Multimodal AI Prompting: The Complete 2026 Input Guide

Key takeaways:

• Multimodal INPUT prompting and image or video GENERATION are different disciplines. This pillar covers the input side — sending media into text models and getting analysis back. The image and video pillars cover the output side. The structural moves rhyme; the model choices and failure modes do not.
• Each frontier model has a distinct input surface. Claude leads on PDFs and multi-page document reasoning. GPT-4o leads on screenshots, charts, and native audio. Gemini 2.5 Pro is the only major model that takes video as a first-class input and has the broadest surface across modalities. Picking per-modality is half the work.
• A strong multimodal prompt fills five slots: modality, instruction, context, output shape, success criteria. Forgetting any of them means the model picks a generic default, and the default is almost always a long descriptive paragraph when you wanted structured data.
• Format choice is a real decision, not a default. PDF preserves layout and tables when sent to Claude; converting the same document to images loses that structure. Audio sent natively to GPT-4o or Gemini retains tone, pacing, and overlapping speech that a transcript destroys. Video sent natively to Gemini lets the model reason across visual, spoken, and on-screen text simultaneously.
• Most multimodal prompts under-use what the model can see. Describing what is in the image before the model sees it biases the response and wastes tokens; only describe what is not visible. Crop or annotate before sending when you want the model to focus on a region. Number multiple images when you want the response to reference them precisely.
• Hallucinated-from-media is the most common and most dangerous failure mode. A model that confidently describes a chart that is not in the image, cites a clause that is not in the PDF, or transcribes audio that was inaudible passes a casual read. Evaluation has to check that every claim maps to something the model actually saw or heard — not just that the prose reads competent.
• Multimodal input composes with everything else. It pairs with reasoning models for analysis-heavy work, with agentic loops for tool-using workflows that act on what the model saw, and with multimodal RAG for retrieval over media corpora. Treat it as the perception layer of the broader stack, not as a standalone trick.

Most people still prompt AI with text only. They type questions, paste paragraphs, maybe format a system prompt. Meanwhile the frontier models in 2026 can see photographs, read PDFs, listen to audio, and watch video. If you are only sending text, you are leaving the most powerful capabilities of GPT-4o, Claude, and Gemini completely untouched.

This pillar consolidates the SurePrompts multimodal cluster into a canonical entry point on the INPUT side specifically. Each section links out to the deep-dive post for the model or modality it references. Use this page to pick the right model per modality, learn the shared five-slot anatomy, understand the per-modality dialects, and know how to evaluate output without confusing fluent prose for accurate analysis. For the OUTPUT side — generating images and video from text prompts — the sister Phase 3 pillars are AI image prompting and AI video prompting; the third sister pillar covers AI reasoning models, which compose with multimodal input on analysis-heavy work. For the broader discipline this all sits inside, see the context engineering pillar — the 2026 replacement for prompt engineering as a generic label.

What Multimodal Prompting Actually Is in 2026

Multimodal prompting means giving an AI model more than one type of input in a single interaction. Instead of describing an image with words and asking the model to imagine it, you attach the image directly. Instead of transcribing a meeting and pasting the transcript, you upload the audio file. Instead of summarizing a video yourself, you give the model the video and ask it to do the work.

Mechanically the underlying capability is a vision-language model — or, in the broader frame, a model with multiple aligned modality encoders feeding into a shared semantic space. The text encoder reads your instruction; the image, audio, and video encoders read their respective inputs; the model reasons across all of them at once. The architectural details vary by family. The prompting consequence is the same — your text and your media are both inputs the model has to compose, not separate channels with separate jobs.

The key word in the definition is structured. In 2024 most multimodal prompts were "here is the file, what do you see?" In 2026 the good ones are five-slot briefs — modality, instruction, context, output shape, success criteria — composed deliberately. An image without an instruction gets a generic description; an instruction without the media forces the model to guess.

