Think of AI models as developers on a team. Each has a different brain, different personality, different strengths. **A model isn't just "smarter" or "dumber." It thinks differently.** Give the same instruction to Claude and GPT, and they'll interpret it in fundamentally different ways.
Oh My OpenCode assigns each agent a model that matches its *working style* — like building a team where each person is in the role that fits their personality.
Sisyphus is the developer who knows everyone, goes everywhere, and gets things done through communication and coordination. Talks to other agents, understands context across the whole codebase, delegates work intelligently, and codes well too. But deep, purely technical problems? He'll struggle a bit.
Using Sisyphus with GPT would be like taking your best project manager — the one who coordinates everyone, runs standups, and keeps the whole team aligned — and sticking them in a room alone to debug a race condition. Wrong fit. No GPT prompt exists for Sisyphus, and for good reason.
Hephaestus is the developer who stays in their room coding all day. Doesn't talk much. Might seem socially awkward. But give them a hard technical problem and they'll emerge three hours later with a solution nobody else could have found.
Using Hephaestus with GLM or Kimi would be like assigning your most communicative, sociable developer to sit alone and do nothing but deep technical work. They'd get it done eventually, but they wouldn't shine — you'd be wasting exactly the skills that make them valuable.
Every agent's prompt is tuned to match its model's personality. **When you change the model, you change the brain — and the same instructions get understood completely differently.** Model matching isn't about "better" or "worse." It's about fit.
**Claude** responds to **mechanics-driven** prompts — detailed checklists, templates, step-by-step procedures. More rules = more compliance. You can write a 1,100-line prompt with nested workflows and Claude will follow every step.
**GPT** (especially 5.2+) responds to **principle-driven** prompts — concise principles, XML structure, explicit decision criteria. More rules = more contradiction surface = more drift. GPT works best when you state the goal and let it figure out the mechanics.
Real example: Prometheus's Claude prompt is ~1,100 lines across 7 files. The GPT prompt achieves the same behavior with 3 principles in ~121 lines. Same outcome, completely different approach.
Agents that support both families (Prometheus, Atlas) auto-detect your model at runtime and switch prompts via `isGptModel()`. You don't have to think about it.
These agents have Claude-optimized prompts — long, detailed, mechanics-driven. They need models that reliably follow complex, multi-layered instructions.
| **Prometheus** | Strategic planner | Claude Opus → GPT-5.2 → Kimi K2.5 → Gemini 3 Pro | Interview-mode planning. GPT prompt is compact and principle-driven. |
| **Atlas** | Todo orchestrator | Kimi K2.5 → Claude Sonnet → GPT-5.2 | Kimi is the sweet spot — Claude-like but cheaper. |
These agents do grep, search, and retrieval. They intentionally use the fastest, cheapest models available. **Don't "upgrade" them to Opus** — that's hiring a senior engineer to file paperwork.
You may see model names like `kimi-k2.5-free`, `minimax-m2.5-free`, or `big-pickle` (GLM 4.6) in the source code or logs. These are free-tier versions of the same model families, served through the OpenCode Zen provider. They exist as lower-priority entries in fallback chains.
You don't need to configure them. The system includes them so it degrades gracefully when you don't have every paid subscription. If you have the paid version, the paid version is always preferred.
Each agent has a fallback chain. The system tries models in priority order until it finds one available through your connected providers. You don't need to configure providers per model — just authenticate (`opencode auth login`) and the system figures out which models are available and where.
