DeepSeek (Recommended)
POST https://api.deepseek.com/chat/completions
If you’re setting up Wolffish for the first time and want one provider that does it all — reliable tool use, strong reasoning, fast responses, minimal cost — start with DeepSeek V4 Pro. You can always add Anthropic or OpenAI later for specific use cases.
Getting an API Key
- Go to platform.deepseek.com
- Sign up or log in
- Navigate to API Keys and create a new key
- Paste it into Wolffish → Settings → Models → DeepSeek
Models
| Model | Context | Modes | Input / Output (per MTok) | Notes |
|---|
| deepseek-v4-pro | 1M | Off, High, Max | 0.44/0.87 | Recommended default. Frontier agentic performance. Cached: $0.01/MTok. |
| deepseek-v4-flash | 1M | Off, High, Max | 0.14/0.28 | Fast and cheap. Cached: $0.003/MTok. |
Reasoning modes
The brain icon next to the message box controls how this model reasons. Click it to cycle through the modes the selected model supports. Two separate ideas combine here:
Thinking — whether the model reasons
- Off — the model answers immediately. Fastest and cheapest; ideal for simple, direct tasks.
- On — the model first works through the problem in a dedicated reasoning pass before replying. Slower and uses more tokens, but markedly more accurate on multi-step, logical, or ambiguous tasks.
Effort — how hard it thinks
Only effort-capable models expose this; it applies once thinking is on.
- High — standard reasoning depth. The right default for most agentic work.
- Max — the model reasons longer and deeper for the hardest problems. More tokens and latency in exchange for higher quality on complex work.
| State | Colour | Meaning |
|---|
| Off | gray | Thinking off — direct answer |
| On | blue | Thinking on — no effort control |
| High | purple | Thinking on, standard effort |
| Max | orange | Thinking on, maximum effort |
Model Selection & Retries
Wolffish communicates with LLMs via nine cloud providers plus a local option, all using pure fetch() — no SDKs. Each provider has its own streaming format and tool-calling convention, which wernicke.ts normalizes into a single interface.
Select your Brain model explicitly in Settings → Modes — the model you choose is the one that runs. There’s no cascade or fallback order; if you want a second model for parallel work, turn on orchestrator mode and assign a Worker model.
When a cloud Brain hits a transient error, thalamus retries the same model on a backoff schedule (it also uses net.isOnline() for instant offline detection). It does not route you to a different provider on failure.