Amazon Nova Premier vs Llama 4 Scout
Amazon · US | Meta · US · Updated June 2026
Quick verdict
Pick Amazon Nova Premier for 1m-token context with deep aws bedrock integration or amazon's most capable nova model, positioned as a 'teacher' for distilling smaller models. 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; Amazon Nova Premier if you want a managed API.
Amazon Nova Premier (Amazon) and Llama 4 Scout (Meta) are two of the models people most often weigh against each other in 2026. Amazon Nova Premier is amazon's flagship 1M-context Nova model on AWS Bedrock - a useful ecosystem anchor and distillation teacher, but weak and pricey on independent intelligence. 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 Amazon Nova Premier is API-metered at $2.5/$12.5 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 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: Amazon Nova Premier is the newer model by about 25 days (released April 30, 2025), usually meaning fresher training data and capabilities.
Side-by-side specs
| Spec | Amazon Nova Premier | Llama 4 Scout |
|---|---|---|
| Provider | Amazon (US) | Meta (US) |
| Released | April 30, 2025 | April 2025 |
| Context window | 1M (~1,500 pages) | 10M (~15,000 pages) |
| Price (in/out) | $2.5/$12.5 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 | Not published | 15% |
Who wins what
1M-token context with deep AWS Bedrock integration
Amazon Nova Premier
Llama 4 Scout is comparatively weak here — ~15% on long-context multi-needle reasoning
Amazon's most capable Nova model, positioned as a 'teacher' for distilling smaller models
Amazon Nova Premier
Amazon's flagship 1M-context Nova model on AWS Bedrock - a useful ecosystem anchor and distillation teacher, but weak and pricey on independent intelligence — and it is the newer of the two.
A natural fit for teams already building on AWS
Amazon Nova Premier
Amazon Nova Premier lists a natural fit for teams already building on AWS among its strengths; Llama 4 Scout does not.
Largest advertised context (10M)
Llama 4 Scout
Its 10M window holds about 10× more than Amazon Nova Premier's 1M in a single prompt.
Open weights, single-GPU friendly
Llama 4 Scout
Open weights make this possible at all — Amazon Nova Premier 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 Amazon Nova Premier's $2.5/$12.5 per 1M tokens.
Largest single-prompt input
Llama 4 Scout
Its 10M window is about 10× larger than Amazon Nova Premier's 1M, 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 Amazon Nova Premier, 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; Amazon Nova Premier is API-only.
Anyone whose priority is 1m-token context with deep aws bedrock integration
→ Amazon Nova Premier
It is specifically built for that.
Anyone whose priority is largest advertised context (10m)
→ Llama 4 Scout
That is its strongest area.
Amazon Nova Premier: where it fits
Amazon's flagship 1M-context Nova model on AWS Bedrock - a useful ecosystem anchor and distillation teacher, but weak and pricey on independent intelligence. Released April 30, 2025 by Amazon, it is built for 1M-token context with deep AWS Bedrock integration, amazon's most capable Nova model, positioned as a 'teacher' for distilling smaller models, a natural fit for teams already building on AWS, and multimodal input for complex reasoning across text and images.
Its trade-offs are real: weak on independent intelligence - Artificial Analysis Intelligence Index of 13, below average for its tier, expensive for its score at $2.50/$12.50 per million tokens, a 2025 model - older than the 2026 frontier it competes against, and sources disagree on modalities (Amazon cites image input; some evaluations list text-only). At $2.5 in / $12.5 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. Amazon Nova Premier 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 Amazon Nova Premier 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 pricingFrequently asked questions
Is Amazon Nova Premier 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, Amazon Nova Premier leans toward 1m-token context with deep aws bedrock integration while Llama 4 Scout leans toward largest advertised context (10m), and that positioning usually predicts which feels better on your codebase.
Which is cheaper, Amazon Nova Premier or Llama 4 Scout?
Llama 4 Scout is open-weight, so self-hosting means no per-token fee (you pay for hardware instead), while Amazon Nova Premier is API-metered at $2.5/$12.5 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 1M, about 10× larger. Useful only if the model actually reasons over the full window, which not all do.
Can I use both Amazon Nova Premier and Llama 4 Scout together?
Yes — a multi-model platform like LumiChats gives you Amazon Nova Premier, 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, Amazon Nova Premier or Llama 4 Scout?
Amazon Nova Premier — released April 30, 2025, about 25 days after Llama 4 Scout.
Related comparisons
Specifications and benchmarks reflect publicly reported figures as of June 2026 and may change as providers release updates. Always verify on your own workload.