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AI Model Radar

The rules

How every number is made

Every figure on this site is one of three things: a measured reading with a collection timestamp, a sourced vendor fact with a citation, or a declared assumption you can see and change. When none of the three exists, we show the reason there is no number — a wrong price is worse than no price.

What is measured, and how often

Four collectors run on our servers — no browser is involved, and they run whether or not anyone visits:

The three badges

● Collected marks a measured reading and names its age; the exact instant sits in the markup, so the claim can be checked. A section's freshness is always its oldest contributing source — a fresh price run is not allowed to vouch for old download numbers.

◇ Estimated marks a figure computed from our curated catalog by a deterministic engine — real inputs, real arithmetic, but not a live reading.

◈ Demo data marks placeholder values that exist so the interface can be developed offline. You should not see this badge in production; if you do, a data source is down and the page is telling you so rather than pretending.

The Heat Score

Heat answers one question — how much momentum and relevance does this model have right now — and it refuses to pretend it is objective truth. It is a weighted average of five components, every one a measured signal expressed as the model's percentile among all models with a full week of history:

35% trending level (Hugging Face, today)
30% 7-day download growth (Hugging Face)
15% 7-day trending change (our own daily history)
15% 30-day downloads (Hugging Face)
5% Hub likes (Hugging Face)

heat = 100 × Σ(weight × percentile) / Σ(weight of available components)

Percentiles, not raw counts, so a four-digit trending score and a nine-digit download counter can share one scale without magic constants. When a component cannot be measured for a model, its weight is renormalized across the rest — and the coverage is published as the confidence figure next to every score. Absence lowers certainty; it never silently lowers the score.

The rules around the number: a model gets no score at all until its history spans seven days (“ collecting” is not “cold”); download momentum only counts on a baseline that is itself about a week old and at least 10,000 downloads deep, so a tiny new repo cannot fake a breakout; the direction arrow needs a move beyond ±5% of the trending baseline (downloads only ever vote “up” — a falling 30-day window usually means an old spike aging out, so only trending may vote “down”). BREAKOUT marks a model in the top 5% of download growth with confidence ≥ 65%; COOLING marks a previously relevant model whose trending score fell by a quarter or more over the week.

Two components of the original design are deliberately absent: cross-provider usage momentum (no public source exists — the one we wanted is login-gated) and benchmark quality (we refuse benchmarks entirely). The board keeps showing every raw signal beside the composite, and the score is recomputable from the stored daily history alone — there is no hidden state to trust.

The local-fit estimate

Whether a model runs on your machine is arithmetic, not opinion:

estimated memory = measured weights + computed context cache + runtime overhead
usable memory = your memory − safety margin

The weights figure is the byte size of a real, published GGUF file — never derived from a parameter count. The context cache is computed from the model's own config.json: layer by layer, respecting hybrid attention (many modern models cache full context on only a fraction of their layers), sliding windows, and latent attention (which stores one compressed vector instead of separate keys and values). The safety margin reserves 12% of memory for the display, the OS and allocator overhead — and on Apple Silicon we plan with only 70% of unified memory, because macOS caps what the GPU may take.

When a model's attention shape is unpublished or one we cannot model, the site says UNKNOWN instead of guessing. Fit labels are working comfort levels, not benchmarks: we publish no quality scores and no speed claims.

The cost comparison

The calculator prices the same model three ways. Cloud uses the measured per-token prices; your own machine uses a sourced vendor list price (editable — what you actually pay wins) plus electricity from the vendor's power figure and your rate; rental uses the cheapest measured GPU class that actually fits the model. Usage volume, active hours, the input/output token split and the amortization window are assumptions, listed next to the result, every one changeable. A configuration that cannot exist — a model too big for the card — gets a reason, never a price.

What we refuse to publish

Independence

Outbound links to providers route through an internal redirect and currently carry no referral codes; if that ever changes, the legal notice will disclose it first. Referral revenue never influences a ranking, a recommendation or a fit verdict — all three are computed from the measured data by deterministic code, and this page is the standing promise that it stays that way.

Corrections

Found a number that looks wrong? The contact address is in the legal notice. A correction that survives checking updates the data — and, where the mistake was systematic, the rule that produced it.