Subwave Llm Bench

SUB/WAVE LLM model benchmark

Two harnesses, one job: measure how well a provider + model handles the station's real LLM calls before trusting it on air.

• llm-bench (controller/scripts/llm-bench/, npm run llm-bench) — the primary harness. A matrix runner over every on-air call kind: pool picks (pickNextTrack), agent picks (djAgentPick), pool + agent segments (generateSegment / djAgentSegment), request matching (matchRequest / djAgentRequest), the free-text scripts (intro, link, station ID, hourly), banter, and the programme family (plan, beats, exchanges). Scores reliability + deterministic rule checks, prints a per-model comparison table, writes a diffable JSON report.
• picker-test.mjs — the legacy picker-only deep-dive. Still useful for high-iteration picker runs comparable with historical results, and it has a bundled orchestration script (see the legacy section at the end).

Both import the live prompts and schemas from src/ (never copies), fake everything external (library, tools, weather/news data), and override provider/model only inside their own short-lived process — the live controller's configured model is never touched.

The two things that surprise people

1. Routing matters as much as the model. The same model can pass through one provider and fail through another, because each @ai-sdk/* provider translates tools / structured output differently. Canonical case: deepseek-v4-flash scored 0/4 via the deepseek direct provider but 4/4 via openrouter. Always benchmark through the routing you'll actually deploy — "is model X good?" is the wrong question; "is provider+X good?" is the right one.
2. The stress scenarios are where truth lives. Easy cells make every model look fine. The verdict cells are: djAgentPick/long-context (full prompt + tool loop), generateSegment/dull-weather (the model must decline to air — small models botch the silence encoding; a huge wall-clock here means the structured-output retry rescued a failed first attempt), and the multi-voice JSON kinds (generateBanter, generateProgrammeExchange).

When NOT to use this

• Changing the live model — that's an admin Settings change, not this skill.
• Diagnosing general runtime behaviour — subwave-log-analysis.
• Starting/stopping the stack — subwave-control.

Running llm-bench

Runs on the host from a repo clone (it imports TS source via tsx; prod containers ship only compiled dist/). The running stack is not required — keys resolve from state/secrets.env and controller/.env automatically (existing env vars win). If a key exists only inside a prod container's env, copy it out first: export OPENROUTER_API_KEY=$(docker exec sub-wave-controller printenv OPENROUTER_API_KEY).

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cd <repo>/controller
npm run llm-bench -- \
  --models openrouter:google/gemma-4-31b-it,ollama:qwen3:8b \
  --iterations 3

Flags:

• --models (required) — comma list of provider:model, split on the FIRST colon, so Ollama tags keep theirs (ollama:qwen3:8b). Providers: ollama | openai-compatible | anthropic | openai | google | deepseek | openrouter | requesty | gateway.
• --kinds — groups (pick,segment,request,scripts,banter,programme) or exact kind names. Default: all.
• --modes — pool,agent (default both). Mode-independent kinds run once either way. If the operator runs pool mode live (Agentic picker off), --modes pool is the honest benchmark — agent cells they'll never use just add noise and cost.
• --iterations — per scenario. 1 to smoke, 3 to screen (default), 5+ to confirm a winner. Full matrix ≈ 32 scenario cells, so ~100 calls per model at 3 iterations.
• --out — report path (default scripts/llm-bench/reports/<ts>.json, gitignored).

A full run takes many minutes (every call is a real model call, agent cells can take 45 s each). Run it in the background with a long timeout and don't poll; OLLAMA_URL=http://localhost:11434 translates the container-internal Ollama address for host runs.

Reading llm-bench results

Each run is ok, violation (call succeeded, named rule checks failed), or thrown (bucketed: no-object-generated, timeout, unreachable, thrown). The summary prints pass% + p50 latency per cell, then per-model histograms of rule failures and thrown buckets.

Judge in this order:

1. Thrown rate — a model that can't produce the schema at all is out. no-object-generated on structured kinds = weak structured output; everything landing at ~45 s = too slow for the loop, not incapable. unreachable = provider/config problem, not the model — fix and re-run.
2. Violations — the named rules tell you the failure character: hallucinated-id (invents track ids — dangerous), banned-phrase:* / stage-direction:* / wrapping-quotes (would be read aloud by TTS), digits-in-spoken-time / clock-leak (hourly/link discipline), opener-repeat (ignores anti-repeat lists), unusable-exchange / single-voice (can't hold multi-voice JSON), variety:same-artist (editorial-pressure miss — informative, not disqualifying).
3. Latency — p50 per cell; a pick has a whole track of slack so even 20 s works, but segments and links air sooner. Watch for retry-rescued cells (pass with 100×-median wall clock).
4. Pool vs agent gap — a model that's clean in pool cells but falls apart in agent cells is a "candidate pool" model; that's a valid deployment, just say so in the recommendation.

Reports are JSON with every run record — keep old ones to diff a model across prompt changes or across model updates.

Producing the recommendation

1. A compact table: one row per provider+model, columns for thrown rate, violation rate, p50 latency, and the dominant failure.
2. A clear call naming the best provider + model + mode ("gemma via OpenRouter, pool mode — clean structured output, agent cells time out").
3. Call out disqualifiers and deferrals (5xx throughout = re-test later).

Do not apply the change. The operator sets llm.provider + llm.model (and the Agentic picker toggle) in admin Settings — those are global, their call.

Legacy: picker-test.mjs deep-dive

For picker-only, high-iteration runs comparable with historical results:

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# DEV stack (src bind-mounted in container):
docker exec sub-wave-controller npx tsx scripts/picker-test.mjs <provider> <model> [iterations] [short|long]
# Host (works regardless of stack):
( cd <repo>/controller && STATE_DIR=<repo>/state OLLAMA_URL=http://localhost:11434 \
  npx tsx scripts/picker-test.mjs <provider> <model> [iterations] [short|long] )

long is the verdict mode (realistic session window); short is a sanity check. The bundled scripts/assess-models.sh (in this skill's directory) runs one or more models in both modes, auto-detects dev/prod, and summarises event- log failures: assess-models.sh <provider> [iterations] <model>... — with ollama and no models it discovers and tests everything installed.

Failure strings glossary (from the event log, kind: pickerTest):

• agent did not call the done tool before stopping — model ignored the tool protocol. Check routing before rejecting the model (see surprise #1).
• Every failure at ~agentTimeoutMs (45000) with tools=0 — a latency failure, not incapability; the deadline aborted it mid-loop.
• Failed after N attempts … Internal Server Error — provider outage; re-run later, don't reject the model on this.
• hallucinated-id / no-object-generated — weak structured output.

Cross-check live behaviour in /admin/debug → llm.recentCalls: the via field shows which path produced each result (ai-sdk:agent:native = clean native path; …:recovery = rescued by the done-tool recovery; a thrown djAgentPick falls back to the pool picker, so the station never goes silent).

Notes

• Both harnesses append telemetry rows to the controller event log (state/logs/events-*.jsonl) and, if LLM_DEBUG_RAW is on, raw request bodies to state/logs/llm-debug.log. Harmless, filterable.
• The docs page mapping every call kind to its code lives at docs/llm-calls.md; the harness design spec at docs/superpowers/specs/2026-07-09-llm-bench-design.md.