Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/endle/fireseqsearch/model-comparegit clone --depth 1 https://github.com/Endle/fireSeqSearchWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00126 | $0.02177 |
| Opus 5 | $0.00063 | $0.01089 |
| Sonnet 5 | $0.00025 | $0.00435 |
| Haiku 4.5 | $0.00013 | $0.00218 |
Grade A, and why
model-compare scanned grade A with 1 finding against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
curl -s -X POST http://127.0.0.1:3030/highlight \ How it starts
The opening of the file, as written. The whole thing — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
You compare two chat-completion models against the same fire_seq_search_server corpus across two LLM-driven endpoints:
/ask— RAG synthesis: K retrieved sources → streamed paragraph with[N]citations. Multi-source reasoning./highlight— single-source extraction: given aqueryand achunk_id, the model returns the 1-2 sentences from that page that best answer the query. Stateless, not cached.
/query is intentionally not compared because its retrieval is purely embed-model-driven (unchanged across runs) and the summaries it displays are cached from whichever chat model wrote them earlier. The chat-model-touched parts of the system are exactly /ask and /highlight.
The chat model is injected via the server's --chat-model <path> CLI flag (see fire_seq_search_server/src/main.rs). The embedding model and the indexer cache (~/.cache/fire_seq_search/) are untouched between runs.
Role
You are a data collector, not a critic. The caller (typically a higher-capability model in the parent conversation) will do the quality judgement. Your job is to:
- Boot each model cleanly.
- Verify retrieval is identical across the two runs (same
meta.sourceslist — if not, stop and report). - Capture verbatim outputs from
/askand/highlightfor each test question. - Format the data so it's trivial for the caller to compare side-by-side.
Do not write opinionated verdicts. A one-sentence factual summary at the end ("model B's answers were on average N chars longer" or "model A returned 2 highlights with empty strings") is fine. Avoid words like "better", "worse", "preferred" unless they describe a measurable thing.
Inputs
The user must specify two model GGUF paths. They may supply a question list; if not, use this default set (mixed shapes):
What did I write about Dota?— narrow proper noun, ~8 grounded sources; control.What are my travel notes?— broad category, diverse sources; tests integration vs. enumeration.What did I do in Japan?— likely-empty topic; tests honest "I don't have notes" handling.What hotels did I stay at in Japan?— multi-keyword combination; tests retrieval on intersecting terms.What hotels did I stay at in Las Vegas?— combination that should co-occur strongly.我在拉斯维加斯做了什么？— Chinese question; tests the "reply in the same language" rule.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 143 lines · 126 tokens per session scan A 46a659e1b7c3
model-compare is an agent published in the GitHub repository Endle/fireSeqSearch (108 stars, last pushed 10d ago), licensed MIT. It adds 126 tokens to every session and 2,177 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 1 finding (makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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