# Multimodality and long context: Gemini's superpowers
Hand Gemini a two-hour recorded webinar and a 300-page contract in the same prompt, then ask "at what timestamp does the speaker contradict clause 14?" and it can actually answer. That combination, native multimodality plus a very large context windowcontext windowThe context window is the maximum amount of text (measured in tokens) a language model can process at once, including both the input prompt and the generated output.Voir la définition complète →, is the thing Gemini does that most assistants still fake by chopping inputs into pieces.
This lesson is about using those two strengths deliberately, and knowing when they earn their keep.
Gemini was trained from the start to process text, images, audio, and video as tokenstokens in the same model. This is different from a text-only model bolted to a separate vision or speech system. There is no transcription step you can't see, no separate OCR pass that loses layout.
The practical consequence: Gemini reasons *across* modalities in one pass. It can read the chart in a PDF, connect it to a sentence three pages later, and notice the audio tone in a video clip doesn't match the slide on screen.
TokenTokenA token is the basic unit of text that language models process, often a word fragment, whole word, or punctuation mark rather than a single character.Voir la définition complète → here is the unit of input. Text, pixels, and audio frames all get converted to tokenstokensA token is the basic unit of text that language models process, often a word fragment, whole word, or punctuation mark rather than a single character.Voir la définition complète →. A long context windowcontext windowThe context window is the maximum amount of text (measured in tokens) a language model can process at once, including both the input prompt and the generated output.Voir la définition complète → means a large tokentokenA token is the basic unit of text that language models process, often a word fragment, whole word, or punctuation mark rather than a single character.Voir la définition complète → budget, and Gemini's window (in the millions of tokenstokensA token is the basic unit of text that language models process, often a word fragment, whole word, or punctuation mark rather than a single character.Voir la définition complète → on the Pro tier) is large enough to hold entire books, codebases, or hours of media at once.
You already know RAG: split documents, embed chunks, retrieve the relevant few. That is still the right tool for a knowledge base of 10,000 documents. But for a *single large artifact* you want reasoned over as a whole, chunking actively hurts you. It breaks cross-references, loses global structure, and can't see the thing you didn't know to search for.
Long context lets you skip retrieval entirely for that case. Drop the whole document in. Gemini sees all of it, so it can answer questions that span the entire file.
Say you have a recorded engineering all-hands, 90 minutes, and a design doc PDF. You want to verify the talk matches the doc.
In Google AI Studio, you can upload both and just ask. Via the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète →, here is the same thing with the google-genai SDK:
from google import genai
client = genai.Client()
video = client.files.upload(file="all-hands.mp4")
doc = client.files.upload(file="design-doc.pdf")
response = client.models.generate_content(
model="gemini-2.5-pro",
contents=[
video, doc,
"Find every place the speaker describes the rollout plan. "
"For each, give the timestamp and quote, then say whether it "
"matches the design doc. Cite the doc page for any mismatch.",
],
)
print(response.text)You get timestamps, quotes, page citations, and a verdict. No transcription pipelinepipelineAll active sales opportunities across the stages of the sales process, together with their combined potential value and probability of closing.Voir la définition complète →, no chunking config, no vector storevector storeA vector database stores data as high-dimensional numeric vectors (embeddings) and retrieves items by similarity rather than exact matches, powering semantic search and AI applications.Voir la définition complète →. Gemini watched the video and read the PDF together.
A few things worth knowing here:
client.files.upload, which stores the file temporarily and hands you a reference. Use it for anything beyond small inline images.MM:SS references and even ask "what's on screen at 42:15?"gemini-2.5-pro for deep reasoning over long inputs, gemini-2.5-flash when you want speed and lower cost and the task is more extraction than reasoning.You don't need the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → to use this. In the Gemini app, upload a PDF, an image, or audio and ask away. In Google Workspace, the reachreachThe number of unique people exposed to your message in a given period. Unlike impressions, reach counts each person once, no matter how often they see it.Voir la définition complète → is bigger:
If you do this kind of multi-document analysis repeatedly, build a Gem: a saved, reusable configuration of Gemini with your own instructions and (optionally) attached reference files. Think of it as a saved expert. A "Contract Reviewer" Gem with your clause checklist baked in saves you re-typing the framing every time.
Long context is a tool, not a default. Use it when the value is in *seeing everything at once*.