This pillar is INPUT only. The output side — image and video generation — is a different discipline. The two share vocabulary (slot-based briefs, dialect translation, evaluation against criteria) but the failure modes diverge: generation fails on style, composition, and physics; analysis fails on hallucination, grounding, and source faithfulness. Keep them separate when picking models, evaluating output, and diagnosing what went wrong. The AI image prompting pillar covers the output side for stills; the AI video prompting pillar covers it for video.

The 2026 Multimodal Model Landscape

The multimodal-input market in 2026 is not a one-horse race. Each frontier model has a distinct surface — different modalities supported, different depth on each, different context ceiling for media-heavy prompts. Picking the right model per modality is half the work.

A few threads worth pulling on.

GPT-4o is the conversational multimodal default. Strong screenshot and chart reading, native audio input that handles voice cleanly, and a Code Interpreter sandbox that processes file uploads (CSV, Excel, code) programmatically. Image-analysis quality is high across natural scenes, UI screenshots, receipts, and diagrams. Audio handles speech with reasonable diarization on clear recordings and degrades gracefully on overlapping or noisy audio. The single gap on the input side is video — GPT-4o does not accept video as a first-class input, so video tasks route to Gemini. The conversational image-iteration patterns that overlap with the output side are covered in ChatGPT image prompts in 2026; the broader cross-model comparison sits in ChatGPT vs Claude in 2026.

Claude Opus 4.7 and Sonnet 4.6 are the document-reasoning workhorses. Claude's native PDF processing reads multi-page documents while preserving layout, headings, tables, and footnotes — capability that screenshot-based approaches structurally cannot match. Multi-page reasoning across long contracts, research papers, and reports is where Claude opens the largest gap. Image input is strong on charts, diagrams, screenshots, and photographs; the gaps are audio and video. Opus 4.7's million-token context window means a long document plus reference materials plus the instruction all fit in a single prompt, which makes the model the right pick for legal review, contract diff, and long-form research synthesis.

Gemini 2.5 Pro and Flash have the broadest multimodal input surface in 2026. Text, images, audio, and video — all first-class. Pro is the only frontier model that takes video natively, processing visual content, spoken audio, and on-screen text in a single pass. Multi-image prompts (compare these five photos, find the differences across this set) handle better on Gemini than on the others, helped by its million-token context. Flash is the cost-efficient sibling for high-volume routine work where Pro is overkill. The cross-model comparison is in 9 AI models compared; the canonical existing multimodal walkthrough is the multimodal prompting guide, which this pillar consolidates.

The general rule: do not pick one model for all multimodal work. The right architecture for a real workflow is heterogeneous — Claude for the contract, GPT-4o for the screenshot review and the audio meeting note, Gemini for the video. The AI model selection guide covers the broader task-to-model framework this principle sits inside.

The Universal Multimodal-Prompt Anatomy

Every strong multimodal prompt — regardless of model or modality — fills five slots. You can omit a slot on purpose. You cannot forget the slot exists. When a slot is missing, the model fills it with a plausible default, and the default is almost always too generic.

1. Modality — what kind of media you are sending. Image, PDF, screenshot, audio file, video, or multiple of the above. The model needs to know what to do with each input, especially in mixed-modality prompts. "Here is a screenshot of our checkout page and a PDF of our brand guidelines" tells the model how to weigh each input differently from "here are our checkout page and our brand guidelines." Be explicit about what each piece of media is and what role it plays.

2. Instruction — what the model should do with the media. Analyze, extract, transcribe, classify, compare, summarize, critique, redact. The verb matters. "Describe this image" produces a generic description; "extract every line item from this receipt as JSON with fields name, quantity, price" produces structured data your pipeline can use. Pair the verb with the specific aspects you want the model to attend to. "Evaluate the visual hierarchy and the call-to-action contrast" outperforms "review this design" by a wide margin.

3. Context — what the model needs to know about the media that is not visible in it. A receipt photo is just a receipt unless you tell the model it is from a business expense report and needs IRS-category classification. A screenshot of a checkout page is just a screenshot unless you tell the model the submit button was changed from green to blue last week and you are testing conversion. Context is the project-specific information the model cannot infer from the media alone, and it is the slot that lifts output quality the most on real work.

