Pick GPT-5.3-Codex for dedicated coding agent or cli and ide integration. 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-5.3-Codex if you want a managed API.
GPT-5.3-Codex (OpenAI) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. GPT-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. 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
Cost model: Llama 4 Scout ships open weights you can self-host (hardware cost only, no per-token fee), while GPT-5.3-Codex is API-metered at $1.5/$10 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 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: GPT-5.3-Codex is the newer model by about 11 months (released 2026), usually meaning fresher training data and capabilities.
Specifications
Spec
GPT-5.3-Codex
Llama 4 Scout
Provider
OpenAI (US)
Meta (US)
Released
2026
April 2025
Context window
128K (~192 pages)
10M (~15,000 pages)
Price (in/out)
$1.5/$10 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
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
Dedicated coding agent: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
CLI and IDE integration: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
Autonomous software tasks: GPT-5.3-Codex — A core design strength of GPT-5.3-Codex.
Largest advertised context (10M): Llama 4 Scout — A core design strength of Llama 4 Scout.
Open weights, single-GPU friendly: Llama 4 Scout — A core design strength of Llama 4 Scout.
Self-hosted, data-private deployment: Llama 4 Scout — A core design strength of Llama 4 Scout.
Lowest cost at scale: Llama 4 Scout — At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input: Llama 4 Scout — Its 10M window is about 78× larger, 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-5.3-Codex, 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-5.3-Codex is API-only.
Anyone whose priority is dedicated coding agent: GPT-5.3-Codex — It is specifically built for that.
Anyone whose priority is largest advertised context (10m): Llama 4 Scout — That is its strongest area.
GPT-5.3-Codex: where it fits
OpenAI's coding-specialized agent model for autonomous software engineering. Released 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.
Its trade-offs are real: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. At $1.5 in / $10 out per million tokens, it sits in the mid 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-5.3-Codex 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 GPT-5.3-Codex 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-5.3-Codex leans toward dedicated coding agent 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-5.3-Codex 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-5.3-Codex is API-metered at $1.5/$10 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 128K, about 78× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-5.3-Codex and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you GPT-5.3-Codex, 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-5.3-Codex or Llama 4 Scout?
GPT-5.3-Codex — released 2026, about 11 months after Llama 4 Scout.
GPT-5.3-Codex vs Llama 4 Scout
OpenAI · US | Meta · US · Updated June 2026
Quick verdict
Pick GPT-5.3-Codex for dedicated coding agent or cli and ide integration. 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-5.3-Codex if you want a managed API.
GPT-5.3-Codex (OpenAI) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. GPT-5.3-Codex is openAI's coding-specialized agent model for autonomous software engineering. 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-5.3-Codex is API-metered at $1.5/$10 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 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: GPT-5.3-Codex is the newer model by about 11 months (released 2026), usually meaning fresher training data and capabilities.
Side-by-side specs
Spec
GPT-5.3-Codex
Llama 4 Scout
Provider
OpenAI (US)
Meta (US)
Released
2026
April 2025
Context window
128K (~192 pages)
10M (~15,000 pages)
Price (in/out)
$1.5/$10 per 1M tokens
Open weight (self-host / free)
Open weight?
No — API only
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
Dedicated coding agent
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
CLI and IDE integration
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
Autonomous software tasks
GPT-5.3-Codex
A core design strength of GPT-5.3-Codex.
Largest advertised context (10M)
Llama 4 Scout
A core design strength of Llama 4 Scout.
Open weights, single-GPU friendly
Llama 4 Scout
A core design strength of Llama 4 Scout.
Self-hosted, data-private deployment
Llama 4 Scout
A core design strength of Llama 4 Scout.
Lowest cost at scale
Llama 4 Scout
At Open weight (self-host / free), it is the cheaper of the two — the gap dominates the bill on high-volume workloads.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 78× larger, 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-5.3-Codex, 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-5.3-Codex is API-only.
Anyone whose priority is dedicated coding agent
→ GPT-5.3-Codex
It is specifically built for that.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
That is its strongest area.
GPT-5.3-Codex: where it fits
OpenAI's coding-specialized agent model for autonomous software engineering. Released 2026 by OpenAI, it is built for dedicated coding agent, cLI and IDE integration, autonomous software tasks, and tool calling.
Its trade-offs are real: coding-specialized, narrower general use, and retired in favor of GPT-5.5 Codex. At $1.5 in / $10 out per million tokens, it sits in the mid 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-5.3-Codex 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-5.3-Codex 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.
Is GPT-5.3-Codex 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-5.3-Codex leans toward dedicated coding agent 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-5.3-Codex 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-5.3-Codex is API-metered at $1.5/$10 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 128K, about 78× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both GPT-5.3-Codex and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you GPT-5.3-Codex, 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-5.3-Codex or Llama 4 Scout?
GPT-5.3-Codex — released 2026, about 11 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.