Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Microsoft Phi-4 for strong reasoning for a small 14b open-weight model or mit-licensed — fully self-hostable at no per-token cost. On a tight budget at scale, Llama 4 Scout is the value pick.
Llama 4 Scout (Meta) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Microsoft Phi-4 is microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. They diverge most on price and context window — each quantified below from the models' real specs.
Key differences
Context window: Llama 4 Scout holds 610× more — 10M (~15,000 pages) vs 16K (~25 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Llama 4 Scout is the newer model by about 3 months (released April 2025), usually meaning fresher training data and capabilities.
Specifications
Spec
Llama 4 Scout
Microsoft Phi-4
Provider
Meta (US)
Microsoft (US)
Released
April 2025
January 10, 2025
Context window
10M (~15,000 pages)
16K (~25 pages)
Price (in/out)
Open weight (self-host / free)
$0.07/$0.14 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
15%
Not published
Who wins what
Largest advertised context (10M): Llama 4 Scout — Its 10M window holds about 610× more than Microsoft Phi-4's 16K 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 — Microsoft Phi-4 is comparatively weak here — no first-party per-token API; hosted prices are third-party
Strong reasoning for a small 14B open-weight model: Microsoft Phi-4 — Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
MIT-licensed — fully self-hostable at no per-token cost: Microsoft Phi-4 — Microsoft Phi-4 lists mIT-licensed — fully self-hostable at no per-token cost among its strengths; Llama 4 Scout does not.
Runs on modest or local hardware: Microsoft Phi-4 — Microsoft Phi-4 lists runs on modest or local hardware among its strengths; Llama 4 Scout does not.
Lowest cost at scale: Llama 4 Scout — Its weights are open, so at volume you pay for your own hardware instead of Microsoft Phi-4's $0.07/$0.14 per 1M tokens.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 610× larger than Microsoft Phi-4's 16K, 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 Microsoft Phi-4, 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 largest advertised context (10m): Llama 4 Scout — It is specifically built for that.
Anyone whose priority is strong reasoning for a small 14b open-weight model: Microsoft Phi-4 — That is its strongest area.
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 are real: 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.
Microsoft Phi-4: where it fits
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Released January 10, 2025 by Microsoft, it is built for strong reasoning for a small 14B open-weight model, mIT-licensed — fully self-hostable at no per-token cost, runs on modest or local hardware, and very cheap hosted inference at about $0.07/$0.14.
Its trade-offs: a tiny 16K context — by far the smallest window in this comparison, text only — no image, audio or video input, an early-2025 small model, outclassed on hard tasks by 2026 flagships, and no first-party per-token API; hosted prices are third-party. At $0.07 in / $0.14 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Llama 4 Scout and Microsoft Phi-4 overlap enough that the right pick depends on your specific job. Llama 4 Scout costs less per token; Llama 4 Scout holds the larger context; and each leads in its own area — Llama 4 Scout for largest advertised context (10m), Microsoft Phi-4 for strong reasoning for a small 14b open-weight model. Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Llama 4 Scout or Microsoft Phi-4 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 Scout leans toward largest advertised context (10m) while Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Microsoft Phi-4?
Llama 4 Scout is cheaper — Open weight (self-host / free) vs $0.07/$0.14 per 1M tokens.
Which has the bigger context window?
Llama 4 Scout — 10M vs 16K, about 610× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Microsoft Phi-4 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, Llama 4 Scout or Microsoft Phi-4?
Llama 4 Scout — released April 2025, about 3 months after Microsoft Phi-4.
Llama 4 Scout vs Microsoft Phi-4
Meta · US | Microsoft · US · Updated June 2026
Quick verdict
Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Microsoft Phi-4 for strong reasoning for a small 14b open-weight model or mit-licensed — fully self-hostable at no per-token cost. On a tight budget at scale, Llama 4 Scout is the value pick.
Llama 4 Scout (Meta) and Microsoft Phi-4 (Microsoft) are two of the models people most often weigh against each other in 2026. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Microsoft Phi-4 is microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. 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 610× more — 10M (~15,000 pages) vs 16K (~25 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Llama 4 Scout is the newer model by about 3 months (released April 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Llama 4 Scout
Microsoft Phi-4
Provider
Meta (US)
Microsoft (US)
Released
April 2025
January 10, 2025
Context window
10M (~15,000 pages)
16K (~25 pages)
Price (in/out)
Open weight (self-host / free)
$0.07/$0.14 per 1M tokens
Open weight?
Yes — self-hostable
Yes — self-hostable
Modalities
text, image, code
text, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
15%
Not published
Who wins what
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 610× more than Microsoft Phi-4's 16K 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
Microsoft Phi-4 is comparatively weak here — no first-party per-token API; hosted prices are third-party
Strong reasoning for a small 14B open-weight model
Microsoft Phi-4
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
MIT-licensed — fully self-hostable at no per-token cost
Microsoft Phi-4
Microsoft Phi-4 lists mIT-licensed — fully self-hostable at no per-token cost among its strengths; Llama 4 Scout does not.
Runs on modest or local hardware
Microsoft Phi-4
Microsoft Phi-4 lists runs on modest or local hardware among its strengths; Llama 4 Scout does not.
Lowest cost at scale
Llama 4 Scout
Its weights are open, so at volume you pay for your own hardware instead of Microsoft Phi-4's $0.07/$0.14 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 610× larger than Microsoft Phi-4's 16K, 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 Microsoft Phi-4, 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 largest advertised context (10m)
→ Llama 4 Scout
It is specifically built for that.
Anyone whose priority is strong reasoning for a small 14b open-weight model
→ Microsoft Phi-4
That is its strongest area.
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 are real: 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.
Microsoft Phi-4: where it fits
Microsoft's MIT-licensed 14B open model — strong reasoning for its size and very cheap, but a tiny 16K context and text-only. Released January 10, 2025 by Microsoft, it is built for strong reasoning for a small 14B open-weight model, mIT-licensed — fully self-hostable at no per-token cost, runs on modest or local hardware, and very cheap hosted inference at about $0.07/$0.14.
Its trade-offs: a tiny 16K context — by far the smallest window in this comparison, text only — no image, audio or video input, an early-2025 small model, outclassed on hard tasks by 2026 flagships, and no first-party per-token API; hosted prices are third-party. At $0.07 in / $0.14 out per million tokens, it sits in the budget price band.
The bottom line for this matchup
Llama 4 Scout and Microsoft Phi-4 overlap enough that the right pick depends on your specific job. Llama 4 Scout costs less per token; Llama 4 Scout holds the larger context; and each leads in its own area — Llama 4 Scout for largest advertised context (10m), Microsoft Phi-4 for strong reasoning for a small 14b open-weight model. Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Llama 4 Scout and Microsoft Phi-4 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 Scout or Microsoft Phi-4 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 Scout leans toward largest advertised context (10m) while Microsoft Phi-4 leans toward strong reasoning for a small 14b open-weight model, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Microsoft Phi-4?
Llama 4 Scout is cheaper — Open weight (self-host / free) vs $0.07/$0.14 per 1M tokens.
Which has the bigger context window?
Llama 4 Scout — 10M vs 16K, about 610× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and Microsoft Phi-4 together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Microsoft Phi-4 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, Llama 4 Scout or Microsoft Phi-4?
Llama 4 Scout — released April 2025, about 3 months after Microsoft Phi-4.
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.