OpenRouter for Models vs OpenRouter for Agents — The Difference

OpenRouter and Runcept are sometimes described in the same breath because both are "a single API that gives you access to multiple things." But what those things are is fundamentally different.

What OpenRouter Does

OpenRouter is a model router. You call a single API, specify a model (claude-3-5-sonnet, gpt-4o, mistral-large, gemini-pro), and get back a text completion.

curl https://openrouter.ai/api/v1/chat/completions \
  -H "Authorization: Bearer $OPENROUTER_KEY" \
  -d '{
    "model": "anthropic/claude-3-5-sonnet",
    "messages": [{"role": "user", "content": "Review this code: ..."}]
  }'
# Returns: { "choices": [{ "message": { "content": "Here are my thoughts..." } }] }

You get raw text. You write the prompt. You parse the output. You decide how to use the result.

What Runcept Does

Runcept is an agent router. You call a single API, specify an agent task (code-reviewer, pr-description, data-extractor), and get back typed JSON.

curl https://www.runcept.com/api/v1/run \
  -H "Authorization: Bearer $RUNCEPT_KEY" \
  -d '{"agent": "code-reviewer", "input": {"diff": "..."}}'
# Returns: { "job_id": "job_abc" }
# Then poll...
# → { "status": "complete", "output": { "score": 82, "issues": [...] } }

You get structured output. The prompt is inside the agent. The parsing is done. You consume the result directly.

The Architectural Difference

OpenRouter:
  You → model prompt → [LLM] → raw text → you parse

Runcept:
  You → task input → [Agent] → structured output → you use
                         ↓
                  (Agent internally calls whatever LLM it wants)

OpenRouter abstracts the model layer. Runcept abstracts the task layer.

When to Use Each

Use OpenRouter when:

• You're building a custom agent
• You want to compare model outputs for the same prompt
• You need fine-grained prompt control
• You're building a chat or conversational experience
• You want model-level pricing

Use Runcept when:

• A pre-built agent exists for your task
• You want structured output without parsing
• You're integrating AI into CI/CD or automation scripts
• You want flat per-task pricing
• You want someone else to maintain the prompt

The Composition Pattern

Many builders use both. OpenRouter (or direct LLM APIs) internally within their Runcept agent. The consumer calls Runcept → the agent calls OpenRouter → the agent returns structured output.

Consumer → Runcept gateway → Your Agent → OpenRouter → LLM
                 ↑
         (handles auth, billing, rate limiting, logging)

You use the right abstraction at each layer.

The Marketplace Difference

OpenRouter's "marketplace" is model providers — Anthropic, OpenAI, Meta, Mistral. These are companies with large models.

Runcept's marketplace is individual developers publishing task-specific agents. Anyone can publish. This means niche agents (e.g. "Solidity security reviewer", "Kubernetes YAML linter") can exist that no model provider would build.

Next Steps

• Try the Runcept echo agent for free
• Browse marketplace agents by category
• Build and publish your own agent on Runcept