Finding the cheapest Llama 4 Scout API price comparison means looking past the headline per-million-token number. Your real bill depends on context handling, batching, and what happens when a provider throttles you. This article puts five hosting options side by side so you can pick based on workload, not marketing.
The contenders
We compare four direct inference providers and one gateway that fronts them:
- Together AI – GPU cloud with open-weight focus.
- Groq – LPU-accelerated inference, known for low latency.
- Fireworks AI – GPU serving with per-model optimization.
- OpenRouter – Aggregator routing to multiple upstreams.
- n4n.ai – An OpenAI-compatible gateway that addresses 240+ models, including Llama 4 Scout, with automatic fallback when a provider is degraded and per-token usage metering.
All five expose an OpenAI-compatible /v1/chat/completions surface, so swapping between them is a base_url change.
Capabilities
Llama 4 Scout ships with a 10M-token context window in the weights, but no provider passes that through untouched. Groq currently caps at 128K, Together and Fireworks offer 1M on their hosted Scout, OpenRouter defers to the upstream (usually 128K–1M), and n4n.ai honors the upstream limit while letting you pin a route.
Function calling is supported across all, but schema strictness varies. Together and Fireworks enforce JSON mode reliably; Groq’s beta tool-calling on Scout can drop parallel calls under load. OpenRouter and n4n.ai forward whatever the selected upstream supports.
Price/cost model
The cheapest Llama 4 Scout API price comparison must separate raw token price from hidden costs. Direct providers charge per input/output token, sometimes with separate caching discounts. Groq and Fireworks price aggressively to win throughput; Together sits slightly higher but offers longer context without surcharge. OpenRouter adds a small margin (typically 5–10%) over the underlying provider but gives you one bill. n4n.ai meters per token and applies the upstream rate, so cost equals the routed provider plus any gateway margin disclosed in its dashboard.
None of these run free tiers for Scout at production scale; all require a funded account. If you self-host on your own GPUs, the API cost is zero but you eat ops overhead—out of scope here.
Latency/throughput
Groq’s LPUs give the lowest time-to-first-token (TTFT) for sub-32K prompts—often a fraction of GPU-based serving. Together and Fireworks run H100 clusters; TTFT lands in the hundreds of milliseconds and degrades with concurrent batches. OpenRouter inherits the latency of its chosen route; if it picks a congested upstream, you wait. n4n.ai adds single-digit milliseconds of proxy overhead but mitigates tail latency via fallback to a healthy provider.
Throughput (tokens/sec) on long generations favors GPU clouds for big batches; Groq stays flat but caps max concurrency per key unless you negotiate.
Ergonomics
All five speak OpenAI’s schema. Minimal Python:
from openai import OpenAI
def scout_client(base_url, key):
return OpenAI(base_url=base_url, api_key=key)
# Together
c_together = scout_client("https://api.together.xyz/v1", "together-key")
# Groq
c_groq = scout_client("https://api.groq.com/openai/v1", "groq-key")
# Fireworks
c_fw = scout_client("https://api.fireworks.ai/inference/v1", "fw-key")
# OpenRouter
c_or = scout_client("https://openrouter.ai/api/v1", "or-key")
# n4n.ai
c_n4n = scout_client("https://api.n4n.ai/v1", "n4n-key")Streaming works identically. Where they diverge: Groq rejects some sampling params that Scout’s reference impl allows; Fireworks supports response_format with stricter validation; OpenRouter requires a model string like meta-llama/llama-4-scout while direct providers use llama-4-scout. n4n.ai forwards provider cache-control hints, so you can tag prompts for reuse across fallbacks.
Ecosystem
Together bundles a finetuning pipeline for Scout. Fireworks ships a playground and eval hooks. Groq’s ecosystem is lean—fast inference, minimal extras. OpenRouter’s value is model breadth: one key hits Scout, Claude, and GPT. n4n.ai’s angle is routing control: send x-n4n-route: groq to force a provider, or omit it for automatic.
Limits
- Rate limits: Groq enforces low default RPM (requests/min) on Scout; raise by ticket. Together starts at 60 RPM, scales with spend. Fireworks similar. OpenRouter tier-based. n4n.ai inherits upstream limits but retries on 429.
- Context caps: as above.
- Max output: 4K–8K per call on most; Scout can do more but providers truncate.
- Logging: Groq keeps zero prompt logs by default; Together optional; OpenRouter shows usage in dashboard; n4n.ai meters per token and exposes line-item usage.
Head-to-head table
| Provider | Cost model | TTFT (small prompt) | Context cap | Key ergonomics | Hard limits |
|---|---|---|---|---|---|
| Together | Per-token, ~mid-tier | 200–500 ms | 1M | Finetuning, strict JSON | 60 RPM default |
| Groq | Per-token, aggressive | <100 ms | 128K | Lean, LPU speed | Low default RPM |
| Fireworks | Per-token, competitive | 200–400 ms | 1M | Eval hooks, validation | Concurrency caps |
| OpenRouter | Upstream + margin | Upstream-dependent | 128K–1M | 240+ models, one bill | Tier-based |
| n4n.ai | Upstream + metering | +few ms proxy | Upstream | Fallback, route pinning | Inherits upstream |
Calling patterns that change cost
A 10K-token RAG prompt repeated hourly benefits from prompt caching. Fireworks and Together honor cache_control on system blocks; n4n.ai forwards those hints so the cached segment survives a fallback. Groq’s cache is automatic on exact prefix match. OpenRouter passes through if upstream supports it.
{
"model": "llama-4-scout",
"messages": [
{"role": "system", "content": "You are a docs bot.", "cache_control": {"type": "ephemeral"}}
]
}If you skip caching, the cheapest Llama 4 Scout API price comparison flips: Groq’s low token rate wins for short calls, but Together’s 1M context without surcharge wins for long-system-prompt apps.
Which to choose
Latency-critical, short context (chat bots, agents with small state) Groq. You trade the 128K cap for sub-100ms TTFT. Build a fallback to Together when Groq 429s.
Long-context RAG or code analysis up to 1M Together or Fireworks. Same price band, longer window than Groq. Fireworks if you want eval tooling; Together if you’ll finetune.
Multi-model product, one integration OpenRouter or n4n.ai. OpenRouter for breadth and simple billing. n4n.ai when you need to pin or auto-fallback across providers and keep per-token metering across all calls.
Cost-optimized at scale with variability Run the cheapest Llama 4 Scout API price comparison live: point at n4n.ai, set route hints by traffic type, and let it shift to the cheapest healthy upstream. You pay token parity plus gateway transparency.
Self-host curious None of the above. Deploy the weights on your own H100s and front with vLLM. API cost zero, but you own p99.
Pick by traffic shape, not by the lowest number on a pricing page.