diff --git a/__LOCAL_LLMs/docs/07-model-recommendations.md b/__LOCAL_LLMs/docs/07-model-recommendations.md
index c64ea1c7..f31e66d6 100644
--- a/__LOCAL_LLMs/docs/07-model-recommendations.md
+++ b/__LOCAL_LLMs/docs/07-model-recommendations.md
@@ -31,10 +31,12 @@
 
 ## Tier 4 — Reasoning & Deep Thinking
 
-| Model                 | Size  | RAM Used | Pull Command                  | Notes                                            |
-| --------------------- | ----- | -------- | ----------------------------- | ------------------------------------------------ |
-| **`deepseek-r1:32b`** | 20 GB | ~22 GB   | `ollama pull deepseek-r1:32b` | Chain-of-thought reasoning, closest to Kimi k1.5 |
-| `deepseek-r1:7b`      | 5 GB  | ~6 GB    | `ollama pull deepseek-r1:7b`  | Lightweight reasoning                            |
+| Model                 | Size  | Parameters | Quant  | RAM Used | Pull Command                  | Notes                                                                                                                             |
+| --------------------- | ----- | ---------- | ------ | -------- | ----------------------------- | --------------------------------------------------------------------------------------------------------------------------------- |
+| **`deepseek-r1:32b`** | 20 GB | 32B        | Q4_K_M | ~22 GB   | `ollama pull deepseek-r1:32b` | Chain-of-thought reasoning — emits `<think>` blocks before JSON output; ~75–80% of llama3.3:70b reasoning quality at half the RAM |
+| `deepseek-r1:7b`      | 5 GB  | 7B         | Q4_K_M | ~6 GB    | `ollama pull deepseek-r1:7b`  | Lightweight reasoning, good for quick triage                                                                                      |
+
+> **⚠️ JSON output note:** DeepSeek R1 models emit `<think>...</think>` reasoning traces before the JSON response. Strip these before `JSON.parse()` — see [06-extraction-service-evals.md](06-extraction-service-evals.md) for the transform pattern.
 
 ## Tier 5 — Vision (Multimodal)
 
@@ -78,17 +80,62 @@ Only one loads into RAM at a time. You can have all 10 on disk simultaneously.
 
