v0.2.1

frugon v0.2.1

A much bigger recommendation roster, better quality coverage for reasoning models, a demo that runs on the exact same roster a real analysis uses, and freshly synced data tables.

Added

• A 23-model recommendation roster across 11 vendors. The default candidate pool grew from 10 to 23 current top models spanning OpenAI, Anthropic, Google, DeepSeek, Moonshot, xAI, Mistral, Z.ai, MiniMax, Alibaba, and Meta (open-source Llama) — every one both priced and quality-rated — so recommendations on your real logs draw from a much wider, more current field. The two open-source Llama 4 checkpoints, which have no single first-party price, are priced via Groq as a labeled reference host rather than an invented number.
• See what else was considered. A default (no --candidates) run now shows a "Candidates considered" block alongside the headline recommendation — the recommended model plus the next four cheapest candidates that also beat your current spend, each one with its own projected monthly cost, saving%, and quality tier. Previously this transparency only appeared when you passed --candidates yourself; now it's there by default, on the demo too, with a line telling you how many models were in the full pool and how to compare specific ones with --candidates. When two or more candidates save the same amount to the decimal place shown, the recommendation goes to whichever has the higher quality tier — the Quality tier column makes that call visible, not just asserted.

Improved

• Better quality coverage for reasoning models. frugon now recognises that LMArena often rates a reasoning model under an effort-tagged or dated name (gpt-5-high, grok-4-0709) while your logs carry the bare name (gpt-5, grok-4) — and honestly attributes the rating to the base model, since reasoning effort changes how many tokens a call spends thinking, not the per-token rate. More of the models in your logs come back rated instead of unrated. Pricing is never folded this way — a thinking variant keeps its own price.
• The demo is no longer special-cased. frugon analyze --demo used to route against a small, fixed illustrative candidate set — a stand-in that could drift from what the tool actually recommends on your own logs. It now uses the SAME default 23-model roster a real run does, so the demo shows exactly what you'd get. The one narrower exception: --demo --measure still samples a single pinned model so the try-out path only needs an OPENAI_API_KEY — that pin never touches the recommendation itself.
• The demo's recommendation moved. With the un-pinned roster and the refreshed data below, the demo's headline recommendation now routes easy calls to deepseek-v4-flash (it previously routed to a fixed illustrative pick) — an honest side effect of no longer special-casing the demo.
• Fresh pricing and quality data. The bundled pricing table and LMArena quality tiers are re-synced to their public sources as of this release, the same weekly process frugon update runs for you.
• Honest disclosure copy. The post-recommendation note no longer claims a "fixed demo candidate set" — it says plainly that --demo runs on bundled sample data, full stop.
• --measure/--judge now recognise every vendor in the 23-model roster. DeepSeek, xAI (Grok), Moonshot (Kimi), Z.ai (GLM), MiniMax, and Alibaba (Qwen) models — plus the two reference-host Llama 4 checkpoints — now get the correct provider-key prompt and route correctly to their provider, instead of a generic "bad request" the first time you tried to measure one of them.

Install / upgrade

uvx frugon@latest          # or:  uv tool upgrade frugon  /  pipx upgrade frugon  /  pip install -U frugon

Full Changelog: v0.2.0...v0.2.1