Llama 4 Scout 17B 16E Instruct
meta/llama-4-scout-17b-16e-instruct제공: Meta · 패밀리: llama · 출시 2025-04-05 · 지식 컷오프: 2024-08
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
기능
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.
Coding52
- Tool calling40/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)2/20
- Provider availability10/10
Agents50
- Tool calling35/35
- Structured output0/25
- Reasoning0/15
- Output token limit5/15
- Provider availability10/10
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
Vision87
- Accepts image input50/50
- Context window (10K → 1M)17/30
- Has published price10/10
- Provider availability10/10
Production-readiness94
- Number of independent providers40/40
- 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. |
가격 상세
추천 가격 제공자: deepinfra · meta-llama/Llama-4-Scout-17B-16E-Instruct
가장 저렴한 제공자: llama · Unknown 입력 + Unknown 출력
12곳 제공사에서 이용 가능
| 제공자 | 제공자 모델 ID | 입력 / 1M | 출력 / 1M | 컨텍스트 | 출시일 |
|---|---|---|---|---|---|
| Azure azure | llama-4-scout-17b-16e-instruct | $0.200 | $0.780 | 128K | 2025-04-05 |
| Groq groq | meta-llama/llama-4-scout-17b-16e-instruct | $0.110 | $0.340 | 131K | 2025-04-05 |
| Deep Infra deepinfra | meta-llama/Llama-4-Scout-17B-16E-Instruct | $0.080 | $0.300 | 10M | 2025-04-05 |
| Llama llama | cerebras-llama-4-scout-17b-16e-instruct | Unknown | Unknown | 128K | 2025-04-05 |
| Llama llama | llama-4-scout-17b-16e-instruct-fp8 | Unknown | Unknown | 128K | 2025-04-05 |
| NovitaAI novita-ai | meta-llama/llama-4-scout-17b-16e-instruct | $0.180 | $0.590 | 131K | 2025-04-06 |
| Weights & Biases wandb | meta-llama/Llama-4-Scout-17B-16E-Instruct | $0.170 | $0.660 | 64K | 2025-01-31 |
| Cloudflare AI Gateway cloudflare-ai-gateway | workers-ai/@cf/meta/llama-4-scout-17b-16e-instruct | $0.270 | $0.850 | 128K | 2025-04-16 |
| Azure Cognitive Services azure-cognitive-services | llama-4-scout-17b-16e-instruct | $0.200 | $0.780 | 128K | 2025-04-05 |
| Synthetic synthetic | hf:meta-llama/Llama-4-Scout-17B-16E-Instruct | $0.150 | $0.600 | 328K | 2025-04-05 |
| Cloudflare Workers AI cloudflare-workers-ai | @cf/meta/llama-4-scout-17b-16e-instruct | $0.270 | $0.850 | 128K | 2025-04-16 |
| GitHub Models github-models | meta/llama-4-scout-17b-16e-instruct | Unknown | Unknown | 128K | 2025-01-31 |
제공자 간 데이터 불일치
- context_window varies: 10000000, 128000, 131072, 328000, 64000
- release_date varies (span 75d): 2025-01-31, 2025-04-05, 2025-04-06, 2025-04-16
- modalities varies across offerings
제공자별로 이 모델의 값이 다릅니다. 위의 핵심 정보는 대표 제공자 기준이며, 제공자별 상세는 표를 참고하세요.
Frequently asked questions
How much does Llama 4 Scout 17B 16E Instruct cost?
Llama 4 Scout 17B 16E Instruct costs $0.080 per 1M input tokens and $0.300 per 1M output tokens, sourced from deepinfra. 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?
Llama 4 Scout 17B 16E Instruct has a context window of 128K tokens, with a max output of 8K tokens per reply. This is the total combined size of prompt + completion.
Does Llama 4 Scout 17B 16E Instruct support tool calling?
Yes. Llama 4 Scout 17B 16E Instruct 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 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Llama 4 Scout 17B 16E Instruct in our data source. Verify with Meta's official documentation before relying on it in production.
Can Llama 4 Scout 17B 16E Instruct accept image input?
Yes. Llama 4 Scout 17B 16E Instruct 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 open-weight?
Yes. Llama 4 Scout 17B 16E Instruct'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?
If Llama 4 Scout 17B 16E Instruct 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.
Explore more
More Meta models
- Meta-Llama-3.1-8B-Instruct$0.02 in / $0.03 out
- Llama-3.3-70B-Instruct$0.05 in / $0.23 out
- Llama 4 Maverick 17B 128E Instruct FP8$0.14 in / $0.59 out
- Meta-Llama-3.1-70B-Instruct$0.40 in / $0.40 out
- Llama 3.2 3b Instruct$0.01 in / $0.01 out
Capability lists this model is in
마지막 업데이트:
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.