 ## By Use Case (Quick Reference)
 
-| Use Case                   | Best Model          | Fallback                |
-| -------------------------- | ------------------- | ----------------------- |
-| **TypeScript/ESM coding**  | `qwen2.5-coder:32b` | `qwen2.5-coder:7b`      |
-| **Python coding**          | `qwen2.5-coder:32b` | `deepseek-coder-v2:16b` |
-| **Swift/iOS coding**       | `qwen2.5-coder:32b` | `codestral:22b`         |
-| **Extraction evals**       | `llama3.1:8b`       | `qwen2.5:7b`            |
-| **JSON structured output** | `qwen2.5:7b`        | `qwen2.5-coder:7b`      |
-| **Complex reasoning**      | `deepseek-r1:32b`   | `phi4:14b`              |
-| **Image understanding**    | `llava:34b`         | `qwen2.5vl:7b`          |
-| **Embeddings**             | `nomic-embed-text`  | `mxbai-embed-large`     |
-| **Fast iteration**         | `qwen2.5-coder:7b`  | `llama3.1:8b`           |
+| Use Case                       | Best Model          | Fallback                |
+| ------------------------------ | ------------------- | ----------------------- |
+| **TypeScript/ESM coding**      | `qwen2.5-coder:32b` | `qwen2.5-coder:7b`      |
+| **Python coding**              | `qwen2.5-coder:32b` | `deepseek-coder-v2:16b` |
+| **Swift/iOS coding**           | `qwen2.5-coder:32b` | `codestral:22b`         |
+| **Extraction evals**           | `llama3.1:8b`       | `qwen2.5-coder:32b`     |
+| **JSON structured output**     | `qwen2.5-coder:32b` | `qwen2.5:7b`            |
+| **Complex reasoning / triage** | `deepseek-r1:32b`   | `phi4:14b`              |
+| **Brain signal routing**       | `deepseek-r1:32b`   | `qwen2.5-coder:32b`     |
+| **Image understanding**        | `llava:34b`         | `qwen2.5vl:7b`          |
+| **Embeddings**                 | `nomic-embed-text`  | `mxbai-embed-large`     |
+| **Fast iteration / dev evals** | `llama3.1:8b`       | `qwen2.5-coder:7b`      |
+
+---
+
+## Comprehensive Model Comparison Table
+
+All models discussed — detailed capability reference for M4 Pro 48 GB:
+
+| Model                   | Disk    | Params | Quant  | RAM    | Tok/s     | JSON           | Reasoning  | Code       | Instruction Following | Context | `<think>` | Status on this machine     |
+| ----------------------- | ------- | ------ | ------ | ------ | --------- | -------------- | ---------- | ---------- | --------------------- | ------- | --------- | -------------------------- |
+| `llama3.1:8b`           | 4.9 GB  | 8B     | Q4_K_M | ~6 GB  | 40–60     | ✅ Good        | ⭐⭐       | ⭐⭐       | ⭐⭐⭐                | 128k    | ❌        | ✅ Installed               |
+| `qwen2.5-coder:32b`     | 18.5 GB | 32.8B  | Q4_K_M | ~22 GB | 15–25     | ✅ Excellent   | ⭐⭐⭐     | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐            | 128k    | ❌        | ✅ Installed               |
+| `deepseek-r1:32b`       | 20 GB   | 32B    | Q4_K_M | ~22 GB | 12–20     | ⚠️ Needs strip | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐   | ⭐⭐⭐⭐              | 128k    | ✅ Yes    | 🔲 Not installed           |
+| `llama3.3:70b` (Q4)     | 40 GB   | 70B    | Q4_K_M | ~42 GB | 5–10      | ✅ Excellent   | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐   | ⭐⭐⭐⭐⭐            | 128k    | ❌        | ⚠️ Tight (6GB left for OS) |
+| `qwen2.5:7b`            | 5 GB    | 7B     | Q4_K_M | ~6 GB  | 40–60     | ✅ Excellent   | ⭐⭐       | ⭐⭐⭐     | ⭐⭐⭐⭐              | 128k    | ❌        | 🔲 Not installed           |
+| `deepseek-r1:7b`        | 5 GB    | 7B     | Q4_K_M | ~6 GB  | 35–50     | ⚠️ Needs strip | ⭐⭐⭐⭐   | ⭐⭐⭐     | ⭐⭐⭐                | 128k    | ✅ Yes    | 🔲 Not installed           |
+| `phi4:14b`              | 9 GB    | 14B    | Q4_K_M | ~11 GB | 25–35     | ✅ Good        | ⭐⭐⭐⭐   | ⭐⭐⭐     | ⭐⭐⭐⭐              | 16k     | ❌        | 🔲 Not installed           |
+| `deepseek-coder-v2:16b` | 10 GB   | 16B    | Q4_K_M | ~12 GB | 25–35     | ✅ Good        | ⭐⭐⭐     | ⭐⭐⭐⭐   | ⭐⭐⭐⭐              | 128k    | ❌        | 🔲 Not installed           |
+| `codestral:22b`         | 13 GB   | 22B    | Q4_K_M | ~15 GB | 20–30     | ✅ Good        | ⭐⭐⭐     | ⭐⭐⭐⭐   | ⭐⭐⭐⭐              | 32k     | ❌        | 🔲 Not installed           |
+| `gemini-2.5-flash`      | —       | —      | Cloud  | —      | ~1s/req   | ✅ Excellent   | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐            | 1M      | ❌        | ☁️ Cloud ($0.003/run)      |
+| `gpt-4o`                | —       | —      | Cloud  | —      | ~1–2s/req | ✅ Excellent   | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐ | ⭐⭐⭐⭐⭐            | 128k    | ❌        | ☁️ Cloud ($0.05–0.15/run)  |
+
+### Column Key
+
+| Column                    | Meaning                                                              |
+| ------------------------- | -------------------------------------------------------------------- |
+| **Tok/s**                 | Tokens per second on M4 Pro 48 GB (Metal backend)                    |
+| **JSON**                  | Reliability of structured JSON output compliance                     |
+| **Reasoning**             | Multi-step / chain-of-thought quality (⭐ = weak, ⭐⭐⭐⭐⭐ = best) |
+| **Code**                  | Code generation quality across TS/Python/Swift                       |
+| **Instruction Following** | Adherence to output format constraints                               |
+| **`<think>`**             | Emits reasoning traces before output (needs stripping for JSON)      |
+
+### Gap Analysis vs llama3.3:70b (cloud-quality ceiling locally)
+
+| Gap                    | `llama3.1:8b` | `qwen2.5-coder:32b` | `deepseek-r1:32b` |
+| ---------------------- | :-----------: | :-----------------: | :---------------: |
+| Multi-step reasoning   |     ~40%      |        ~65%         |       ~80%        |
+| Strict JSON compliance |     ~75%      |        ~95%         |      ~70%\*       |
+| Brain signal routing   |     ~60%      |        ~80%         |       ~90%        |
+| Code generation        |     ~55%      |        ~95%         |       ~80%        |
+| Instruction following  |     ~70%      |        ~90%         |       ~85%        |
+| **Overall vs 70B**     |   **~55%**    |      **~85%**       |    **~75–80%**    |
+
+\*With `<think>` strip transform applied
 
 ---