4. Output shape — what the answer should look like. JSON matching a named schema, a markdown table with specified columns, a bulleted list, a single-paragraph summary under 100 words, a structured report with named sections. Vague requirements produce vague output. For pipeline work, a strict JSON schema is almost always right. For human consumption, a structured report with named sections beats a long description because the reader can scan. "Make it good" is not an output shape.

5. Success criteria — how you will know the answer is right. Name the standards the model can use to self-check before generating. "Every line item must include name, quantity, and price; if a field is not legible, mark it as unclear rather than guessing." "Every claim must reference a specific page of the PDF." "Mark inaudible audio segments as [inaudible] rather than transcribing a guess." Success criteria do triple work — they steer generation, they give you something concrete to evaluate against, and they suppress the most common multimodal failure mode (hallucinating-from-media).

A worked example. The weak version: "What's in this image?" with a receipt photo attached. The strong version names the modality (photo of a paper receipt), the instruction (extract line items, totals, and metadata), the context (business expense from a client dinner, USD), the output shape (JSON with restaurant name, date, line_items, subtotal, tax, total, payment_method, notes), and the success criteria (mark any unclear field as "unclear" rather than guessing). The strong version is longer because it fills slots, not because it is more ornate — every phrase is doing work.

Per-Modality Dialects

Five slots are portable. How you express them shifts by modality.

Images and Screenshots

Image-plus-text is the most widely used form of multimodal prompting. The text tells the model what to do; the image provides the raw visual information. Neither is useful alone.

Three tactical decisions matter. First, crop or annotate before sending when the model only needs part of the frame. Sending a full screenshot when you want focus on one button forces processing of the whole UI; cropping the region focuses the analysis. Annotation tools that let you circle a region communicate intent in a way text alone cannot. Second, send multiple images deliberately. Two product photos for a comparison work; twenty product photos with no instructions overwhelm the model and produce shallow average descriptions. Number multiple images ("In image 1 (the kitchen)..."; "In image 2 (the bathroom)...") so the response can reference each one precisely. Third, describe what is not visible, not what is. Telling the model "this is a screenshot of a login page with a username field, password field, and blue submit button" wastes tokens and biases the response. "This is our production login page; the submit button was changed from green to blue last week and we're testing conversion impact" gives it context it could not infer from the image.

A short image prompt for a UI review:

You are a senior UX reviewer evaluating a mobile checkout screen.
Attached: a single screenshot of the screen as it appears to a
returning customer.

Identify:
1. Three usability issues, ranked by severity (high, medium, low)
2. Whether the visual hierarchy guides the user toward the
   primary action (the "Place Order" button)
3. Two accessibility concerns (contrast, touch target size,
   text readability)
4. One concrete redesign suggestion with reasoning

Output as a markdown table with columns: Finding, Severity,
Why it matters, Suggested fix. Do not invent issues that are not
visible in the screenshot.

PDFs and Documents

Document analysis is where Claude opens the largest gap, and PDF is the input format that captures the most. Claude reads PDFs natively — preserving layout, headings, tables, footnotes, and page boundaries — so a multi-page contract or research paper can be sent as a single input and reasoned about across sections. The same document sent as screenshots loses that structural metadata. For multi-page work, PDF to Claude is almost always the right choice.

Three tactical decisions. First, be specific about what you want extracted. A long PDF can answer hundreds of questions; tell the model which ones. "Summarize this document" produces a generic summary; "list every party with their role, every key date, every payment term, and any termination or renewal clauses" produces a structured extract you can verify against the source. Second, handle scan quality explicitly. Tell the model to mark unclear text as [illegible] rather than guessing, and to note the section so a human reviewer can check it. Third, for table-heavy documents, request explicit table output — markdown tables preserve row/column structure; prose summaries lose the data shape.

A short PDF prompt for contract review:

Attached: a 14-page commercial lease agreement (PDF, native digital,
not scanned).

