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Side-by-side features, use cases and pricing — because the right pick depends on your job and budget, not just the ranking.

OpenRouter logo
OpenRouter
✓ verifiedFreemium

Unified API gateway that routes requests to 400+ LLMs across 70+ providers with failover and no subscription.

17M visits/mo
4.4M visits/mo
Qoder logo
Qoder
✓ verifiedFreemium

Agentic AI platform with a coding desktop app, CLI, and cloud agents for autonomous software development and office work.

2.7M visits/mo32K saves
Dify.ai logo
Dify.ai
✓ verifiedFreemium

Open-source platform to build, deploy and monitor agentic AI workflows and RAG apps, with cloud, self-host and enterprise options.

1.1M visits/mo
Pricing
Free: $0 (free models only, 50 requests/day)
Pay-as-you-go: 5.5% platform fee on inference

No public pricing

No public pricing

Free trial available

Sandbox: Free (200 message credits)
Professional: $590/workspace/year
Team: $1,590/workspace/year
Core features
  • One unified, OpenAI-compatible API for 400+ models
  • Automatic provider failover for higher uptime
  • Edge routing for low latency
  • Custom data and provider policies
  • Pay-as-you-go credits usable across any model
  • Dialogue with GLM large model
  • AI search
  • AI drawing
  • AI reading
  • AI-generated video (沉思清影-AI生视频)
  • AI-generated PPT
  • Data analysis tools
  • Code assistance (代码速写)
  • Intelligent agents
  • Multi-agent collaboration for end-to-end tasks
  • Persistent memory and custom rules
  • Extensible skills and plugins
  • Rich context across code, images, and directories
  • Automatic codebase documentation generation
  • Terminal-native CLI and JetBrains IDE plugin
  • Cloud-hosted agents for enterprise use
  • Visual workflow studio for agents
  • RAG knowledge pipelines
  • Agent runtime with tools and memory
  • Marketplace of models and plugins
  • Publish as app, API or MCP tool
  • Logging, analytics and monitoring
Use cases
  • Accessing many LLMs through one integration
  • Adding provider redundancy to AI apps
  • Comparing model price and performance
  • Powering agents and AI-native products
  • Engaging in conversations with an AI model
  • Generating images and videos using AI
  • Creating presentations with AI assistance
  • Analyzing data with AI tools
  • Assisting with code development
  • Autonomous feature development in large codebases
  • Terminal-based AI pair programming
  • Cross-department task automation for legal, finance, HR
  • Onboarding developers to unfamiliar codebases
  • Building AI agents and chatbots
  • Creating RAG-based knowledge apps
  • Deploying LLM apps at enterprise scale
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