Llama 4 Scout
ActiveEfficient multimodal model with 17B active parameters.
History
Llama 4 Scout became available via the Meta API on 2025-04-05.
Training & availability
Training data has a knowledge cutoff of 2024-08-31 — information about events after that date is unlikely to appear in the model's responses. Weights are publicly available, making this an open-weight model suitable for on-prem deployment and fine-tuning.
Capabilities
- Input modalities: text, image.
Recommended for: vision, agentic, open-source.
Limitations
- The knowledge cutoff is 19 months old — this model will not know about recent events, releases, or API changes.
Quick start
Minimal example using the OpenRouter API. Copy, paste, replace the key.
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="sk-or-...",
)
resp = client.chat.completions.create(
model="meta/llama-4-scout",
messages=[{"role": "user", "content": "Explain quantum computing in one sentence."}],
)
print(resp.choices[0].message.content)Cost calculator
Estimate your monthly bill. Presets are typical workload sizes.
Popularity
Signals from open-source communities — not a quality measure, but useful for gauging adoption among developers.
Integrations & tooling support
- Tool calling
- Supported
- Structured outputs
- Not supported
Price vs quality
Priced low — good for high-volume tasks. Quality tier pending more benchmark coverage.
- Quality percentile
- —
- Effective price
- $0.282/1M
- Pricing breakdown
- $0.11/1M in
$0.34/1M out
Community ratings
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Comments
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