GPT-6 Astra vs Llama 4 Scout

OpenAI · US | Meta · US · Updated June 2026

Quick verdict

Pick GPT-6 Astra for computer & browser use (screenspot-pro 92.7%) or cybersecurity exploit development (exploitbench 100%). Pick Llama 4 Scout for largest advertised context (10m) or open weights, single-gpu friendly. Choose Llama 4 Scout if you need self-hosting or data privacy; GPT-6 Astra if you want a managed API.

GPT-6 Astra (OpenAI) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. GPT-6 Astra is openAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. Llama 4 Scout is the 10M-token open-weight giant — enormous on paper, but usable recall is far smaller. They diverge most on price, 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 GPT-6 Astra is API-metered at $10/$50 per 1M tokens. Your choice depends on whether you want zero marginal cost at the price of running infrastructure.
▸Context window: Llama 4 Scout holds 9.5× more — 10M (~15,000 pages) vs 1.05M tokens (~1,575 pages). But effective recall usually fades long before the advertised ceiling, so the bigger number only helps if the model reasons over it.
▸Long-context recall: GPT-6 Astra is far stronger at 1M tokens on MRCR v2 (96.3% vs 15%) — important if you actually fill the window with documents.
▸Recency: GPT-6 Astra is the newer model by about 17 months (released September 3, 2026), usually meaning fresher training data and capabilities.

Side-by-side specs

Spec	GPT-6 Astra	Llama 4 Scout
Provider	OpenAI (US)	Meta (US)
Released	September 3, 2026	April 2025
Context window	1.05M tokens (~1,575 pages)	10M (~15,000 pages)
Price (in/out)	$10/$50 per 1M tokens	Open weight (self-host / free)
Open weight?	No — API only	Yes — self-hostable
Modalities	text, image	text, image, code
SWE-Bench Verified	Not published	Not published
MRCR v2 @ 1M	96.3%	15%

Who wins what

Computer & browser use (ScreenSpot-Pro 92.7%)

GPT-6 Astra

Cybersecurity exploit development (ExploitBench 100%)

GPT-6 Astra

GPT-6 Astra lists cybersecurity exploit development (ExploitBench 100%) among its strengths; Llama 4 Scout does not.

Frontier math reasoning (FrontierMath Tier 4: 97.6%)

GPT-6 Astra

Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning

Largest advertised context (10M)

Llama 4 Scout

Its 10M window holds about 9.5× more than GPT-6 Astra's 1.05M tokens in a single prompt.

Open weights, single-GPU friendly

Llama 4 Scout

Open weights make this possible at all — GPT-6 Astra 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.

Lowest cost at scale

Llama 4 Scout

Its weights are open, so at volume you pay for your own hardware instead of GPT-6 Astra's $10/$50 per 1M tokens.

Largest single-prompt input

Llama 4 Scout

Its 10M window is about 9.5× larger than GPT-6 Astra's 1.05M tokens, 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 GPT-6 Astra, 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.

A team with data-privacy or self-hosting needs

→ Llama 4 Scout

Open weights let you run it on your own hardware; GPT-6 Astra is API-only.

Anyone whose priority is computer & browser use (screenspot-pro 92.7%)

→ GPT-6 Astra

It is specifically built for that.

Anyone whose priority is largest advertised context (10m)

→ Llama 4 Scout

That is its strongest area.

GPT-6 Astra: where it fits

OpenAI's flagship reasoning model for computer use, browsing, coding and science, released September 3, 2026 with near-perfect scores on FrontierMath, ExploitBench and long-context recall benchmarks. Released September 3, 2026 by OpenAI, it is built for computer & browser use (ScreenSpot-Pro 92.7%), cybersecurity exploit development (ExploitBench 100%), frontier math reasoning (FrontierMath Tier 4: 97.6%), and long-context recall (MRCR v2 512K-1M: 96.3%).

Its trade-offs are real: no native audio or video input, pricing doubles for prompts over 272K tokens (input/cache 2x, output 1.5x), trails Meta's Muse Spark 1.3 on some coding evals (DeepSWE v1.1: 74.1 vs 75.4), and a separate opt-in "Daybreak" program gives vetted cybersecurity defenders a less-restricted version for legitimate vulnerability research; the public version already refuses ~91.5% of offensive cyber jailbreak attempts by default. At $10 in / $50 out per million tokens, it sits in the premium price band.

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

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. GPT-6 Astra 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 GPT-6 Astra 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.

See pricing

Frequently asked questions

Is GPT-6 Astra 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, GPT-6 Astra leans toward computer & browser use (screenspot-pro 92.7%) while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.

Which is cheaper, GPT-6 Astra or Llama 4 Scout?

Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while GPT-6 Astra is API-metered at $10/$50 per 1M tokens. 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 1.05M tokens, about 9.5× larger. Useful only if the model actually reasons over the full window, which not all do.

Can I use both GPT-6 Astra and Llama 4 Scout together?

Yes — a multi-model platform like LumiChats gives you GPT-6 Astra, 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, GPT-6 Astra or Llama 4 Scout?

GPT-6 Astra — released September 3, 2026, about 17 months after Llama 4 Scout.

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Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.