claude-fable-5 vs claude-opus-4-8
claude-opus-4-8 is about 50% cheaper per 1M input tokens on CaMeL Hub.
| claude-fable-5 | claude-opus-4-8 | |
|---|---|---|
| Provider | Anthropic | Anthropic |
| per 1M input tokens | $10.00 | $5.00 |
| per 1M output tokens | $50.00 | $25.00 |
| Context window | 1000000 tokens | 1000000 tokens |
| Capabilities | streaming, vision, function-calling, reasoning |
When to use which
claude-fable-5
claude-fable-5 is Anthropic's most capable widely released model, built for long-running agent work. On CaMeL Hub you call it with any OpenAI SDK by setting base_url to https://api.camel-hub.com/v1 — $10 per 1M input tokens, $50 per 1M output tokens. It ships with a 1M-token context window, up to 128K output tokens, and adaptive reasoning that is always on, so the model decides how deeply to think on each request without you managing a thinking budget.
- Long-running autonomous coding agents that plan, edit, and verify changes across a large repository over many hours
- Whole-corpus analysis — contracts, filings, or research collections loaded into the 1M-token context in a single request
- Multi-step tool orchestration where the agent must diagnose failed calls, re-plan, and keep state consistent
- High-stakes reasoning tasks such as architecture reviews, incident postmortems, and math-heavy analysis
claude-opus-4-8
Claude Opus 4.8 is Anthropic's model for complex agentic coding and enterprise work. On CaMeL Hub it costs $5 per 1M input tokens and $25 per 1M output tokens — call it through the OpenAI-compatible API by setting base_url to https://api.camel-hub.com/v1 and model to "claude-opus-4-8", or use the Anthropic-native endpoint with your existing Claude tooling.
- Long-running coding agents (Claude Code, Cursor, Cline) performing multi-file refactors and test-driven bug fixes across large repositories
- Whole-codebase or multi-document analysis in a single request, loading up to 1M tokens of source files, contracts, or logs as context
- Function-calling agent backends that orchestrate many tools over dozens of sequential steps, such as research, ops automation, or data pipelines
- Enterprise document workflows — contract review, RFP drafting, policy summarization — that need long, structured outputs up to 128K tokens