# Model × Agent Compatibility Matrix Recommendation matrix for which model to pair with each OMC/OMO agent, framed around cost vs. quality. This page exists so the recurring "어떤 모델을 어느 agent에 박아야 함?" question stops being tribal Discord knowledge. This is a **usage matrix, not a benchmark report**. Numbers and per-task scores are deliberately out of scope. ## Recommendation matrix | Agent | Role | Recommended (premium) | Recommended (cost-effective) | Avoid | Notes | |---|---|---|---|---|---| | Prometheus | Planning | Claude Opus 4.8, GPT-5.5 high | Sonnet 4.6 | — | Heavy reasoning; runs 1–2x per session | | Hyperplan | Planning | Claude Opus 4.8, GPT-5.5 high | Sonnet 4.6 | — | Same as Prometheus | | Sisyphus | Implementation | Sonnet 4.6 | DeepSeek V4 Pro, Kimi K2.5 | — | Token-heavy; cost matters most here | | Hephaestus | Implementation | Sonnet 4.6, Kimi K2.5 | DeepSeek V4 Pro | **GPT-\* (tool-calling/format breakage)** | Tuned for non-GPT | | Oracle | Review | Claude Opus 4.8, GPT-5.5 high | Sonnet 4.6 | — | Quality > cost; called sparingly | | Aletheia | Review | Sonnet 4.6 | DeepSeek V4 Pro | — | | | Hermes | Coordination | Sonnet 4.6 | DeepSeek V4 Flash | — | Coordinator only, not direct executor | ## Design rules These four rules drive every recommendation above. If you only remember one thing, remember rule 3. 1. **Planning/Review = expensive; Implementation = cheap.** Token weight typically differs 5–20× between a single Prometheus/Oracle pass and a full Sisyphus implementation loop. Spend on the rare, decisive calls; economize on the high-volume ones. 2. **Hephaestus should not be paired with GPT-family models.** Tool-calling and structured-output formats break. Use Sonnet 4.6 / Kimi K2.5 for premium and DeepSeek V4 Pro for cost-effective. This is the "Hephaestus is trash with non-GPT models" folklore turned the right way up. 3. **Sisyphus is the highest-value cost lever.** Because Sisyphus dominates total tokens in any non-trivial session, swapping it from Opus → Sonnet (or → DeepSeek V4 Pro) typically moves total spend more than any other single change. Tune this slot first. 4. **DeepSeek V4 Pro/Flash is now a first-class budget option.** Treat V4 Pro as the default cost-effective choice for execution agents (Sisyphus, Hephaestus, Aletheia) and V4 Flash as the default coordinator model. It is no longer an experimental fallback. ## Starter presets Pick the preset that matches your budget posture and adjust from there. Each block is a self-contained example — drop into your provider/agent config and edit per agent as needed. ### Premium (max quality) Use when correctness dominates cost: production-impacting refactors, security reviews, architecture decisions. ```yaml agents: Prometheus: { model: claude-opus-4-8 } Hyperplan: { model: claude-opus-4-8 } Sisyphus: { model: claude-sonnet-4-6 } Hephaestus: { model: claude-sonnet-4-6 } # never GPT-* Oracle: { model: claude-opus-4-8 } Aletheia: { model: claude-sonnet-4-6 } Hermes: { model: claude-sonnet-4-6 } ``` ### Balanced (default) Recommended starting point. Keeps planning/review on a strong model while moving the token-heavy implementation slot to a cost-effective one. ```yaml agents: Prometheus: { model: claude-sonnet-4-6 } Hyperplan: { model: claude-sonnet-4-6 } Sisyphus: { model: deepseek-v4-pro } Hephaestus: { model: kimi-k2-5 } # never GPT-* Oracle: { model: claude-sonnet-4-6 } Aletheia: { model: deepseek-v4-pro } Hermes: { model: deepseek-v4-flash } ``` ### Budget (cost-first) For long-running loops, batch refactors, or experimentation where total spend matters more than peak per-call quality. Keep Oracle on a strong model so the final review pass still catches regressions. ```yaml agents: Prometheus: { model: claude-sonnet-4-6 } Hyperplan: { model: claude-sonnet-4-6 } Sisyphus: { model: deepseek-v4-pro } Hephaestus: { model: deepseek-v4-pro } # never GPT-* Oracle: { model: claude-sonnet-4-6 } Aletheia: { model: deepseek-v4-pro } Hermes: { model: deepseek-v4-flash } ``` ## Out of scope - Provider routing internals (tracked elsewhere). - Benchmarks — this page is a usage matrix, not a benchmark report. - Hermes deep-coordination patterns.