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Free AI Visibility AuditGemini 2.5 Flash is Google's workhorse model in the Gemini 2.5 family, built for advanced reasoning, coding, mathematics and scientific work, with built-in "thinking" that lets it work through a problem before answering.
GoogleVerified 24/09/2026Released 17/06/202501 / 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 126 models we track, not against an absolute standard.
Capability
Not measured
No published benchmark scores yet
Input / 1M tokens
$0.30
field median $0.30
Output / 1M tokens
$2.50
field median $1.25
Context
1,049K
field median 524K
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.
Gemini 2.5 Flash is a general-purpose reasoning model aimed at the everyday volume work of a product: classification, extraction, code, structured analysis and long-document questions. Google positions it as the default workhorse rather than the top of the range, so it takes on reasoning tasks at scale rather than the hardest single problems where a frontier sibling is the better call.
Where it sits in its line, and when a sibling is the better pick.
Gemini 2.5 Flash first appeared in June 2025 as the mid-tier member of the Gemini 2.5 line, sitting between the cheapest Flash-class options and Google's heavier Pro models. It is the right pick when you are running a lot of requests and still want reasoning behind each one. It is the wrong pick when a single answer carries a lot of weight and you would rather pay for the largest model in the family, or when the job is trivial enough that a lighter model would do.
The API, the apps it powers, and what its limits let you do.
Gemini 2.5 Flash is reached through the Gemini API and Google's developer tooling, and it powers consumer-facing Gemini experiences. It takes text, images, audio, video and files as input, so you can hand it a recording, a screenshot, a video or a long PDF without a separate pipeline. The context window runs to roughly a million tokens, which is enough to hold an entire documentation set, a long transcript or a large codebase in a single request, and output can run long enough for full reports or substantial code.
What changes because this exists, or why it does not.
Gemini 2.5 Flash makes reasoning cheap enough to apply to every request rather than the important ones. Work that used to need a routing layer, a small model for the easy cases and an expensive one for the rest, can now sit on one model. The very large context plus native audio and video input also removes a chunk of preprocessing that teams used to build by hand.
Superseded by Gemini 2.5 Flash Lite. See the whole line.
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 | $0.30 | per 1M tokens | 2026-09-24 | Check |
| Output | $2.50 | per 1M tokens | 2026-09-24 | Check |
Gemini 2.5 Flash is priced as a volume model, cheap enough to sit in the hot path of a product rather than behind a cost gate, with output costing several times more than input as is normal across the field. Against frontier models from the same generation it is an order of magnitude cheaper to run, which is the whole point of it, though genuinely lightweight models still undercut it.
| Workload | Input / month | Output / month | Cost |
|---|---|---|---|
| A small product team | 20M tokens | 5M tokens | $18.50 |
| A busy support assistant | 200M tokens | 40M tokens | $160.00 |
| A document pipeline | 1000M tokens | 100M tokens | $550.00 |
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 | 65,535 tokens |
| Modalities | file, image, text, audio, video |
| Released | 17/06/2025 |
| Status | Current |
| Catalogue identifier | google/gemini-2.5-flash |
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.
Came before
The first we track in this line.
Came after
Gemini 2.5 Flash Lite →Ordered by release date and worked out from the naming, so a new member slots in as soon as its page exists. A retired model keeps its page and its place in the line.
10 / 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 Gemini 2.5 Flash is cheap enough to serve high-volume AI answers, it is likely to be the model actually reading your site and summarising your category for a large share of Google-originated queries. That rewards content that survives compression: clear claims, named products, explicit pricing and comparison detail that a model can lift without inference. The long context also means it can hold your full documentation or a whole competitor set at once, so partial coverage of your own product shows up plainly next to rivals.
Buyers meet Gemini 2.5 Flash inside the Gemini app and across Google surfaces where Gemini answers appear, often without knowing which model in the family served the response. Developers meet it directly through the Gemini API, where it is a common default for production traffic. Plenty of third-party AI features in other products are also running on it underneath.
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.