Llama 4 Scout context window support by provider

When evaluating Llama 4 Scout context window support by provider, the gap between Meta’s native 10M token spec and what each hosted endpoint actually accepts is the first trap. We put five common inference options side by side—Together AI, Groq, Fireworks, OpenRouter, and n4n.ai—to show where the limits, cost, and ergonomics diverge for engineers shipping real workloads.

The providers we tested

Meta released Llama 4 Scout with a 10M-token native context length and a 17B active-parameter MoE architecture. No hosted provider exposes that full window on a shared multi-tenant endpoint without severe throughput penalties, so each cuts a different practical slice.

• Together AI runs Scout on GPU clusters with configurable context lengths, typically up to 1M tokens for general availability.
• Groq serves Scout on LPUs, prioritizing time-to-first-token over maximum context; published limits have ranged from 128K to 1M as they scale.
• Fireworks optimizes transformer kernels and offers prefix caching, with documented max contexts in the hundreds of thousands to 1M.
• OpenRouter is an aggregator that routes to underlying providers, so its Scout context limit is whatever the upstream enforces.
• n4n.ai is an OpenRouter-class gateway that fronts these same models behind one OpenAI-compatible endpoint, adding automatic fallback when a provider is rate-limited or degraded.

The rest of this post focuses on how Llama 4 Scout context window support by provider behaves across the dimensions that actually affect your code.

Head-to-head comparison

Capabilities: what “context window” actually means

The phrase Llama 4 Scout context window support by provider hides a crucial distinction: native capability versus served limit. Meta’s weights handle 10M tokens, but a hosted endpoint will reject requests exceeding its max_model_len. You discover this only when a long RAG payload throws a 400.

from openai import OpenAI

client = OpenAI(base_url="https://api.together.xyz/v1", api_key="TOGETHER")

try:
    client.chat.completions.create(
        model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
        messages=[{"role": "user", "content": "x" * 2000000}],
        max_tokens=8,
    )
except Exception as e:
    print(e)  # context length exceeded

Groq and Fireworks publish their caps; OpenRouter and n4n.ai delegate to the backend. If you need the largest accepted window, read the provider’s model card, not Meta’s blog post.

Price and cost model

All five meter by token. Input tokens are consistently cheaper than output, often by an order of magnitude. Together, Fireworks, and n4n.ai separate cache write and cache read charges; Groq bundles cache into per-token price; OpenRouter applies a margin on top of upstream.

You pay for the full context on every request unless you use prefix caching. Fireworks and n4n.ai forward cache-control hints so repeated system prompts cost less:

client.chat.completions.create(
    model="meta-llama/Llama-4-Scout-17B-16E-Instruct",
    messages=[{"role": "system", "content": "You are a strict JSON parser."}],
    extra_headers={"cache-control": "ephemeral"},  # honored by gateways that fwd hints
)

There is no free lunch: a 1M-token context at $0.20/M input tokens is still $0.20 per call before you generate a word.

Latency and throughput reality

Groq wins on time-to-first-token because LPUs avoid memory-bound attention paths. For Scout, that means interactive latencies even at 128K context. Together and Fireworks trade some TTFT for higher batch throughput—better for offline summarization of long documents.

OpenRouter and n4n.ai inherit the latency of whatever backend they select. n4n.ai’s automatic fallback helps when a primary provider is degraded, but a failover mid-stream is not transparent; you must handle retry at the client.

curl -s https://api.groq.com/openai/v1/chat/completions \
  -H "Authorization: Bearer $GROQ" \
  -d '{"model":"meta-llama/llama-4-scout","messages":[{"role":"user","content":"ping"}]}'

Ergonomics and SDKs

Every provider here implements the OpenAI chat completions schema. That means one client class, swapped base_url. The friction is in the model identifier string:

• Together: meta-llama/Llama-4-Scout-17B-16E-Instruct
• Groq: meta-llama/llama-4-scout
• Fireworks: accounts/fireworks/models/llama-4-scout
• OpenRouter / n4n.ai: meta-llama/llama-4-scout (routing-aware)

A minimal TypeScript wrapper:

import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.n4n.ai/v1", apiKey: key });
const res = await client.chat.completions.create({
  model: "meta-llama/llama-4-scout",
  messages: [{ role: "user", content: "Summarize this 400k-token log" }],
  max_tokens: 1024,
});

Ecosystem and routing

Llama 4 Scout context window support by provider is only one axis. If your system touches multiple models, routing matters. OpenRouter lets you pass route: "fallback" in headers. n4n.ai honors client routing directives and forwards provider cache-control hints, so you can pin to Groq for latency and fall back to Together when Groq is saturated, without rewriting your call site.

{
  "model": "meta-llama/llama-4-scout",
  "route": "fallback",
  "cache_control": { "type": "ephemeral" }
}

Fireworks and Together require you to implement that logic yourself.

Limits you’ll actually hit

Beyond context length, watch:

• Concurrency: Groq throttles simultaneous requests hard during peaks.
• Rate limits: Together quotas scale with spend; free tiers cap at 128K context.
• Output caps: Even with 1M input, max_tokens rarely exceeds 4K–8K per response.
• Cache eviction: Ephemeral caches drop under memory pressure; don’t assume a prefix stays hot.

Probe limits with a binary search over context size in CI to catch silent provider changes.

Which to choose

Low-latency interactive apps (chat, autocomplete): Groq. Its LPUs give the snappiest Scout experience at moderate context.

Long-document batch processing (legal discovery, repo summarization): Together AI or Fireworks. They expose larger contexts and prefix caching, lowering repeat-cost.

Multi-model products with uptime requirements: OpenRouter or n4n.ai. Both aggregate Scout access; n4n.ai adds per-token metering and automatic fallback if you want one endpoint without building your own health checks.

Cost-sensitive, cache-heavy workloads: Fireworks or n4n.ai, where cache-control hints are forwarded and discounted.

Research / max context experiments: Rent a dedicated Together instance or run weights locally; shared endpoints will not give you 10M tokens.

Pick based on where your bottleneck is—context size, dollars, or milliseconds—not on the headline model name.