Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly.
Inkling (Thinking Machines Lab) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. 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 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: Inkling is the newer model by about 16 months (released July 15, 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
Inkling
Llama 4 Scout
Provider
Thinking Machines Lab (US)
Meta (US)
Released
July 15, 2026
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, audio, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI: Inkling — Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and it is the newer of the two.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model: Inkling — Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request: Inkling — Inkling lists a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request among its strengths; Llama 4 Scout does not.
Largest advertised context (10M): Llama 4 Scout — Its 10M window holds about 10× more than Inkling'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 — Inkling is comparatively weak here — no official hosted API price - available via third-party hosts only at launch
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 10× larger than Inkling's 1M, 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 the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai: Inkling — It is specifically built for that.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — That is its strongest area.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs are real: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. 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
Inkling and Llama 4 Scout overlap enough that the right pick depends on your specific job. Llama 4 Scout holds the larger context; and each leads in its own area — Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai, Llama 4 Scout for largest advertised context (10m). Rather than crowning one, run the same hard task through both once and let the results decide.
Frequently asked questions
Is Inkling 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, Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Inkling 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.
Can I use both Inkling and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you Inkling, 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, Inkling or Llama 4 Scout?
Inkling — released July 15, 2026, about 16 months after Llama 4 Scout.
Inkling vs Llama 4 Scout
Thinking Machines Lab · US | Meta · US · Updated June 2026
Quick verdict
Pick Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai or a 975b-parameter moe (41b active) with native text, image, and audio reasoning in one model. Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly.
Inkling (Thinking Machines Lab) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. Inkling is mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. 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 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: Inkling is the newer model by about 16 months (released July 15, 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
Inkling
Llama 4 Scout
Provider
Thinking Machines Lab (US)
Meta (US)
Released
July 15, 2026
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, audio, code
text, image, code
SWE-Bench Verified
Not published
Not published
MRCR v2 @ 1M
Not published
15%
Who wins what
The first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI
Inkling
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control — and it is the newer of the two.
A 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model
Inkling
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
A dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request
Inkling
Inkling lists a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request among its strengths; Llama 4 Scout does not.
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 10× more than Inkling'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
Inkling is comparatively weak here — no official hosted API price - available via third-party hosts only at launch
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 10× larger than Inkling's 1M, 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 the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai
→ Inkling
It is specifically built for that.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
That is its strongest area.
Inkling: where it fits
Mira Murati's Thinking Machines Lab debuts with Inkling - a 975B open-weight MoE with native multimodal reasoning and a dialable thinking-effort control. Released July 15, 2026 by Thinking Machines Lab, it is built for the first model release from Mira Murati's Thinking Machines Lab (ex-OpenAI CTO) - one of the most closely-watched new labs in AI, a 975B-parameter MoE (41B active) with native text, image, and audio reasoning in one model, a dialable 'thinking effort' knob (0.2-0.99) that lets you trade speed for depth of reasoning per-request, and fully open weights (Apache 2.0) at frontier scale - unusual for a model this large and this new.
Its trade-offs are real: a brand-new lab's first release - no multi-generation track record yet, no official hosted API price - available via third-party hosts only at launch, and independent third-party benchmark verification is still limited given how recently it shipped. 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
Inkling and Llama 4 Scout overlap enough that the right pick depends on your specific job. Llama 4 Scout holds the larger context; and each leads in its own area — Inkling for the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai, Llama 4 Scout for largest advertised context (10m). Rather than crowning one, run the same hard task through both once and let the results decide.
Want both Inkling 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, Inkling leans toward the first model release from mira murati's thinking machines lab (ex-openai cto) - one of the most closely-watched new labs in ai while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Inkling 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.
Can I use both Inkling and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you Inkling, 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, Inkling or Llama 4 Scout?
Inkling — released July 15, 2026, about 16 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.