OpenRouter
1 Tool · 199 Intel
OpenRouter is a unified API platform, founded in early 2023, that gives developers access to hundreds of large language models from OpenAI, Anthropic, Google, and dozens of other providers through a single endpoint. It handles routing, caching, and uptime fallback across providers and charges a small fee on top of underlying model costs rather than marking up prices. The company raised a $12.5 million seed round in early 2025 followed by a $28 million Series A led by Menlo Ventures.
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Tools
Async Python agent toolkit for OpenRouter with tools, streaming, and human approval gates.
Intel
The upgraded Auto Router classifies each prompt into roughly 30 task types, then ranks models by the community's real spend share for that task over the prior 7 days, filtered by your cost tier. It keeps multi-turn conversations on one model to avoid cache rebuilds.
Cloudflare's open-source Clef (27B) and Clef Flash (9B) decision models are on OpenRouter. They accept text, JSON or images and return typed answers with probabilities. Input costs $0.24/M and $0.09/M, with output free.
OpenRouter adds Cerebras as a provider, quoting 3,000+ tokens per second on Llama 4 Scout and 1,800+ on Llama 3.3 70B. The wafer-scale design keeps 40 GB of memory on chip, removing the external memory bottleneck that caps decode speed.
An explainer on the layer between an app and multiple model providers: unified request format, failover, routing, guardrails and cost attribution, plus where OpenRouter, LiteLLM, Portkey, Kong AI Gateway and Helicone differ on hosting, compliance and setup time. Written by OpenRouter.
OpenRouter extends zero completion token insurance to every model: a response with no output tokens and a blank, null or error finish reason is not billed, even when the upstream provider charges for prompt processing. Failed calls read as zero cost.
TypeSafe's Jev 1.14 is live on OpenRouter. The release improves multilingual performance most, adds experimental vision input, and keeps pricing at $0.042 per million input tokens with free output.
Classification of LLM traffic against a taxonomy you define, applied by a small model after the request completes so inference latency is untouched. Sampling rates keep the tagging bill proportional, and the tags land in logs and usage analytics.