OpenRouter Review: One API Key to Rule Them All

The modern LLM landscape has a fragmentation problem. Claude needs an Anthropic API key. GPT-5 needs an OpenAI key. Gemini needs a Google key. Llama needs a Meta or third-party inference key. If you are building an application that lets users choose their model - or if you want to route requests to the cheapest or fastest provider dynamically - you are looking at integrating and maintaining half a dozen API clients, billing relationships, and authentication flows. OpenRouter's pitch is simple: one API key, one endpoint, 300+ models. After six weeks of running production traffic through it, we can confirm that the pitch mostly delivers.

What OpenRouter Does

OpenRouter is an API routing layer. You send an OpenAI-compatible API request to openrouter.ai/api/v1/chat/completions, specify a model identifier (e.g., anthropic/claude-opus-4-6, openai/gpt-5.2, google/gemini-3-1-pro), and OpenRouter forwards your request to the appropriate provider, handles authentication, and returns the response in a standardized format.

The service supports 300+ models from every major provider (OpenAI, Anthropic, Google, Meta, Mistral, Cohere, xAI) plus dozens of open-source models hosted on third-party inference platforms (Together AI, Fireworks, Lepton). Pricing is pass-through plus a small margin - typically 0-5% above direct provider pricing, depending on the model and volume.

OpenRouter was founded in 2023 and has grown to process over 2 billion API calls per month. The company raised a $12 million Series A in late 2025 and is profitable on a unit economics basis, per its co-founder Alex Atallah.

Routing and Fallback

The core value beyond aggregation is intelligent routing. OpenRouter supports several routing modes:

Provider fallback: If Anthropic's API returns a 529 (overloaded) or times out, OpenRouter automatically retries with an alternative provider hosting the same model. For Claude models, this means falling back from Anthropic's direct API to AWS Bedrock or Google Vertex AI. We tested this by artificially triggering rate limits, and failover completed in under 2 seconds - fast enough that most applications would not notice.

Cost optimization: You can specify route: "cheapest" to automatically select the lowest-cost provider for a given model. For popular models hosted on multiple platforms, savings of 10-30% are common. During our testing, routing GPT-5.2 calls through the cheapest available provider saved 18% compared to direct OpenAI pricing.

Latency optimization: route: "fastest" selects the provider with the lowest current latency. OpenRouter maintains real-time latency measurements across all providers and makes routing decisions based on trailing 5-minute averages. In practice, this delivered 15-25% lower p95 latency compared to any single provider.

Model fallback: You can specify an ordered list of models (e.g., Claude Opus 4.6 -> GPT-5.2 -> Gemini 3.1 Pro) and OpenRouter tries each in sequence if the preferred model is unavailable.

Latency Overhead

The critical question for any proxy service: how much latency does the routing layer add? We measured time-to-first-token (TTFT) and total request duration for 1,000 requests each across five popular models, comparing direct API access to OpenRouter.

The overhead is consistently 50-70ms, which represents the round trip from your server to OpenRouter's proxy and from the proxy to the provider. For interactive chat applications, this is perceptible but not disruptive. For batch processing or non-interactive workloads, it is negligible. For latency-critical applications where every millisecond matters, direct API access remains the better choice.

Pricing and Billing

OpenRouter's billing model is straightforward. You load credits into your account (minimum $5), and each API call deducts from your balance at the listed per-token price. There are no monthly fees, no minimum commitments, and no separate charges for the routing layer on most models.

The pricing page is one of the best resources in the industry for comparing model costs. Every model lists its input and output token prices, and you can sort and filter by cost, speed, context window, and provider. For developers evaluating models, this comparison alone is worth bookmarking.

Volume discounts kick in at $500/month (5% discount) and $5,000/month (10% discount). Enterprise plans with custom pricing, dedicated endpoints, and SLA guarantees are available but not publicly listed.