I am not a lawyer — I need help understanding this document, not
legal advice. Extract:

- Lease term, renewal options, and rent escalation schedule
- Allocation of responsibility (maintenance, insurance, taxes,
  utilities — who pays for each)
- Restrictions on use, subleasing, or modifications
- Early-termination conditions and penalties
- Any clauses that are unusually one-sided or non-standard for
  a commercial lease

Output as a markdown report with one section per item above.
For every claim, cite the section number from the PDF. Flag
anything I should ask a lawyer about before signing.

The deeper category framing is in the document-AI glossary entry; the long-form walkthrough across PDF, screenshot, and image workflows is the multimodal prompting guide.

Audio

Audio input is supported natively by GPT-4o and Gemini 2.5 Pro and Flash. Claude does not accept audio in 2026; for Claude-side audio work you transcribe first and send the transcript as text. The choice between native audio and transcribe-then-send is real and has tradeoffs.

Send audio natively when tone, pacing, overlapping speech, or background sound carries information the transcript would lose — sentiment evaluation on a meeting, direct quotes from a podcast, cleaning up a rambling voice memo. Transcribe first when you need precise quoting, speaker diarization against a known speaker map, when audio length pushes against the context budget, when you need to redact sensitive segments, or when you are working in Claude.

Two tactical decisions. First, name the speakers if you know them, or ask the model to label generically (Speaker 1, Speaker 2) and infer roles from context. "The first speaker is the project lead, the second is the client" steers the response. Second, mark uncertain audio explicitly. Flag unclear segments as [inaudible] rather than guessing, with approximate timestamps. A confident transcription of an inaudible segment is the most common audio failure mode.

A short audio prompt for a meeting note:

Attached: a 35-minute audio recording of a sprint retrospective
with five participants (four engineers, one engineering manager).

Provide:
1. A clean transcript with speaker labels (Speaker 1 through
   Speaker 5; mark the engineering manager as EM if you can
   identify a clearly leadership-tone voice)
2. A structured summary by section: what went well, what did
   not, action items with named owners (only if explicitly
   assigned in the audio)
3. The overall sentiment with one supporting quote
4. Any segments where the audio was unclear, with timestamps

Output as a markdown document with named sections. Do not invent
action items that were not explicitly assigned.

Video

Video input is Gemini's standout 2026 capability. Gemini 2.5 Pro and Flash are the only frontier models that accept video as a first-class input, processing visual frames, audio, and on-screen text simultaneously. GPT-4o and Claude do not accept video natively; for those models, video tasks require frame extraction or audio-only transcription, which throws away most of what video carries.

Three tactical decisions. First, respect the clip-length sweet spot. Gemini handles long videos via its million-token context, but analysis quality is meaningfully higher on focused segments under roughly 30 minutes. For long-form content, split into logical segments (per chapter, per scene, per topic) and analyze each separately. Second, timestamps matter — for any retrieval or extraction task, ask the model to return timestamps with its findings. "List every product feature demonstrated in this competitor demo, with the timestamp" gives you a usable artifact; "summarize this demo" gives you prose you cannot navigate back to. Third, use the needle-in-a-haystack framing for long-video retrieval. Recall on specific facts buried in long videos degrades the same way it does for long text. Telling the model exactly what you are looking for ("find every mention of pricing or billing in this 90-minute earnings call, with timestamps and a one-sentence summary of each mention") outperforms generic summarization.

A short video prompt for a competitive teardown:

Attached: a 22-minute product demo video from a competitor.

Provide:
1. A structured list of every feature demonstrated, in the order
   they appear, with the timestamp where each one starts
2. Any pricing, plan, or trial information shown on screen
3. UI/UX patterns they use that are notably different from
   industry norms
4. Claims they make about performance, accuracy, or capability,
   with the timestamp where each claim is made

Output as a markdown document with sections matching the items
above. Do not summarize features that are merely mentioned
verbally without being demonstrated visually — flag those
separately as "verbal-only mentions."

