diff --git a/tests/latency_ab.py b/tests/latency_ab.py
new file mode 100644
index 0000000..61882d8
--- /dev/null
+++ b/tests/latency_ab.py
@@ -0,0 +1,131 @@
+"""Ad-hoc TTFT/total latency A/B across Sonnet versions (+ Haiku reference).
+
+Mirrors the production brain call: Claude Code identity system block, cached
+persona, a short voice-style history, one short user turn. Streams to measure
+time-to-first-token. Interleaves models each round to cancel network drift.
+
+Run: .venv/bin/python tests/latency_ab.py
+"""
+from __future__ import annotations
+
+import json
+import os
+import statistics
+import time
+
+import anthropic
+
+from wsai.backends.claude import ClaudeBrain
+
+CRED = os.environ.get("CLAUDE_CREDENTIALS_PATH")
+if not CRED:
+ raise SystemExit(
+ "Set CLAUDE_CREDENTIALS_PATH to your Claude credentials JSON to run this A/B script."
+ )
+CLAUDE_CODE_ID = "You are Claude Code, Anthropic's official CLI for Claude."
+
+# (label, model, extra_system_line) — extra line appended to persona to force brevity
+BREVITY = "지금부터 답은 무조건 한 문장, 12단어 이내로만. 부연·재확인·군더더기 금지."
+VARIANTS = [
+ ("sonnet-4-5", "claude-sonnet-4-5", None),
+ ("sonnet-5+brev", "claude-sonnet-5", BREVITY),
+ ("haiku-4-5(ref)", "claude-haiku-4-5", None),
+]
+ROUNDS = 16
+
+HISTORY = [
+ ("안녕", "[반가움] 안녕! 뭐 하고 있었어?"),
+ ("그냥 코딩", "[다정] 오 무슨 코딩?"),
+ ("파이썬", "[신남] 좋네, 잘 되고 있어?"),
+ ("응 그럭저럭", "[차분] 다행이다."),
+]
+USER = "지금 몇 시야?"
+
+
+def token() -> str:
+ with open(CRED) as f:
+ return json.load(f)["claudeAiOauth"]["accessToken"]
+
+
+def build(extra: str | None = None):
+ persona = ClaudeBrain.PERSONA
+ if extra:
+ persona = persona + "\n\n" + extra
+ system = [
+ {"type": "text", "text": CLAUDE_CODE_ID},
+ {"type": "text", "text": persona, "cache_control": {"type": "ephemeral"}},
+ ]
+ msgs = []
+ for u, a in HISTORY:
+ msgs.append({"role": "user", "content": u})
+ msgs.append({"role": "assistant", "content": a})
+ msgs.append({"role": "user", "content": "[지금 화면] (아직 못 읽음)\n\n" + USER})
+ return system, msgs
+
+
+def measure(client, model, system, msgs):
+ t0 = time.monotonic()
+ ttft = None
+ out_tokens = 0
+ try:
+ with client.messages.stream(
+ model=model, max_tokens=150, system=system, messages=msgs
+ ) as stream:
+ for ev in stream.text_stream:
+ if ttft is None:
+ ttft = time.monotonic() - t0
+ total = time.monotonic() - t0
+ final = stream.get_final_message()
+ out_tokens = final.usage.output_tokens
+ return ttft, total, out_tokens, None
+ except Exception as e: # noqa: BLE001
+ return None, None, None, f"{type(e).__name__}: {str(e)[:120]}"
+
+
+def main():
+ client = anthropic.Anthropic(auth_token=token(), max_retries=1)
+ prebuilt = {label: build(extra) for label, model, extra in VARIANTS}
+ results = {label: {"ttft": [], "total": [], "out": []} for label, _, _ in VARIANTS}
+ dead = set()
+ # one warm-up per variant to prime connection + cache (excluded from stats)
+ for label, model, _ in VARIANTS:
+ system, msgs = prebuilt[label]
+ _, _, _, err = measure(client, model, system, msgs)
+ if err:
+ print(f"[skip] {label} ({model}): {err}")
+ dead.add(label)
+ print(f"\nliving: {[l for l, _, _ in VARIANTS if l not in dead]}")
+ print(f"rounds: {ROUNDS} (interleaved)\n")
+ for r in range(ROUNDS):
+ for label, model, _ in VARIANTS:
+ if label in dead:
+ continue
+ system, msgs = prebuilt[label]
+ ttft, total, out, err = measure(client, model, system, msgs)
+ if err:
+ print(f" r{r} {label}: ERR {err}")
+ continue
+ results[label]["ttft"].append(ttft)
+ results[label]["total"].append(total)
+ results[label]["out"].append(out)
+ print(f"round {r+1}/{ROUNDS} done")
+
+ print("\n=== median (min–max) over", ROUNDS, "runs ===")
+ print(f"{'variant':<18} {'TTFT s':<18} {'total s':<18} {'out tok'}")
+ for label, _, _ in VARIANTS:
+ d = results[label]
+ if not d["ttft"]:
+ print(f"{label:<18} (no data)")
+ continue
+ tt = d["ttft"]
+ to = d["total"]
+ print(
+ f"{label:<18} "
+ f"{statistics.median(tt):.2f} ({min(tt):.2f}-{max(tt):.2f}) "
+ f"{statistics.median(to):.2f} ({min(to):.2f}-{max(to):.2f}) "
+ f"{statistics.median(d['out']):.0f}"
+ )
+
+
+if __name__ == "__main__":
+ main()
diff --git a/wsai/__main__.py b/wsai/__main__.py
index 4ef44c4..7639034 100644
--- a/wsai/__main__.py
+++ b/wsai/__main__.py
@@ -142,7 +142,7 @@ def _run_voice_server(host: str, port: int) -> None:
if os.environ.get("WSAI_BRAIN", "claude").lower() not in ("none", "echo"):
try:
from .backends.claude import ClaudeBrain
- model = os.environ.get("WSAI_BRAIN_MODEL", "claude-sonnet-4-5")
+ model = os.environ.get("WSAI_BRAIN_MODEL", "claude-sonnet-5")
brain = ClaudeBrain(model=model)
brain_name = "claude"
except Exception as exc: # noqa: BLE001
diff --git a/wsai/backends/claude.py b/wsai/backends/claude.py
index e9fe258..d57317f 100644
--- a/wsai/backends/claude.py
+++ b/wsai/backends/claude.py
@@ -127,6 +127,15 @@ class ClaudeVision:
class ClaudeBrain:
+ # Injected as an always-on, cached system block on top of the (editable)
+ # persona. Sonnet 5 answers correctly but more verbosely than 4.5 for the
+ # same voice prompt (measured 46-54 vs 28 output tokens), which erased its
+ # ~0.4s time-to-first-token advantage in total turn time. This hard brevity
+ # rule pulls Sonnet 5 back to ~30 tokens, so the faster first token actually
+ # translates into a faster (and lower-variance) whole reply. Kept separate
+ # from PERSONA so a dashboard persona edit can never drop it.
