Llama-4-Scout-17B-16E-Instruct-FP8
meta/llama-4-scoutPor Meta · família: llama · lançado 2025-04-05 · data de conhecimento: 2025-01
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capacidades
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding47
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability5/10
Agents40
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit0/15
- Provider availability5/10
JSON / structured output49
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume19/20
Cost efficiency73
- Headline price (log-scaled)73/95
- Has prompt-cache pricing0/5
Long context45
- Context window (100K → 2M)35/90
- Has published price for full window10/10
Vision82
- Accepts image input50/50
- Context window (10K → 1M)17/30
- Has published price10/10
- Provider availability5/10
Production-readiness79
- Number of independent providers25/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers4/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.55 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $1.10 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $0.31 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $0.94 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $1.14 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Detalhes de preço
Preço recomendado de kilo · meta-llama/llama-4-scout
Provedor mais barato: vercel · Unknown entrada + Unknown saída
Disponível em 5 provedores
| Provedor | ID do modelo do provedor | Entrada / 1M | Saída / 1M | Contexto | Lançado |
|---|---|---|---|---|---|
| Vercel AI Gateway vercel | meta/llama-4-scout | Unknown | Unknown | 128K | 2025-04-05 |
| NanoGPT nano-gpt | meta-llama/llama-4-scout | $0.085 | $0.460 | 328K | 2025-09-05 |
| Kilo Gateway kilo | meta-llama/llama-4-scout | $0.080 | $0.300 | 328K | 2025-04-05 |
| Helicone helicone | llama-4-scout | $0.080 | $0.300 | 131K | 2025-01-01 |
| LLM Gateway llmgateway | llama-4-scout | $0.180 | $0.590 | 33K | 2025-04-05 |
Inconsistências de dados entre provedores
- context_window varies: 128000, 131072, 32768, 327680, 328000
- release_date varies (span 247d): 2025-01-01, 2025-04-05, 2025-09-05
- modalities varies across offerings
Os provedores reportam valores diferentes para este modelo. Os dados rápidos acima usam um provedor representativo; consulte a tabela para detalhes por provedor.
Frequently asked questions
How much does Llama-4-Scout-17B-16E-Instruct-FP8 cost?
Llama-4-Scout-17B-16E-Instruct-FP8 costs $0.080 per 1M input tokens and $0.300 per 1M output tokens, sourced from kilo. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Llama-4-Scout-17B-16E-Instruct-FP8?
Llama-4-Scout-17B-16E-Instruct-FP8 has a context window of 128K tokens, with a max output of 4K tokens per reply. This is the total combined size of prompt + completion.
Does Llama-4-Scout-17B-16E-Instruct-FP8 support tool calling?
Yes. Llama-4-Scout-17B-16E-Instruct-FP8 supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does Llama-4-Scout-17B-16E-Instruct-FP8 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Llama-4-Scout-17B-16E-Instruct-FP8 in our data source. Verify with Meta's official documentation before relying on it in production.
Can Llama-4-Scout-17B-16E-Instruct-FP8 accept image input?
Yes. Llama-4-Scout-17B-16E-Instruct-FP8 accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Meta's docs for the exact rule.
Is Llama-4-Scout-17B-16E-Instruct-FP8 open-weight?
Yes. Llama-4-Scout-17B-16E-Instruct-FP8's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Llama-4-Scout-17B-16E-Instruct-FP8?
If Llama-4-Scout-17B-16E-Instruct-FP8 doesn't fit, consider Meta-Llama-3.1-8B-Instruct, Llama-3.3-70B-Instruct, Llama 4 Maverick 17B 128E Instruct FP8. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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Capability lists this model is in
Última atualização:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.