Strong fits:
Weak fits (use RAG or tools instead):
Big inputs cost more and run slower, in rough proportion to tokenstokensA token is the basic unit of text that language models process, often a word fragment, whole word, or punctuation mark rather than a single character.Voir la définition complète → processed. Two ways to manage that:
1. Context caching: if you ask many questions against the *same* large document, cache it once via the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → so you don't re-send (and re-pay for) the full input every turn. See the Gemini API context caching docs.
2. Right-size the model: Flash for extraction at scale, Pro for the genuinely hard reasoning passes.
Long context handles *your* data. For *world* data, combine it with grounding. Grounding with Google Search lets Gemini check claims against live results and return citations, which directly fights hallucinationhallucinationA hallucination is when an AI model generates output that is fluent and confident but factually wrong, fabricated, or unsupported by its source data.Voir la définition complète →.
In the APIAPIApplication Programming Interface: a standardised interface that lets applications communicate and exchange data without knowing each other's internal workings.Voir la définition complète → you enable it as a tool:
from google import genai
from google.genai import types
client = genai.Client()
response = client.models.generate_content(
model="gemini-2.5-pro",
contents="Summarize this report, then flag any claims that current "
"public data contradicts.",
config=types.GenerateContentConfig(
tools=[types.Tool(google_search=types.GoogleSearch())]
),
)
print(response.text)Now Gemini reasons over the long document you provided *and* checks specific claims against the live web. That pairing, your private context plus grounded public facts, is hard to replicate with a text-only assistant.
Vérification des acquis
1. What does it mean that Gemini is "natively multimodal"?
2. According to the lesson, when does long context beat chunk-and-retrieve (RAG)?
3. In Google's Gemini ecosystem, which model tier is described as offering a context window in the millions of tokens?
4. Select ALL correct answers about why chunk-and-retrieve can hurt when working with a single large document.
Sélectionnez toutes les réponses correctes.
5. Select ALL correct answers about how Gemini handles tokens and modalities.
Sélectionnez toutes les réponses correctes.
The same multimodal, long-context capability shows up across Google's stack at different altitudes. Pick by what you're building.
For individual and team workflows with no code. Upload files, build Gems, use Workspace integration. This is where most professionals start and where a lot of real work happens.
For prototyping and building products. AI Studio is the fastest place to test a multimodal prompt, tune settings, and grab the equivalent code. When you're ready, the Gemini API (the google-genai SDK above) ships it. This is the right layer for startups and quick internal tools.
For production at enterprise scale: governance, data residency, MLOpsMLOpsMachine Learning Operations: combining ML and DevOps practices to industrialise, deploy, monitor, and retrain models reliably in production.Voir la définition complète →, IAM, and tighter Google Cloud integration. Same models, enterprise wrapper. See Vertex AI. The mental model: AI Studio to prototype, Vertex AI to run it where compliance and scale matter.
A few patterns that reliably produce good results with large multimodal inputs.
Ask for structure. When extracting from a long file, request JSON. It forces precision and is easy to consume downstream:
config = types.GenerateContentConfig(
response_mime_type="application/json",
response_schema={
"type": "object",
"properties": {
"findings": {
"type": "array",
"items": {
"type": "object",
"properties": {
"timestamp": {"type": "string"},
"quote": {"type": "string"},
"matches_doc": {"type": "boolean"},
},
},
}
},
},
)Put the question after the data. With huge inputs, state the documents first, then ask. The model attends well across the whole window, but a clear instruction at the end keeps the task sharp.
Ask for citations every time. "Cite the page or timestamp" is not optional on long inputs. It makes the answer verifiable and exposes when the model is guessing.
Decompose long video. For a 2-hour recording, one pass to build a timestamped outline, then targeted follow-ups against specific segmentssegmentsDividing a market into distinct groups of customers who share similar needs, characteristics or behaviours, so each group can be served with a tailored approach.Voir la définition complète →, often beats one giant open-ended question.
Three real differences, stated plainly:
1. Native video and audio in the model, not a transcript handed to a text model. You can ask about what's *shown* and *said*, with timestamps.
2. A genuinely large context window that holds whole books or codebases, so you can skip chunking for single-artifact reasoning.
3. Deep Workspace and Google Cloud reach, so the same capability runs from the Gemini app to Vertex AI without re-architecting.
That doesn't make long context the answer to everything. Big corpora still want RAG. But for "here is one large, messy, multimodal thing, reason over all of it," this is where Gemini is genuinely ahead.