For the OUTPUT side of video — generating clips from text prompts — see the sister AI video prompting pillar, which covers Veo 3, Sora 2, Runway Gen-3, Kling, and Luma. The two disciplines share almost no operational overlap; video understanding is perception, video generation is synthesis.

Charts and Diagrams

Charts, graphs, flowcharts, and technical diagrams are an in-between case worth handling separately. They are images, but they encode structured information the model has to interpret. Two failure modes — OCR errors (misreading axis labels) and reasoning errors (misreading visual encoding, confusing categories, miscounting bars).

Two tactical decisions. First, ask for the data, not the description. "Extract quarterly revenue values from this bar chart as a markdown table with columns Quarter, Product Line, Revenue (USD)" gives you verifiable data; "describe what this chart shows" gives you prose. Second, separate observation from inference. Ask the model to first list what it sees (categories, axes, values), then separately describe trends and anomalies. This two-step framing reduces the chance the model invents a trend the data does not support — a common failure on busy charts where the model pattern-matches to a generic narrative.

A short chart prompt:

Attached: a bar chart showing quarterly revenue by product line
for 2025.

Step 1: Extract the data. List every product line, every quarter,
and every value visible on the chart, as a markdown table with
columns Product Line, Quarter, Revenue (USD).

Step 2: Once the data is extracted, separately answer: which
product line grew fastest in percentage terms over the year,
and which one shrank? Cite the values from your Step 1 table.

Do not infer values that are not visible. If a label is unclear,
mark the value as "unclear" in Step 1 and note it in Step 2.

The Image and Video Output Boundary

This pillar covers INPUT only. The two disciplines on the other side of the line are image generation and video generation. They share vocabulary with multimodal input but the model choices, prompt structures, and failure modes diverge.

Image generation sends a text brief in and gets pixels out. The model landscape is different (Midjourney, DALL-E, Flux, Stable Diffusion, Imagen, Ideogram, Firefly — none of which appear in this pillar's input-side table). The prompt anatomy is different (six slots: subject, style, lighting, composition, mood, technical). The failure modes are different (style drift, composition errors, hand and text artifacts — versus hallucinated-from-media on the input side). The canonical guide is the sister AI image prompting pillar.

Video generation is the same shape, scaled up with motion, camera, duration, and audio. Models are Veo 3, Sora 2, Runway Gen-3, Kling, Luma, Pika. The prompt anatomy is ten slots. Failure modes include rubbery physics, character drift across frames, and text that morphs. The canonical guide is the sister AI video prompting pillar.

The one place input and output meet is the conversational image-iteration flow inside ChatGPT — where you generate an image, then send a follow-up combining new instruction with the image you just generated, and the model reasons across both. That hybrid sits at the intersection, and the patterns are in ChatGPT image prompts in 2026.

The mental model: input-side multimodal is perception, output-side is synthesis. Different verbs, different tools, different evaluation. Keeping them separate when you pick a model and diagnose a failure saves a lot of time.

Multimodal Workflows that Actually Ship

Production multimodal workflows tend to follow a small set of repeatable patterns. Each composes a modality, a model choice, and an output shape into something that ships.

Screenshot-to-code. A UI mockup or design screenshot in, a working component out. Send the screenshot to GPT-4o or Claude with an instruction that names the framework (React, SwiftUI, HTML/CSS), the styling approach (Tailwind, CSS modules, system styles), and the success criteria (working code, semantic markup, accessible by default). Both models do this well; Claude tends to produce cleaner, more idiomatic code with stricter constraint adherence.

Attached: a screenshot of a card component design.
Generate the React + Tailwind code for this card. Use semantic
HTML, ensure WCAG AA contrast on text, keep all styling in
Tailwind classes. Output the component code only, no explanation.

Document-to-data. A PDF, scan, or image of a structured document in, a structured data object out. Send to Claude (for native PDFs) or GPT-4o (for screenshots and short scans) with an explicit JSON schema and a rule that unclear fields must be marked rather than guessed. Receipts, invoices, business cards, forms, lab reports, and shipping documents all fit. The output schema is the most important slot — without it, you get prose; with it, parseable data.

