Llama 4 Scout
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Llama 4 Scout

llama-4-scout
Llama
Llama 4 Scout is a highly efficient Mixture-of-Experts (MoE) model from Meta, activating 17B out of 109B total parameters per inference. It natively supports multimodal input (text and image) and multilingual output (text and code) across 12 languages. Designed for assistant-style interaction and visual reasoning, Scout features a massive 10-million-token context window. It is instruction-tuned for tasks like multilingual chat and image understanding and is released under the Llama 4 Community License for local or commercial deployment.

Pricing

  • Input Tokens: $0.200 /M tokens
  • Output Tokens: $0.200 /M tokens

Input Modalities

  • Text
  • Vision

Output Modalities

  • Text

Capabilities

  • Tools
  • Tool calling
  • Structured outputs

Providers

Baidu bai-llama-4-scout-17b-16e-instruct
Pricing$0.822$2.466
Context32K
Max output32K
Latency0.3S
Throughput2637.0TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today
Groq groq-llama-4-scout-17b-16e-instruct
Pricing$0.122$0.366
Context131K
Max output8K
Latency-
Throughput-
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today
Deepinfra deepinfra-llama-4-scout-17b-16e-instruct
Pricing$0.088$0.330
Cache$0.000
Context320K
Max output128K
Latency-
Throughput-
Uptime
100.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today
Chutes chutesai/Llama-4-Scout-17B-16E-Instruct
Pricing$0.200$0.200
Cache$0.000
Context131K
Max output131K
Latency0.9S
Throughput147.6TPS
Uptime
0.00% uptime 2 days ago
0.00% uptime yesterday
0.00% uptime today

Performance for llama-4-scout

Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

Uptime
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Latency
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Throughput
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Try this model

Python
import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["AIHUBMIX_API_KEY"],
    base_url="https://aihubmix.com/v1",
)

response = client.chat.completions.create(
    model="llama-4-scout",
    messages=[
      {
        "role": "user",
        "content": "Hello, how are you?"
      }
    ],
    max_tokens=1024,
    stream=False,
)

print(response.choices[0].message.content)

Frequently asked questions

What is Llama 4 Scout?

Llama 4 Scout is a highly efficient Mixture-of-Experts (MoE) model from Meta, activating 17B out of 109B total parameters per inference. It natively supports multimodal input (text and image) and multilingual output (text and code) across 12 languages. Designed for assistant-style interaction and visual reasoning, Scout features a massive 10-million-token context window. It is instruction-tuned for tasks like multilingual chat and image understanding and is released under the Llama 4 Community License for local or commercial deployment.