Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Palmyra X6 for enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents or writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (writer's own figures). Choose Llama 4 Scout if you need self-hosting or data privacy; Palmyra X6 if you want a managed API.
Llama 4 Scout (Meta) and Palmyra X6 (Writer) 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. Palmyra X6 is writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences
Cost model: Llama 4 Scout ships open weights you can self-host (hardware cost only, no per-token fee), while Palmyra X6 is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
Context window: Llama 4 Scout holds 78× more — 10M (~15,000 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
Recency: Palmyra X6 is the newer model by about 17 months (released August 13, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Llama 4 Scout
Palmyra X6
Provider
Meta (US)
Writer (US)
Released
April 2025
August 13, 2026
Context window
10M (~15,000 pages)
128K (~192 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
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 78× more than Palmyra X6's 128K in a single prompt.
Open weights, single-GPU friendly: Llama 4 Scout — Open weights make this possible at all — Palmyra X6 is API-only, so it cannot leave the vendor's servers.
Self-hosted, data-private deployment: 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.
Enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents: Palmyra X6 — Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (Writer's own figures): Palmyra X6 — Writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half — and it is the newer of the two.
A post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use: Palmyra X6 — Palmyra X6 lists a post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use among its strengths; Llama 4 Scout does not.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 78× larger than Palmyra X6's 128K, 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.
A team with data-privacy or self-hosting needs: Llama 4 Scout — Open weights let you run it on your own hardware; Palmyra X6 is API-only.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — It is specifically built for that.
Anyone whose priority is enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents: Palmyra X6 — 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.
Palmyra X6: where it fits
Writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half. Released August 13, 2026 by Writer, it is built for enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents, writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (Writer's own figures), and a post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use.
Its trade-offs: no public per-token API price - sold through Writer's enterprise platform, not a self-serve API, not independently benchmarked on general leaderboards like SWE-bench or Artificial Analysis, and built for a narrower enterprise-agent use case rather than general-purpose chat.
The bottom line for this matchup
The defining split here is open vs. closed. Llama 4 Scout gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Palmyra X6 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Frequently asked questions
Is Llama 4 Scout or Palmyra X6 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 Palmyra X6 leans toward enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Palmyra X6?
Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Palmyra X6 is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Llama 4 Scout — 10M vs 128K, about 78× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and Palmyra X6 together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Palmyra X6 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 Palmyra X6?
Palmyra X6 — released August 13, 2026, about 17 months after Llama 4 Scout.
Llama 4 Scout vs Palmyra X6
Meta · US | Writer · US · Updated June 2026
Quick verdict
Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Pick Palmyra X6 for enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents or writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (writer's own figures). Choose Llama 4 Scout if you need self-hosting or data privacy; Palmyra X6 if you want a managed API.
Llama 4 Scout (Meta) and Palmyra X6 (Writer) 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. Palmyra X6 is writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half. They diverge most on context window and open vs. closed weights — each quantified below from the models' real specs.
Key differences at a glance
▸Cost model: Llama 4 Scout ships open weights you can self-host (hardware cost only, no per-token fee), while Palmyra X6 is API-metered at Not published. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Llama 4 Scout holds 78× more — 10M (~15,000 pages) vs 128K (~192 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Recency: Palmyra X6 is the newer model by about 17 months (released August 13, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Llama 4 Scout
Palmyra X6
Provider
Meta (US)
Writer (US)
Released
April 2025
August 13, 2026
Context window
10M (~15,000 pages)
128K (~192 pages)
Price (in/out)
Open weight (self-host / free)
Not published
Open weight?
Yes — self-hostable
No — API only
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 78× more than Palmyra X6's 128K in a single prompt.
Open weights, single-GPU friendly
Llama 4 Scout
Open weights make this possible at all — Palmyra X6 is API-only, so it cannot leave the vendor's servers.
Self-hosted, data-private deployment
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.
Enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents
Palmyra X6
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (Writer's own figures)
Palmyra X6
Writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half — and it is the newer of the two.
A post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use
Palmyra X6
Palmyra X6 lists a post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use among its strengths; Llama 4 Scout does not.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 78× larger than Palmyra X6's 128K, 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.
A team with data-privacy or self-hosting needs
→ Llama 4 Scout
Open weights let you run it on your own hardware; Palmyra X6 is API-only.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
It is specifically built for that.
Anyone whose priority is enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents
→ Palmyra X6
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.
Palmyra X6: where it fits
Writer's enterprise agentic flagship - a GLM-5.2-based model with harness upgrades Writer says cut agent token costs by roughly half. Released August 13, 2026 by Writer, it is built for enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents, writer says its harness upgrades cut agent-workflow token costs by roughly 52% and improve speed by roughly 48% (Writer's own figures), and a post-trained variant built on Z.ai's open-weight GLM-5.2, tuned specifically for business agent use.
Its trade-offs: no public per-token API price - sold through Writer's enterprise platform, not a self-serve API, not independently benchmarked on general leaderboards like SWE-bench or Artificial Analysis, and built for a narrower enterprise-agent use case rather than general-purpose chat.
The bottom line for this matchup
The defining split here is open vs. closed. Llama 4 Scout gives you weights you control — self-host it, fine-tune it, keep data in-house, pay only for hardware. Palmyra X6 gives you a managed, always-updated API with no infrastructure to run. Teams with GPUs, privacy requirements, or huge volume often favour the open model; teams that want zero ops and the latest capabilities favour the closed one. Capability is close enough that this operational question, not the benchmark, usually decides it.
Want both Llama 4 Scout and Palmyra X6 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, Llama 4 Scout leans toward largest advertised context (10m) while Palmyra X6 leans toward enterprise agentic workflows - built specifically for marketing/revenue teams running multi-step agents, and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Llama 4 Scout or Palmyra X6?
Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Palmyra X6 is API-metered at Not published. For most teams without GPUs, the API model is cheaper to start; at very high volume, self-hosting can win.
Which has the bigger context window?
Llama 4 Scout — 10M vs 128K, about 78× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Llama 4 Scout and Palmyra X6 together?
Yes — a multi-model platform like LumiChats gives you Llama 4 Scout, Palmyra X6 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 Palmyra X6?
Palmyra X6 — released August 13, 2026, about 17 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.