OpenRouter vs Together vs Fireworks 2026 Compared

The same open model can carry three different prices across these three platforms, and the reason is structural. OpenRouter is an aggregating router that reaches 300+ models through one API and adds a 5.5% platform fee; Together and Fireworks host models directly and price per host. So the cheapest path depends on whether you value one integration across many models or a single model served efficiently at scale. This article sorts the routing-versus-direct tradeoff and works a concrete example.

1. Router vs direct host

• OpenRouter is an aggregating router. One API key reaches 300+ models across many providers, and OpenRouter can route a model to the cheapest available host^[1][4].
• Together is a direct inference host that serves a wide catalog of open models with per-model token pricing^[2].
• Fireworks is a direct inference host that prices in parameter-size tiers and discounts cached input and batch inference by 50%^[3].

The first decision is breadth versus directness. If you want to try many models or fail over between providers with one integration, OpenRouter's router is the convenience play. If you have settled on one or two specific models and run real volume, going direct to whichever host serves them cheapest avoids the router fee.

2. The fee structures

Fees verified against each vendor's pricing page as of May 26, 2026.

Provider	Model markup	Platform fee	Discounts
OpenRouter	None (passthrough)[1]	5.5% on pay-as-you-go credits[1]	BYOK: 5% after ~1M free reqs/mo[1]
Together	Per-model direct pricing[2]	None (direct)[2]	Varies by model[2]
Fireworks	Parameter-tier pricing[3]	None (direct)[3]	50% off cached input and batch[3]

OpenRouter's deal is no model markup plus a 5.5% fee on credit usage, so you pay the underlying rate plus that fee for the convenience of one API across many models^[1]. Together and Fireworks have no router fee but price each model on their own infrastructure, with Fireworks offering 50% discounts on cached input and batch inference that can materially cut cost for repetitive or offline workloads^[2][3].

3. Same model, different price

The key insight for cost: the same open-weights model can cost different amounts on different hosts. A large Llama or DeepSeek variant priced per million tokens on Together can differ from the same model on Fireworks, because each provider runs its own serving stack and sets its own margin^[2][3].

OpenRouter exploits exactly this by routing to the cheapest available host, but adds its 5.5% fee on top^[1]. So the comparison is never "which platform is cheapest" in the abstract; it is "for this specific model at my token mix, which is cheapest after fees." For a heavy workload on one model, go direct to the cheaper host; for a varied workload across many models, OpenRouter's routing plus fee can still net lower than integrating five providers. Track the token math with the token cost optimization playbook.

4. Worked cost example

Suppose a model lists at $0.80 per million input and $0.90 per million output tokens on its cheapest direct host, and you run 100M input plus 30M output tokens in a month:

Path	Base token cost	Fee	Total
Direct host	$80 + $27 = $107[2]	$0	$107
OpenRouter (same rate)	$107[1]	+5.5% = $5.89[1]	~$113

The arithmetic: 100M input × $0.80/M = $80, plus 30M output × $0.90/M = $27, for $107 in base token cost. Direct, you pay $107. Through OpenRouter at the same underlying rate you add 5.5% ($5.89) for about $113. The router costs roughly $6 more here for the convenience of one API and automatic failover. If OpenRouter can route the model to a host even ~6% cheaper than your chosen direct host, the fee washes out. So the decision turns on whether routing finds a cheaper host than you would pick yourself, and how much you value a single integration. Model the full stack with the AI stack cost calculator.

5. Decision guidance

• Trying many models, want one API and failover: OpenRouter, accepting the 5.5% pay-as-you-go fee.
• Settled on one model at real volume: go direct to the cheaper of Together or Fireworks for that model.
• Repetitive or offline workloads: Fireworks, for its 50% cached-input and batch discounts.
• Already have a provider key and high request volume: OpenRouter BYOK (5% after the free allowance) or direct.
• Cost-critical at scale: compare the exact model at each host after fees; do not assume parity.

Re-verify each pricing page and the specific model rate before committing; inference pricing moves often and per-host rates diverge. For the cross-vendor frontier view, see the cheapest LLM API ranking, and to fold inference into your full stack, the AI stack cost calculator.

All pricing figures verified against official pricing pages as of 2026-05-26.

Frequently asked questions

Is OpenRouter cheaper than Together or Fireworks?

OpenRouter does not mark up model pricing; you pay the underlying provider's posted rate, plus a 5.5% platform fee on pay-as-you-go credit usage, verified May 2026. Because OpenRouter routes to many providers, it can route a given model to the cheapest available host, so on shared models it is frequently competitive or cheaper. Together and Fireworks host models directly, so for a model they run efficiently their direct rate can undercut routing through OpenRouter once the 5.5% fee is added. There is no universal winner: compare the specific model you need at each provider, because the same model can carry different rates.

What fee does OpenRouter charge in 2026?

OpenRouter charges a 5.5% platform fee on pay-as-you-go credit usage and states it does not mark up the model rates shown in its catalog, meaning the per-token price matches the underlying provider's posted rate. If you bring your own provider API key (BYOK), there is a free allowance of around 1M requests per month and a 5% fee after that. So the trade is convenience and routing across 300+ models for a single fee, versus integrating each provider directly with no router fee. For low volume the convenience usually outweighs the 5.5%.

Why does the same model cost different amounts on Together and Fireworks?

Because each host runs the model on its own infrastructure and prices independently. The same open-weights model (for example a large Llama or DeepSeek variant) can cost meaningfully more for input or output tokens on one host than another, even though the weights are identical, because pricing reflects each provider's serving stack, batching, and margin. Fireworks prices in parameter-size tiers and discounts cached input and batch inference by 50%; Together prices per model. The practical upshot: never assume two hosts charge the same for the same model. Check the specific model on each before committing volume.