Llama 4 Scout APIOPEN WEIGHTS10M context

Pricing & specs mirror our live pricing as of July 2026 (pay-as-you-go).

What is Llama 4 Scout?

Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...

Llama 4 Scout on the release timeline

Llama 4 Scout pricing

Pay-as-you-go on DataLLM Lab — these are our live list prices (identical to the pricing page):

On output price, Llama 4 Scout is cheaper than 84% of the 309 models in the catalog. It is served by 4 providers; the best combined rate at our last snapshot was ≈ $0.10 / $0.30 per 1M via DeepInfra, with upstream quantizations bf16, fp8.

What Llama 4 Scout costs per month

Cost = input price × input volume + output price × output volume. The same five workloads run on every model page, so any two compare directly.

Llama 4 Scout vs alternatives

Specs

How to call Llama 4 Scout

Copyfrom openai import OpenAI
client = OpenAI(base_url="https://api.datallmlab.com/v1", api_key="YOUR_DATALLM_LAB_KEY")
resp = client.chat.completions.create(
    model="meta-llama/llama-4-scout",
    messages=[{"role": "user", "content": "Hello"}],
    # supports tools=[...] and tool_choice
)
print(resp.choices[0].message.content)

Copycurl https://api.datallmlab.com/v1/chat/completions \
  -H "Authorization: Bearer YOUR_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"meta-llama/llama-4-scout","messages":[{"role":"user","content":"Hello"}]}'

Copyimport OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.datallmlab.com/v1", apiKey: process.env.DATALLM_KEY });
const r = await client.chat.completions.create({
  model: "meta-llama/llama-4-scout",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(r.choices[0].message.content);

The same key routes Llama 4 Scout and 300+ other models — switch models by changing one string. How OpenAI-compatible APIs work.

Prompting tips for Llama 4 Scout

• Be explicit and show the shape of the answer. Spell out format, tone and constraints up front, and include one short example of the output you want — Llama 4 Scout follows concrete instructions better than abstract ones.
• Huge 10M context — but anchor the ask. You can paste whole documents or codebases; models attend most to the start and end, so put the key instruction at the top and restate it after long inputs.
• It reads images. Send image parts alongside your text and ask for specific outputs (named JSON fields, a table, "what changed") rather than "describe this".
• Give it real tools. Pass a tools=[...] schema instead of asking it to "pretend" — let Llama 4 Scout emit tool calls, execute them, and feed results back for the next turn.
• Constrain JSON with a schema. Use response_format / structured outputs rather than "reply in JSON" to get valid, parseable objects every time.

Tips are derived from Llama 4 Scout's actual capabilities — context window, tool & JSON support, modality and price tier — not generic advice.

Open-source tools for Llama 4 Scout

Popular open-source projects for running and building with Llama 4 Scout — star counts pulled from GitHub (July 2026).

Listed by GitHub stars; inclusion is by ecosystem relevance (inference engines, agent frameworks and SDKs), not affiliation. Stars change — see each repo for current numbers.

Migrating to Llama 4 Scout

Coming from OpenAI or another gateway? On an OpenAI-compatible setup the only changes are base_url → https://api.datallmlab.com/v1 and model → meta-llama/llama-4-scout. Messages, streaming, tool calls and the rest of your code stay the same. Routing & failover guide.

Self-host or use the API?

Llama 4 Scout ships open weights (meta-llama/Llama-4-Scout-17B-16E-Instruct, ~747.3K downloads and 1.3K likes in the last 30 days), with community quantizations (bf16, fp8) for smaller GPUs, so you can run it yourself. Most teams still use the API: no GPU to provision or keep warm, no inference ops, and instant access across 4 providers with automatic failover. Self-host when data residency or fixed per-token economics matter most.

Rate limits & reliability

Rate limits here are the DataLLM Lab gateway's, not the upstream vendor's. On 429 (rate limited) or 503 (provider busy), retry with exponential backoff; because Llama 4 Scout is served by 4 providers, requests can fail over to a healthy one automatically. See the error-code guide and failover setup.

Related reading

Frequently asked questions