I upgrade from Llama 4 Scout to Llama 4 Maverick?
Since both are Meta models, the newer one (Llama 4 Scout) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Llama 4 Maverick or Llama 4 Scout?
They were released around the same time (April 2025 and April 2025).
Llama 4 Maverick vs Llama 4 Scout
Meta · US | Meta · US · Updated June 2026
Quick verdict
Both are Meta models. Llama 4 Scout is the newer, generally stronger default; reach for Llama 4 Maverick when its lower price or specific profile matters more than the latest capabilities.
Llama 4 Maverick and Llama 4 Scout are both Meta models, so the real question is not which lab to trust but which tier fits your workload and budget. Llama 4 Maverick is meta's open-weight 1M-context multimodal model for self-hosted deployments. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Since both come from the same lab, the comparison below focuses on the tier-and-cost trade-offs that actually separate them.
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.
Side-by-side specs
Spec
Llama 4 Maverick
Llama 4 Scout
Provider
Meta (US)
Meta (US)
Released
April 2025
April 2025
Context window
1M (~1,500 pages)
10M (~15,000 pages)
Price (in/out)
Open weight (self-host / free)
Open weight (self-host / free)
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Open weights, 1M context
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Strong image + text understanding
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Self-hostable
Llama 4 Maverick
A core design strength of Llama 4 Maverick.
Largest advertised context (10M)
Llama 4 Scout
A core design strength of Llama 4 Scout.
Open weights, single-GPU friendly
Llama 4 Scout
A core design strength of Llama 4 Scout.
Self-hosted, data-private deployment
Llama 4 Scout
A core design strength of Llama 4 Scout.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 10× larger, fitting roughly 15,000 pages in one prompt.
Which should you pick?
Someone analysing very long documents or codebases
→ Llama 4 Scout
Larger 10M window fits more in one prompt.
Anyone whose priority is open weights, 1m context
→ Llama 4 Maverick
It is specifically built for that.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
That is its strongest area.
Llama 4 Maverick: where it fits
Meta's open-weight 1M-context multimodal model for self-hosted deployments. Released April 2025 by Meta, it is built for open weights, 1M context, strong image + text understanding, self-hostable, and 400B MoE, 17B active.
Its trade-offs are real: needs serious hardware to self-host, and trails closed frontier on reasoning. As an open-weight model, its running cost is your own hardware rather than a per-token fee.
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
Because Llama 4 Maverick and Llama 4 Scout come from the same lab (Meta), they share the same training philosophy and ecosystem — the decision is purely tier vs. cost. Llama 4 Scout is the more capable, more recent option; the other earns its place only when its price or latency profile fits a specific job better. Most teams should default to Llama 4 Scout and drop down only with a concrete reason.
Want both Llama 4 Maverick 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 Llama 4 Maverick 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, Llama 4 Maverick leans toward open weights, 1m context while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Maverick or Llama 4 Scout?
They are priced almost identically, so cost will not decide between them.
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.
Should I upgrade from Llama 4 Scout to Llama 4 Maverick?
Since both are Meta models, the newer one (Llama 4 Scout) is usually the better default unless you need a specific cost or latency profile from the other.
Which is newer, Llama 4 Maverick or Llama 4 Scout?
They were released around the same time (April 2025 and April 2025).
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.