Blog — OpenRouter Blog

How to Stop an AI Agent Stuck in an Infinite Loop

An AI agent gets stuck in an infinite loop when a model keeps calling a tool that fails, usually with the same arguments, because nothing in the agent loop tells it to stop. Each retry resends the whole conversation, so every turn costs more than the last, and a loop left running overnight can cost thousands of dollars.

November 12, 2026

How to Benchmark LLMs

Choose models using evidence from your application’s inputs and output formats. Run identical tasks through OpenRouter and compare graded accuracy, billed cost, and request time. On our 60 synthetic tasks, GPT-6.1 Sol passed 180/180 trials across three repeats under the tested contracts and settings.

November 12, 2026

Self-Hosted LLM vs API: When Does Self-Hosting Pay Off

If you pay an API bill for an open-weight model, you may wonder whether your own GPU would cost less. GPU rent is charged every hour, busy or idle, while an API charges only for the tokens you use.

November 12, 2026

LLM API Rate Limits: How to Fix OpenAI and Claude 429 Errors

A 429 Too Many Requests error means you sent more requests or tokens than your limit allows in the current time window. OpenAI, Anthropic and OpenRouter all return it, and it usually shows up the first time an app runs under real load or a batch job fans out across several workers.

November 11, 2026

LLM Quantization: What fp8 and fp4 Do to Quality and Price

When you call an open-weight model through an API, several providers may serve it at different precisions, such as 16-bit bf16, 8-bit fp8 or 4-bit fp4. The precision can affect both the answers and the price. LLM quantization creates these versions by storing a model's weights in fewer bits, so the model needs less memory and hardware, at some cost in accuracy.

November 11, 2026

MCP vs CLI vs Skills: What Each Costs in Context

Every tool you give an agent sends its definition to the model on every request, and you pay for it as input tokens. A Model Context Protocol (MCP) server can add tens of thousands of tokens to the context window. The usual alternatives are a command-line tool (CLI) or a skill.

November 11, 2026

Best OCR Model for Scanned Documents, Measured

We made eight scanned pages from text we wrote, ran them through 15 vision models, Mistral OCR and Tesseract, and checked every value. Claude Opus 5.5 was the most accurate and Mistral OCR the best value.

November 10, 2026

How to Build a Voice AI Agent

A voice agent needs to do more than generate a good answer. It must understand what someone said, decide what to do, call any needed tools, and start speaking again before the pause gets awkward.

November 10, 2026

Introducing the Extractions API

A new API for pulling typed fields out of documents and images: name the fields, get values, spans and confidence back. Three models serve it at launch; on Cloudflare's Clairic the same request reads text and images and returns numbers, dates, lists, estimates and forecasts.

November 10, 2026

How to Get Structured Output from Any LLM: JSON Schema, Tool Calls, or a Decision Model

When your code reads fields from a model's reply, like an order ID or a refund amount, it expects clean JSON every time. Most of the time, it gets it. But now and then the model wraps the JSON in a code fence, adds a field you didn't ask for, or sends a number as text, and your code breaks. If you use several models, one fix won't cover them all, because each model and provider handles structured output in its own way.

November 10, 2026

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How to Stop an AI Agent Stuck in an Infinite Loop

OpenRouter is Joining Stripe

Ori Eval: Find the Best Model for What You're Building

Surpassing Frontier Performance with Fusion

How to Benchmark LLMs

Self-Hosted LLM vs API: When Does Self-Hosting Pay Off

LLM API Rate Limits: How to Fix OpenAI and Claude 429 Errors

LLM Quantization: What fp8 and fp4 Do to Quality and Price

MCP vs CLI vs Skills: What Each Costs in Context

Best OCR Model for Scanned Documents, Measured

How to Build a Voice AI Agent

Introducing the Extractions API

How to Get Structured Output from Any LLM: JSON Schema, Tool Calls, or a Decision Model