Models

Which model, and what it costs you.

One key, OpenAI or Gemini, is a complete setup. This page is the reference behind that sentence: the two profiles benchmarked on real postings, what a tailored application actually costs, and where local models stand.

Choose your models

Two setups, measured on real postings.

Any OpenAI-compatible model can be configured. These two are benchmarked end to end with every call traced, one per API key, so you can start from a known-good setup instead of guessing. The Fast tier decides almost everything: how much of a posting gets extracted, how honest your base score is, and most of the waiting.

OpenAI

depth

about a penny

per application

Every tier set to

gpt-5.6-luna

The one key you need

One OpenAI key

JD requirements captured

The most complete extraction we measured

Capture + tailor feels like

~40 seconds

Hallucinated skills

none measured

Gemini

speed

under 3¢

Gemini promo pricing doubles Jan 2027

Every tier set to

gemini-3.7-flash

The one key you need

One Gemini key

JD requirements captured

About ¾ of that, strongest on named tools

Capture + tailor feels like

~10 seconds

Hallucinated skills

none measured

A fresh install ships with the OpenAI one already set, because honest scoring starts at extraction: a fast model that misses requirements inflates your fit score, in our tests by about nine points. Neither is a tier above the other, and switching is three dropdowns in Settings → Models. Mixing tiers across providers works too. These two already cover what it would buy you.

What it costs

A tailored application costs about a penny.

We traced real applications end to end with Langfuse in August 2026, on the model profile a fresh install ships with, priced at list. Your payloads will vary. Not by an order of magnitude.

≈1¢

per tailored application

Capture the posting, score it, close the gaps, tailor, render the PDF, and write the cover letter and screening answers. All of it.

My whole search so far has cost under $2 in tokens. Hosted tools charge $15–75 a month.

Operation	Input tokens	Output tokens	Cost
JD extraction	~3k	~1.5k	~¼¢
Gap enrichment	~7k	~3k	~½¢
Tailoring pass	~11k	~0.6k	~¼¢
Cover letter + screening answers	~8k	~0.9k	~¼¢
Career KB consolidation, per resume (one-time)	~7k	~1.5k	~⅓¢
Capture → tailored resume → full apply package			≈1.3¢

Priced against GPT-5.6 Luna at $0.20 per million input tokens and $1.20 per million output. Multiply it out yourself. That is the point of showing the split.

Running your own

Local model servers: configurable, untested

Configurable, not yet validated

The client speaks to any OpenAI-compatible endpoint, so Ollama, LM Studio or vLLM can be pointed at with OPENAI_BASE_URL. We haven't validated a local model end to end yet, and the heavy asks are exactly where small models struggle: long tailoring prompts, strict JSON, streaming tool calls. So we make no offline promise until we can stand behind one. If you try it, tell us what worked and what broke, with the model name. That's a genuinely useful contribution.

Career tooling should be infrastructure, not a rental.

Clone it, run it, keep everything it produces. Nothing here was built to make leaving hard.

Run it locally Read the source

Apache-2.0 · no account · no subscription · your data stays on your disk