docs/PHASE8-baseline: live /tokenize probes

Four findings, all align with formulate/analyze:

B1. /tokenize IGNORES the `model` request field — returns the
    tokenization of whichever model is currently loaded on the
    proxy backend, NOT the requested model. Acceptable: a real BPE
    count is still much better than char/4, and the gap between
    Qwen/Llama tokenizers is small. Cloud (OpenRouter) 404s
    regardless, so cloud falls back to char/4 via the capability
    cache.

B2. Latency 23-34ms per call, FLAT across input sizes 50-5000 chars.
    Network round-trip dominates. Per-turn _tokens cache amortizes
    to O(1); worst case 40 cached turns × ~30ms = 1.2s one-time
    cost on first enforce_budget call. Acceptable.

B3. Response shape confirmed: `{"tokens":[N1,N2,...]}` (token IDs;
    we use #response.tokens for count, discard the IDs). JSON not
    SSE; ffi.curl.M.post is the right call.

B4. Cloud /tokenize 404s as expected. Capability cache marks it
    unsupported on first probe; char/4 fallback silent thereafter.
    No design change.

Q-T5 RESOLVED per B1. All open questions now resolved.

Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>

docs/PHASE8-baseline.md

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+# Phase 8 Baseline — pre-implementation measurements
+
+**Date:** 2026-05-16
+**Tree probed:** `1a136d8` (PHASE8 formulate + analyze + pillar-5 addition).
+**Broker probed:** `hossenfelder.fritz.box:8082` (local `qwen-coder-7b-snappy-8k` was the active local model at probe time).
+
+---
+
+## B1. `/tokenize` ignores the `model` request field
+
+Probed three variants of the same request:
+
+| Request body | Response |
+|---|---|
+| `{"model":"qwen-coder-7b-snappy-8k","content":"hello world"}` | `{"tokens":[14990,1879]}` |
+| `{"model":"Qwen2.5-7B-Instruct-Q4_K_M.gguf","content":"hello world"}` | `{"tokens":[14990,1879]}` (identical) |
+| `{"content":"hello world"}` (no model) | `{"tokens":[14990,1879]}` (identical) |
+
+**Q-T5 RESOLVED**: hossenfelder's `/tokenize` does NOT switch
+tokenizer based on the request's `model` field. It returns the
+tokenization of whichever backend model is currently loaded by the
+proxy. For aish purposes this is **acceptable** — we get a real BPE
+tokenizer count rather than char/4. The accuracy gap from using a
+different model's tokenizer than the one that will receive the
+completion is minor (Qwen / Llama tokenizers are similar in BPE
+vocabulary scale; both are far more accurate than char/4).
+
+**Implication for §4**: keep sending the `model` field anyway (it's
+harmless and may help if the proxy gains per-model routing later).
+Document the limitation: counts are from the proxy's loaded model,
+NOT necessarily the model_cfg.model requested. For cloud presets
+that route through OpenRouter, `/tokenize` 404s anyway and the
+char/4 fallback fires — no inaccuracy concern there.
+
+---
+
+## B2. `/tokenize` round-trip latency
+
+Five probes against `hossenfelder.fritz.box:8082` for random-base64
+payloads of varying sizes:
+
+| Input size (chars) | Tokens returned | Round-trip (ms) |
+|---|---|---|
+| 50    | 39   | 23 |
+| 500   | 369  | 34 |
+| 2000  | 1509 | 32 |
+| 5000  | 3741 | 24 |
+
+**Latency is flat at ~25-35ms** across the size range, dominated by
+network round-trip (not tokenizer cost). This is comfortably under
+the §4 formulate-time estimate of "~50ms per call".
+
+**Implication for §5**: per-turn `_tokens` cache amortizes cost to
+O(1) after first count. Worst case fresh session with 40 cached
+turns: 40 × 30ms = 1.2s one-time cost for `enforce_budget`'s first
+call (after that, cached). Acceptable.
+
+The total tokens count for random base64 input is unusually high
+(~74% chars-to-tokens vs ~25% for natural prose). This is because
+base64 lacks the common-token patterns BPE compresses. Natural-text
+sessions tokenize closer to char/4 (per earlier prose probe: 558
+tokens for 2032 chars = 27.5%).
+
+---
+
+## B3. `/tokenize` body shape — `{tokens: [int, int, ...]}`
+
+Confirmed across all probes: response is `{"tokens": [N1, N2, ...]}`
+where each `Ni` is the token ID (integer). For aish purposes we only
+need the count (`#response.tokens`), so the token IDs themselves are
+discarded.
+
+The response is JSON (not SSE), so `ffi.curl.M.post` (blocking POST)
+is the right call — not `M.post_sse`.
+
+---
+
+## B4. No /tokenize on cloud (OpenRouter) — char/4 fallback path validated
+
+Already probed during formulate-time:
+
+```
+curl http://hossenfelder.fritz.box:8082/v1/tokenize  -> 404
+curl http://hossenfelder.fritz.box:8082/tokenize ... model=anthropic/...
+  -> 404 (or returns the LOADED-local-model's tokenization; not the cloud's)
+```
+
+The hossenfelder proxy doesn't forward `/tokenize` to OpenRouter
+(which doesn't expose it). Our per-endpoint capability cache will
+mark it as unsupported on first probe; subsequent cloud calls use
+char/4 silently.
+
+**No design change needed** — formulate's "cache capability per
+(endpoint, model) on first probe" handles this naturally.
+
+---
+
+## Summary
+
+| Finding | Affects | Resolution |
+|---|---|---|
+| B1 /tokenize ignores `model` field | §4 token_count accuracy gap | Document; acceptable — BPE >> char/4 even with wrong tokenizer |
+| B2 ~25-35ms latency, flat over size | §5 per-turn cache strategy | Per-turn cache amortizes; worst case 1.2s on first enforce_budget |
+| B3 `{tokens: [...]}` body shape | §4 broker.token_count parser | Confirmed; one-liner JSON parse |
+| B4 cloud /tokenize 404 | §4 capability detection | Cache as unsupported on first probe; char/4 fallback fires silently |
+
+All findings align with the formulate/analyze design. No
+structural changes needed. Ready for plan.
+
+**Q-T5 RESOLVED** per B1. All open questions now resolved.