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InferenceHunt / Methodology

How InferenceHunt compares AI model prices

InferenceHunt compares the latest successful public price observations for the same AI model across tracked venues. Prices are shown in US dollars per 1 million tokens. Missing prices are never estimated.

Current coverage

InferenceHunt currently has pricing data for 27 models across 8 venues, with the latest successful observation collected 2026-08-23T12:20:55.179Z.

Primary sources

Prices are collected from public venue APIs and retained as source-linked observations.

Comparison rules

  1. STEP 1

    Match the exact model

    Venue listings are mapped to one canonical model. Similar model families or variants are not combined.

  2. STEP 2

    Keep offers separate

    Each venue and processing mode remains a distinct offer. Cached-token prices are not substituted for standard input prices.

  3. STEP 3

    Compare like-for-like units

    Input and output prices are normalized to USD per 1 million tokens before comparison.

  4. STEP 4

    Use the latest successful observation

    Current means collected within 24 hours. Older data is labeled stale and unavailable data is shown as N/A.

Pricing questions

What do input and output prices mean?

Input price is the cost of tokens sent to a model. Output price is the cost of tokens generated by the model. InferenceHunt displays both in US dollars per 1 million tokens.

How does InferenceHunt calculate the cheapest price?

InferenceHunt compares the latest successful price observation for the same canonical model at each tracked venue. The lowest input and output prices are selected independently, so they can come from different venues.

How current is the pricing data?

A successful observation collected within the past 24 hours is current. Older observations are labeled stale, and missing values are shown as N/A rather than estimated.

Does free mean permanently free?

No. Free means the latest observed input and output prices are both zero. Promotions and venue pricing can change, so the observation time and source should be checked before use.

Why can the same model have different prices across venues?

Venues can use different providers, routing, processing modes, promotions, and margins. InferenceHunt keeps each observed offer separate so those differences remain visible.