"OpenRouter thinking or reasoning capabilities" matching MCP servers:

Enables AI agents to query OpenRouter model information including prices, ELO rankings, context, and perform comparisons.

MCP server that provides OpenRouter model pricing data, enabling price lookups, trending/cheapest lists, and model searches without an API key.

Adds a deep_reasoning tool that consults stronger reasoning models (e.g., DeepSeek R1) to produce full reasoning traces for complex problems, helping the agent shape and cross-check its answers.

MCP server providing a model-agnostic subagent tool backed by OpenRouter, supporting multi-model Fusion and orchestration patterns.

An MCP server for discovering and querying over 300 AI models available on OpenRouter. It enables users to list, search, filter, compare, and get detailed information about models with pricing, context limits, and capabilities.

Enables AI assistants to consult other AI models via OpenRouter for help with coding tasks.

Gives AI assistants image generation and editing capabilities through OpenRouter, supporting multiple models, style presets, variations, and batch operations.

A Python MCP server that lets MCP hosts call OpenRouter models for chat, image generation, embeddings, and model search via FastMCP with .env support and retry handling.

An MCP server that provides access to 400+ AI models via OpenRouter, enabling chat, comparison, and document analysis through Claude.

Enables AI assistants like Claude or ChatGPT to query OpenRouter's 300+ models for pricing, context length, capabilities, and rankings through tools such as model-card, compare-models, cheapest-for, cost-estimator, and usage-rankings, using a public, key-less API with 6-hour caching.

Enables AI agents to browse, inspect, and compare LLMs from OpenRouter's catalog with normalized per-million-token pricing, context windows, and modalities. Supports filtering by search, free-only status, minimum context, or modality.

Provides reasoning budget estimates for AI tasks, making effort and time trade-offs visible through a compact control interface.

A powerful MCP server that enhances LLMs with advanced sequential thinking capabilities, supporting 19 thinking modes for structured reasoning and complex cognitive tasks.

MCP server for deep algorithmic and mathematical reasoning, wrapping any OpenAI-compatible LLM endpoint to return only the final distilled answer without reasoning traces. Supports per-call model and endpoint overrides, defaulting to vibethinker-3b via LM Studio.

Lets Claude Code delegate tasks to OpenRouter-backed Claude Code sessions in isolated child processes, keeping Anthropic and OpenRouter credentials separate. It adds a model catalog, per-job cost tracking, and API-key management for running tasks on 400+ OpenRouter models.

Lets a Codex parent delegate bounded work to explicitly approved OpenRouter model/provider routes with strict permission controls, selected-file review, and approval-gated artifact creation.

Provides AI systems with structured thinking frameworks and reasoning tools to maintain consistent problem-solving patterns across conversations. Enables multi-step reasoning, decision analysis, and systematic troubleshooting through invocable mental models.

Enables coding agents to obtain probabilistic decisions from Jev AI via OpenRouter for classification, scoring, and validation, with tools like jev_check, jev_classify, jev_score, and jev_evaluate.

An MCP server that enhances sequential thinking with Ultra Think methodology, enabling structured, step-by-step reasoning with quality metrics, bias detection, and resource management.

MCP server that helps AI assistants automatically find, filter, and rank the best AI models for a specific task using the OpenRouter API.