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+# DeepSeek vs Local Ollama — Cost Comparison
+
+## Per-Scan Cost (Appointment Screenshot Extraction)
+
+| | DeepSeek V4 Flash | Local qwen2.5:14b (Ollama) |
+|---|---|---|
+| OCR step | Free (browser Tesseract.js) | Free (browser Tesseract.js) |
+| LLM input tokens | ~2,000 tokens | ~2,000 tokens |
+| LLM output tokens | ~400 tokens (non-thinking) | ~400 tokens |
+| **Per-scan cost** | **$0.0004** | **$0.0015** (GPU power) |
+| **Scans per dollar** | ~2,500 | ~667 |
+| **Annual (daily)** | ~$0.15 | ~$0.55 |
+
+## Token Breakdown
+
+Typical appointment screenshot (10 appointments):
+- System prompt: ~1,200 tokens (extraction rules, OCR artifact fixes, VIN recovery)
+- OCR text: ~800 tokens (raw text from 10 appointment rows)
+- Output JSON: ~400 tokens (structured appointments array)
+- Total: ~2,400 tokens
+
+## Pricing (DeepSeek V4 Flash, June 2026)
+
+| | Per 1M tokens |
+|---|---|
+| Input (cache miss) | $0.14 |
+| Output | $0.28 |
+
+Note: Cache hits are rare for one-off scan requests. The system prompt is large (~1,200 tokens) and repeatable across scans, so cached input could drop cost 95% if DeepSeek's KV cache covers it.
+
+## Thinking Mode
+
+V4 Flash defaults to **thinking mode** (adds ~200 internal reasoning tokens to output). Disabling via `thinking: {type: "disabled"}` keeps costs at the lower rate. The appointment extraction task is straightforward structured parsing — thinking adds cost with no accuracy benefit.
+
+## Privacy Trade-off
+
+- **DeepSeek**: Customer names, phone numbers, VINs, and vehicle details are sent to DeepSeek's cloud API servers
+- **Ollama**: All data stays on the local GPU (RTX 2080 Ti FE, 11GB VRAM)
+- Cost difference is negligible at ~$0.40/year — the deciding factor is privacy preference