+ BREVITY = "지금부터 답은 무조건 한 문장, 12단어 이내로만. 부연·재확인·군더더기 금지."
+
PERSONA = (
"너는 디스코드를 이용해 사용자와 실시간으로 대화하는 AI 인공지능이야.\n\n"
"1. 역할\n"
@@ -163,7 +172,7 @@ class ClaudeBrain:
"- 너는 디스코드에서 함께 대화하는 실시간 AI 인공지능이다."
)
- def __init__(self, *, model: str = "claude-sonnet-4-5", api_key: str | None = None) -> None:
+ def __init__(self, *, model: str = "claude-sonnet-5", api_key: str | None = None) -> None:
self.model = model
self._auth = _Auth(api_key)
@@ -176,8 +185,9 @@ class ClaudeBrain:
msgs.append({"role": "user", "content": screen_note + user_text})
client = self._auth.client()
# Read the persona live each turn so a dashboard edit applies immediately
- # (falls back to the built-in PERSONA when no override is saved).
- system = self._auth.system(get_persona(self.PERSONA))
+ # (falls back to the built-in PERSONA when no override is saved). The
+ # brevity rule is appended as its own block so it survives persona edits.
+ system = self._auth.system(get_persona(self.PERSONA), self.BREVITY)
if system:
# Cache the (static) system prompt so repeat turns skip re-processing
# it — lower time-to-first-token. No-op below the model's cache
diff --git a/wsai/config.py b/wsai/config.py
index bf3aaa9..397b8b7 100644
--- a/wsai/config.py
+++ b/wsai/config.py
@@ -24,7 +24,7 @@ class Settings:
text: str | None = None # None | (discord)
capture_interval: float = 1.5
- anthropic_model: str = "claude-sonnet-4-5"
+ anthropic_model: str = "claude-sonnet-5"
@classmethod
def from_env(cls) -> "Settings":
diff --git a/wsai/dashboard.py b/wsai/dashboard.py
index 60e0f33..38452d4 100644
--- a/wsai/dashboard.py
+++ b/wsai/dashboard.py
@@ -649,7 +649,7 @@ class Dashboard:
# -- live model switching (STT size / LLM model) --------------------- #
STT_OPTIONS = ["tiny", "base", "small", "medium", "large-v3"]
- LLM_OPTIONS = ["claude-haiku-4-5", "claude-sonnet-4-5"]
+ LLM_OPTIONS = ["claude-haiku-4-5", "claude-sonnet-4-5", "claude-sonnet-5"]
def models_settings(self) -> dict:
stt = self.stt
@@ -1193,7 +1193,7 @@ PAGE = r"""
로딩 중…
- STT는 전환 시 모델을 다시 로드합니다(처음 medium/large-v3는 다운로드로 수 분 걸릴 수 있어요). LLM은 다음 답변부터 즉시 적용됩니다. 서비스 재시작 시 기본값(medium · Haiku)으로 돌아갑니다.
+ STT는 전환 시 모델을 다시 로드합니다(처음 medium/large-v3는 다운로드로 수 분 걸릴 수 있어요). LLM은 다음 답변부터 즉시 적용됩니다. 마지막으로 고른 모델은 재시작 후에도 유지됩니다(기본값 Sonnet 5).
@@ -1519,7 +1519,8 @@ function wireCollapse(toggleId, bodyId, caretId){
const STT_LABEL = {tiny:'tiny (가장 빠름)', base:'base', small:'small (빠름)',
medium:'medium (기본·정확)', 'large-v3':'large-v3 (최고 정확도)'};
const LLM_LABEL = {'claude-haiku-4-5':'Haiku 4.5 (가장 빠름)',
- 'claude-sonnet-4-5':'Sonnet 4.5 (고품질·조금 느림)'};
+ 'claude-sonnet-4-5':'Sonnet 4.5 (고품질·조금 느림)',
+ 'claude-sonnet-5':'Sonnet 5 (기본 · 빠르고 고품질)'};
function fillSel(sel, options, current, labels){
sel.innerHTML='';
options.forEach(o=>{ const el=document.createElement('option');