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Free AI Visibility AuditGemini Pro Latest is a Google alias that always points at the newest model in the Gemini Pro family, so calls made against it move forward automatically as Google ships updates.
GoogleVerified 24/09/2026Released 27/04/202601 / decision
Each figure sits next to the middle of the field, so it can be read as dear or cheap, wide or narrow, rather than floating on its own. Compared against the 231 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$2.00
field median $0.43
Output / 1M tokens
$12.00
field median $1.80
Context
1,049K
field median 500K
02 / overview
Four questions, answered separately, because somebody arrives at one of them rather than at the top of the page.
The job it was built for, and the job it is not for.
It is a rolling pointer rather than a fixed model, aimed at teams who want the current Gemini Pro without editing a model string each time one lands. It accepts text, images, audio, video and files, so it suits mixed-input work across documents and media. It is not the right choice where you need a pinned, reproducible model version for audits, regression tests or compliance records.
Where it sits in its line, and when a sibling is the better pick.
Gemini Pro Latest was first seen on 27 April 2026. It sits above the smaller, cheaper tiers in the Gemini line and below nothing, it simply resolves to whatever Google currently calls the leading Pro model. Pick it when you want continuous access to the newest Pro capability, and avoid it when a change in behaviour underneath you would break something downstream.
The API, the apps it powers, and what its limits let you do.
You reach it through the Gemini API as a model identifier, the same way you would call any pinned Gemini model, and the alias resolves server side. Its very large context window means whole document sets, long transcripts or extended video and audio can go in a single request, and the generous output ceiling covers long reports, full drafts and structured extractions without chunking. The multimodal input range means one call can mix a brief, a spreadsheet, a screenshot and a recording.
What changes because this exists, or why it does not.
The practical change is operational rather than technical. Teams running long-lived integrations no longer need a release-watching routine and a deploy every time Google updates the Pro tier, the alias absorbs that. The trade is control, you gain currency and lose the ability to say exactly which weights answered a given request.
03 / evidence
Split by category, so a strong number never hides a thin evidence base. Verified means we read it on the benchmark's own published results; a provider's claim about its own model is shown and labelled rather than dropped.
No published benchmark scores for this model yet.
We publish a score only where we can link the result to where it was published. Until a benchmark result for this model exists in a source we read, this section stays empty rather than being filled with a provider's marketing figure.
How we decide what counts as evidence04 / ledger
Every published row, grouped by category, each compared with the best published score on the same benchmark. 'Is 64% good' is a question nobody can answer; '26 points behind the leader' is one anybody can.
Nothing in the ledger yet.
Each row here carries a score, the benchmark it came from, what the leading model scored on the same test, and a link to the published result. Rows appear as results are published and read.
How we decide what counts as evidence05 / capability
Where this model is strong, and against how many peers. Ranks are against models with evidence in that category, not against everything we track: ranking against models nobody tested would rank who published, not who is better.
No category scores to shape yet.
A category score is the weighted mean of the benchmarks published for it. With no published rows there is nothing to average, and an empty chart drawn at zero would say something false.
How we decide what counts as evidence06 / cost
List API rates as last read from the provider, with the source on every row, plus every change we have recorded since we started tracking it.
| Charge | Price | Unit | Read on | Source |
|---|---|---|---|---|
| Input | $2.00 | per 1M tokens | 2026-09-24 | Check |
| Output | $12.00 | per 1M tokens | 2026-09-24 | Check |
It is priced in the mid range for a frontier-class general model, cheap enough for production document and media work but not the tier you would reach for on high-volume, low-value classification. Output costs several times more than input, so the cost of a workload depends mostly on how much you ask it to write rather than how much you feed it.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $100.00 |
| A busy support assistant | 200M tokens | 40M tokens | $880.00 |
| A document pipeline | 1000M tokens | 100M tokens | $3,200.00 |
List API rates, no caching and no batch discount, which both providers offer and which change the answer a great deal. Treat these as the ceiling, not the bill.
07 / specs
As listed by the provider's own catalogue and re-read every few hours. Anything absent is absent there too.
| Context window | 1,048,576 tokens |
|---|---|
| Maximum output | 65,536 tokens |
| Modalities | audio, file, image, text, video |
| Released | 27/04/2026 |
| Status | Current |
| Catalogue identifier | ~google/gemini-pro-latest |
08 / lineage
What this model replaced, what replaced it, and what else its provider has in the field.
Also from Google
09 / line
Every model in this family in release order, so a page from eight months ago says in one glance that two newer ones exist.
Nothing else in this line yet.
A line is worked out from the naming and the release dates across every model page we hold. It fills in as the provider ships successors, or as we pick up the models that came before this one.
How we decide what counts as evidence10 / notes
What this model changes for a brand trying to be cited in AI answers, and every change we have logged since it launched.
What it changes for you
Because this alias tracks the leading Gemini Pro model, the answers it gives about your category will shift whenever Google updates underneath, without any announcement in your logs. That makes one-off citation checks unreliable, you need repeated monitoring of how your brand is described across Gemini surfaces. The upside is that whatever earns citations in the current Pro model earns them here too, so the work is the same work, just measured more often.
Buyers meet the Gemini Pro family through the Gemini consumer app and through Google surfaces that run on it, usually without seeing a model name at all. Developers meet this particular entry in the Gemini API, where it appears as a model option alongside the pinned versions. In practice, anyone using the latest Pro tier in a Google product is seeing the same capability this alias resolves to.
Nothing published here yet. Changes appear within hours of a provider announcing them.
11 / questions
The things people ask about this model, answered from what is on this page rather than from anywhere else.
Models change what gets cited. We measure whether AI answers name your brand or your competitors across every assistant, and show you what to change.