Llama 4 Maverick 17B Instruct FP8 vs Llama 4 Scout 17B-16E Instruct
Llama 4 Maverick 17B Instruct FP8 (2025) and Llama 4 Scout 17B-16E Instruct (2025) are general-purpose language models from AI at Meta. Llama 4 Maverick 17B Instruct FP8 ships a 1M-token context window, while Llama 4 Scout 17B-16E Instruct ships a 328K-token context window. On τ-bench, Llama 4 Maverick 17B Instruct FP8 leads by 6.2 pts. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Llama 4 Scout 17B-16E Instruct is ~87% cheaper at $0.08/1M; pay for Llama 4 Maverick 17B Instruct FP8 only for long-context analysis.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-04-05 | 2025-04-05 |
| Context window | 1M | 328K |
| Parameters | 17B | 17B |
| Architecture | mixture of experts | mixture of experts |
| License | Open Source | Open Source |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Llama 4 Maverick 17B Instruct FP8 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Input price | $0.15/1M tokens | $0.08/1M tokens |
| Output price | $0.6/1M tokens | $0.3/1M tokens |
| Providers |
Capabilities
| Capability | Llama 4 Maverick 17B Instruct FP8 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | No | No |
| Tool use | No | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Llama 4 Maverick 17B Instruct FP8 | Llama 4 Scout 17B-16E Instruct |
|---|---|---|
| τ-bench | 68.5 | 62.3 |
Deep dive
On shared benchmark coverage, τ-bench has Llama 4 Maverick 17B Instruct FP8 at 68.5 and Llama 4 Scout 17B-16E Instruct at 62.3, with Llama 4 Maverick 17B Instruct FP8 ahead by 6.2 points. The largest visible gap is 6.2 points on τ-bench, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint is close: both models cover structured outputs. That makes context budget, benchmark fit, and provider maturity more important than a simple checklist. If your application depends on one integration detail, verify it against the provider route you plan to use, not just the base model listing.
For cost, Llama 4 Maverick 17B Instruct FP8 lists $0.15/1M input and $0.6/1M output tokens, while Llama 4 Scout 17B-16E Instruct lists $0.08/1M input and $0.3/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Llama 4 Scout 17B-16E Instruct lower by about $0.14 per million blended tokens. Availability is 7 providers versus 8, so concentration risk also matters.
Choose Llama 4 Maverick 17B Instruct FP8 when long-context analysis and larger context windows are central to the workload. Choose Llama 4 Scout 17B-16E Instruct when provider fit, lower input-token cost, and broader provider choice are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, Llama 4 Maverick 17B Instruct FP8 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Maverick 17B Instruct FP8 supports 1M tokens, while Llama 4 Scout 17B-16E Instruct supports 328K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, Llama 4 Maverick 17B Instruct FP8 or Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct is cheaper on tracked token pricing. Llama 4 Maverick 17B Instruct FP8 costs $0.15/1M input and $0.6/1M output tokens. Llama 4 Scout 17B-16E Instruct costs $0.08/1M input and $0.3/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Llama 4 Maverick 17B Instruct FP8 or Llama 4 Scout 17B-16E Instruct open source?
Llama 4 Maverick 17B Instruct FP8 is listed under Open Source. Llama 4 Scout 17B-16E Instruct is listed under Open Source. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for structured outputs, Llama 4 Maverick 17B Instruct FP8 or Llama 4 Scout 17B-16E Instruct?
Both Llama 4 Maverick 17B Instruct FP8 and Llama 4 Scout 17B-16E Instruct expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface.
Where can I run Llama 4 Maverick 17B Instruct FP8 and Llama 4 Scout 17B-16E Instruct?
Llama 4 Maverick 17B Instruct FP8 is available on Microsoft Foundry, Together AI, OpenRouter, Fireworks AI, and DeepInfra. Llama 4 Scout 17B-16E Instruct is available on OpenRouter, Together AI, Fireworks AI, DeepInfra, and GCP Vertex AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
When should I pick Llama 4 Maverick 17B Instruct FP8 over Llama 4 Scout 17B-16E Instruct?
Llama 4 Scout 17B-16E Instruct is ~87% cheaper at $0.08/1M; pay for Llama 4 Maverick 17B Instruct FP8 only for long-context analysis. If your workload also depends on long-context analysis, start with Llama 4 Maverick 17B Instruct FP8; if it depends on provider fit, run the same evaluation with Llama 4 Scout 17B-16E Instruct.
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Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.