Llama 3.1 405B pricing across five inference gateways

Any Llama 3.1 405B API pricing comparison that stops at the per-million-token rate misses the variables that actually blow up your bill: fallback behavior, context limits, and concurrency caps. This post puts five gateways side by side—OpenRouter, Together AI, Fireworks AI, DeepInfra, and n4n.ai—so you can choose based on engineering constraints rather than the cheapest headline number.

The contenders

All five expose Llama 3.1 405B Instruct through an HTTP API. Four are standalone inference platforms; n4n.ai is an OpenRouter-class gateway that aggregates 240+ models behind one OpenAI-compatible endpoint and adds automatic fallback when an upstream provider is degraded.

• OpenRouter – Meta’s 405B Instruct, routed across multiple upstream providers.
• Together AI – Self-hosted optimized inference, dedicated 405B endpoints.
• Fireworks AI – Fireworks-served 405B with custom CUDA kernels.
• DeepInfra – Commodity GPU cloud serving open weights.
• n4n.ai – Unified endpoint that forwards to providers and honors client routing directives.

Head-to-head table

* Together supports 128K on some models but 405B capped at 32K at time of writing.
** Fireworks 405B context is 16K in standard offering.

Capabilities and model variants

Every gateway here serves the Instruct fine-tune of Llama 3.1 405B. None expose the raw base model for arbitrary completion through the standard chat endpoint. OpenRouter and n4n.ai additionally let you pin a specific upstream (e.g., meta-llama/llama-3.1-405b-instruct:fp8) via model string suffixes, while Together and Fireworks present a single canonical build.

If you need FP8 or quantized variants to cut memory footprint, OpenRouter’s routing string and n4n.ai’s routing directives are the only ones in this set that expose that choice without a separate endpoint URL.

Price and cost model

The Llama 3.1 405B API pricing comparison table shows input and output prices are symmetric for all five. DeepInfra is cheapest at $2.50/MTok; Together is priciest at $3.50. n4n.ai and OpenRouter list $2.70, but n4n.ai applies per-token usage metering with no markup beyond the upstream cost on its default route.

There are no monthly minimums. You pay for tokens. Fireworks and Together occasionally run promotional credits, but production budgeting should assume the listed rates. For high-volume batch jobs, DeepInfra’s $0.20/MTok saving versus OpenRouter compounds: 10B output tokens/month is a $2,000 difference.

Latency and throughput

Raw 405B is slow. Expect 30–60 tokens/sec on a single stream from commodity providers (DeepInfra). Together and Fireworks use tuned serving stacks (TensorRT-LLM style) to push 50–80 tokens/sec. OpenRouter’s latency depends on which upstream it selects; n4n.ai adds a sub-10ms routing layer but inherits the chosen provider’s generation speed.

Throughput under concurrency is where single-provider gateways hurt: Together caps default at 200 req/min, Fireworks at 100 on free tier. OpenRouter and n4n.ai spread load across providers, so burst capacity is effectively the sum of upstream quotas. If you need 1,000 concurrent summaries, DeepInfra’s 60 req/min will block you unless you negotiate.

Ergonomics and API design

All five are OpenAI-compatible, so the same Python client works with a base_url swap:

from openai import OpenAI

client = OpenAI(
    base_url="https://api.n4n.ai/v1",  # or together, fireworks, etc.
    api_key="sk-...",
)
resp = client.chat.completions.create(
    model="meta-llama/llama-3.1-405b-instruct",
    messages=[{"role": "user", "content": "Summarize RFC 9000"}],
    max_tokens=512,
)

n4n.ai and OpenRouter accept extra headers like x-router-prefer: fp8 to steer variant selection; Together and Fireworks ignore unknown headers. Fireworks requires model="llama-3.1-405b-instruct" exactly, while DeepInfra uses meta-llama/Meta-Llama-3.1-405B-Instruct. Minor string differences cause 404s if you abstract across gateways.

Ecosystem and tooling

OpenRouter has the richest community: prebuilt LangChain adapters, a model marketplace, and usage dashboards. Together ships a Python SDK with async support and fine-tune hooks. Fireworks provides a React playground and function-calling examples. DeepInfra is barebones but has a one-click OpenAPI spec.

n4n.ai forwards provider cache-control hints (cache_control in messages) to upstreams that support prompt caching, so you can reuse system prompts without rewriting your client. That matters when 405B system prompts are large and you’re paying $2.70/MTok repeatedly.

Limits and quotas

Context windows are the silent cost driver. Fireworks’ 16K limit forces chunking for long docs; you’ll pay more tokens via overlap. OpenRouter and n4n.ai expose 128K, but generation beyond 32K on 405B is rare in practice due to memory.

Rate limits: DeepInfra 60 req/min, Fireworks 100 (free) / 3,000 (paid), Together 200, OpenRouter tiered by spend, n4n.ai inherits upstream but fails over when one hits 429. For background extraction jobs, set max_retries in your client and backoff; single-provider gateways will hard-limit you.

Which to choose

Cost-sensitive batch processing (billions of tokens/mo): DeepInfra at $2.50/MTok wins if you can live with 32K context and 60 req/min. Use a queue.

Low-latency interactive apps: Fireworks or Together for tuned kernels. Pay the $0.30–$0.80 premium per MTok for 2x tokens/sec.

Multi-model routing with fallback: OpenRouter or n4n.ai. If you already use other models and want one endpoint, n4n.ai’s automatic fallback and cache-control forwarding reduce custom code. The Llama 3.1 405B API pricing comparison shows they sit at the median price.

Long-context RAG over 100K docs: Only OpenRouter and n4n.ai offer 128K. n4n.ai’s routing directives let you force FP8 to cut cost on bulk embedding-like passes.

Prototyping: Fireworks free tier is fastest to sign up; switch to DeepInfra when scale hits.

Pick by limit first, price second. A 405B call that gets rate-limited costs more in engineering time than the $0.20/MTok you saved.