Pick Command A+ for cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model or a 218b-total/25b-active moe released with open weights (apache 2.0) on hugging face. Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly.
Command A+ (Cohere, Canada) 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. Command A+ is cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences
Context window: Llama 4 Scout holds 39× more — 10M (~15,000 pages) vs 256K (unconfirmed) (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Command A+ is the newer model by about 14 months (released May 19, 2026), usually meaning fresher training data and capabilities.
Ecosystem: this is a Canada-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Specifications
Spec
Command A+
Llama 4 Scout
Provider
Cohere (Canada)
Meta (US)
Released
May 19, 2026
April 2025
Context window
256K (unconfirmed) (~384 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, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Cohere's newer, more efficient flagship - runs on just two H100 GPUs, unusually lean for a 218B-parameter model: Command A+ — Cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support — and it is the newer of the two.
A 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face: Command A+ — Command A+ lists a 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face among its strengths; Llama 4 Scout does not.
Supports 48 languages, including all official EU languages: Command A+ — Command A+ lists supports 48 languages, including all official EU languages among its strengths; Llama 4 Scout does not.
Largest advertised context (10M): Llama 4 Scout — Its 10M window holds about 39× more than Command A+'s 256K (unconfirmed) 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; Command A+ does not.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 39× larger than Command A+'s 256K (unconfirmed), 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 cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model: Command A+ — 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 Command A+ — Origin (Canada vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Command A+: where it fits
Cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support. Released May 19, 2026 by Cohere, it is built for cohere's newer, more efficient flagship - runs on just two H100 GPUs, unusually lean for a 218B-parameter model, a 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face, supports 48 languages, including all official EU languages, and positioned by Cohere as its 'most efficient and performant model to date'.
Its trade-offs are real: no official context window or per-token price published yet - both are estimated/unavailable, newer release with less independent benchmark verification than established flagships, and enterprise/multilingual focus rather than topping general reasoning leaderboards. 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
This is less "which is smarter" and more "which ecosystem fits." Command A+ (Canada) and Llama 4 Scout (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. 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 Command A+ 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, Command A+ leans toward cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Command A+ 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 256K (unconfirmed), about 39× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Command A+ and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you Command A+, 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, Command A+ or Llama 4 Scout?
Command A+ — released May 19, 2026, about 14 months after Llama 4 Scout.
Command A+ vs Llama 4 Scout
Cohere · Canada | Meta · US · Updated June 2026
Quick verdict
Pick Command A+ for cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model or a 218b-total/25b-active moe released with open weights (apache 2.0) on hugging face. Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly.
Command A+ (Cohere, Canada) 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. Command A+ is cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. Their biggest split is context window, and the breakdown below shows exactly how that plays out for your workload.
Key differences at a glance
▸Context window: Llama 4 Scout holds 39× more — 10M (~15,000 pages) vs 256K (unconfirmed) (~384 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Command A+ is the newer model by about 14 months (released May 19, 2026), usually meaning fresher training data and capabilities.
▸Ecosystem: this is a Canada-vs-US matchup — they differ in pricing philosophy, data-residency options, and tooling ecosystems, not only benchmarks.
Side-by-side specs
Spec
Command A+
Llama 4 Scout
Provider
Cohere (Canada)
Meta (US)
Released
May 19, 2026
April 2025
Context window
256K (unconfirmed) (~384 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, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
Cohere's newer, more efficient flagship - runs on just two H100 GPUs, unusually lean for a 218B-parameter model
Command A+
Cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support — and it is the newer of the two.
A 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face
Command A+
Command A+ lists a 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face among its strengths; Llama 4 Scout does not.
Supports 48 languages, including all official EU languages
Command A+
Command A+ lists supports 48 languages, including all official EU languages among its strengths; Llama 4 Scout does not.
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 39× more than Command A+'s 256K (unconfirmed) 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; Command A+ does not.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 39× larger than Command A+'s 256K (unconfirmed), 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 cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model
→ Command A+
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 Command A+
Origin (Canada vs US) affects where data is processed and which compliance regime applies — check the provider's terms for your region.
Command A+: where it fits
Cohere's newer, leaner flagship - a 218B open-weight MoE tuned for efficiency (just two GPUs to run) and 48-language support. Released May 19, 2026 by Cohere, it is built for cohere's newer, more efficient flagship - runs on just two H100 GPUs, unusually lean for a 218B-parameter model, a 218B-total/25B-active MoE released with open weights (Apache 2.0) on Hugging Face, supports 48 languages, including all official EU languages, and positioned by Cohere as its 'most efficient and performant model to date'.
Its trade-offs are real: no official context window or per-token price published yet - both are estimated/unavailable, newer release with less independent benchmark verification than established flagships, and enterprise/multilingual focus rather than topping general reasoning leaderboards. 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
This is less "which is smarter" and more "which ecosystem fits." Command A+ (Canada) and Llama 4 Scout (US) differ on pricing philosophy, data-residency, and tooling as much as on raw scores. 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 Command A+ 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.
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, Command A+ leans toward cohere's newer, more efficient flagship - runs on just two h100 gpus, unusually lean for a 218b-parameter model while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Command A+ 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 256K (unconfirmed), about 39× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Command A+ and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you Command A+, 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, Command A+ or Llama 4 Scout?
Command A+ — released May 19, 2026, about 14 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.