One caveat: OpenRouter's pass-through pricing means you are exposed to provider price changes. When Anthropic adjusts Claude pricing, your OpenRouter costs change accordingly. The platform sends email notifications for price changes, but you need to monitor costs actively if you are running production workloads.

Developer Experience

The DX is excellent. A single line change - swapping the base URL from api.openai.com to openrouter.ai/api/v1 - migrates most OpenAI-SDK-based applications. Anthropic SDK users need a slightly larger change (OpenRouter normalizes everything to the OpenAI chat completions format), but the documentation provides copy-paste examples.

The dashboard provides real-time usage analytics, per-model cost breakdowns, latency histograms, and error rate monitoring. An activity feed shows every request with its routing decision, latency, and cost. For debugging routing issues or understanding provider behavior, this visibility is invaluable.

Rate limiting is handled per-account rather than per-model, which simplifies capacity planning. The default limit is 200 requests per minute, adjustable on request. OpenRouter also exposes a /models endpoint that returns real-time availability and pricing for all models - useful for building dynamic model selection into your application.

What Holds It Back

You are adding a dependency. OpenRouter is a single point of failure between your application and every LLM provider. If OpenRouter goes down, all your model calls fail regardless of provider health. During our six-week test, we observed two brief outages (under 5 minutes each), both resolved quickly. But the architectural concern remains.

Streaming reliability is slightly lower. We observed a 0.3% higher error rate on streamed responses compared to direct API access. Most errors were transient timeouts that resolved on retry, but for applications with strict reliability requirements, this matters.

Some provider features are not available. OpenRouter normalizes everything to the OpenAI chat completions format. Provider-specific features - Anthropic's prompt caching, Google's grounding, xAI's X search - are partially or not supported. If you need deep integration with a specific provider's unique capabilities, direct access is still necessary.

No fine-tuned model support. If you have fine-tuned a model on OpenAI or Anthropic's platform, you cannot route to it through OpenRouter. This limits the service to base and instruction-tuned models.

Strengths and Weaknesses

Strengths:

• Single API key for 300+ models across all major providers
• Intelligent routing with cost, latency, and fallback optimization
• Only 50-70ms latency overhead
• Excellent pricing comparison and transparency
• Strong developer experience with minimal migration effort
• Real-time analytics and routing visibility
• No monthly fees - pay only for usage
• Provider fallback handles outages automatically

Weaknesses:

• Single point of failure for all LLM calls
• 50-70ms latency overhead unsuitable for ultra-low-latency apps
• 0.3% higher error rate on streamed responses
• Some provider-specific features not supported
• No fine-tuned model routing
• Pass-through pricing means no protection from provider price increases
• Enterprise SLA details not publicly available

Verdict: 8.5/10

OpenRouter solves a real infrastructure problem with minimal overhead. The 50-70ms routing latency is the cost of not maintaining six separate API integrations, handling provider outages manually, and building your own cost optimization logic. For most applications, that trade is worth it.

The routing intelligence is genuinely useful. Provider fallback means your application survives individual provider outages. Cost optimization saves 10-30% on popular models. Latency optimization squeezes out 15-25% better p95 times. These are the kinds of improvements that are annoying to build yourself and trivial to get through OpenRouter.

The service is not for everyone. If you use a single provider exclusively, direct access is simpler and eliminates the dependency. If you need provider-specific features like prompt caching, direct integration is necessary. If you are running at scale where 50ms of latency represents meaningful cost or user experience degradation, the overhead matters.

But for the increasingly common case of applications that use multiple models, need fallback reliability, or want to dynamically optimize for cost or speed, OpenRouter is the best routing layer available. The fact that it costs effectively nothing (pass-through pricing with minimal markup) makes it an easy decision for most teams.

Sources:

• OpenRouter Official Site - OpenRouter
• OpenRouter API Documentation - OpenRouter
• OpenRouter Raises $12M Series A - Newcomer
• LLM API Routing Benchmark: OpenRouter vs. Direct Access - Latent Space