Photo-to-listing. Multiple product or property photos in, a listing description out. Send as a numbered set, instruct the model to write in the target voice and length, and constrain it to features visible in the photos (forbid invented features). Real estate, e-commerce, and resale all use this pattern.

Attached: 5 numbered photos of a leather messenger bag.

Write an e-commerce product description with a title (under 80
characters), five bullet points for highlights, and one paragraph
for the "Product Details" tab. Use only features visible in the
photos. Where a material is uncertain, use "appears to be." Do
not invent dimensions; omit size if not clear from the photos.

Whiteboard-to-spec. A photo of a whiteboard or scratchpad in, a structured technical spec or meeting note out. Send to GPT-4o or Gemini with context about what the whiteboard captures (architecture diagram, sprint plan, decision tree), instruct the model to translate the visual into a structured document, and ask it to flag anything illegible. One of the highest-value flows for engineering teams because it converts post-meeting cleanup into a one-prompt job.

Video-to-summary. A long video in, a chaptered summary or structured note out. Send to Gemini 2.5 Pro with an instruction naming the artifact (chaptered timeline, action items, claims-with-timestamps, study notes), specify timestamps in the output, and split videos longer than roughly 30 minutes into focused segments. Lecture notes, podcast summaries, competitive teardowns, and meeting recordings all fit. The deeper walkthrough is in the multimodal prompting guide.

The general shape across all five: pick the modality that best carries the source information, pick the model that best handles it, and let the output shape do the structural work prose cannot. A real multimodal-heavy stack uses three or four of these patterns side by side rather than forcing one tool to do everything.

Multimodal RAG Briefly

The natural extension of multimodal input is multimodal RAG — retrieval-augmented generation over a corpus containing images, PDFs, audio, or video alongside text. Instead of one image plus a prompt, you have a thousand images and a prompt, and a retrieval layer that surfaces the right items based on the user's question.

The architectural pieces parallel text RAG with an added wrinkle. You index the corpus using embeddings that span modalities (CLIP-family for image-and-text, audio embeddings for sound, frame embeddings for video). At query time, embed the user's question and retrieve the most relevant items across modalities. Send the retrieved items, with source citations, into a multimodal model alongside the question. Every output claim should ground in a retrieved item — the same source-grounding discipline that makes text RAG work.

Multimodal RAG matters most when the corpus is too large to send in context and questions are open-ended enough that pre-processing into pure text loses information. Examples: a product catalog with photos and spec sheets, an internal training video library, an architectural drawing archive. Full implementation is its own future pillar; the high-level patterns share vocabulary with the broader agentic prompt stack, where retrieval, multimodal perception, and reasoning compose inside an agentic loop.

Honest Evaluation

"It sounds right" and "it is right" are different standards on multimodal output, and the distance between them is wider than most teams account for. The most common multimodal failure mode is hallucinated-from-media: the model confidently describes something that is not in the image, cites a clause that is not in the PDF, transcribes audio that was inaudible, or names a feature that was never demonstrated in the video. The prose reads competent. The grounding is fiction.

Evaluation has to catch this, and it has to be slot-by-slot.

Instruction faithfulness. Did the response do what you asked, or an adjacent thing? "Extract every line item as JSON" is not the same as "describe what is on this receipt." Walk the verb in the original instruction and check whether the response executed it.

Source grounding. Does every claim map to something visible or audible in the media? On images, walk each described element and confirm it is in the frame. On PDFs, check citations against page numbers. On audio, spot-check transcribed quotes against the recording. On video, scrub to claimed timestamps. Source-grounding is the slot most often skipped because it is tedious; it is also where most production multimodal failures live.

Output shape compliance. JSON parses. Schema is satisfied. Required sections are present. Length is within bounds. Pipeline-bound output that ships malformed JSON is worse than no output.

Uncertainty handling. When the media was unclear, did the model flag the uncertainty or assert with confidence? A model that confidently transcribes an inaudible segment, extracts a price from a partially-visible receipt, or describes the interior of a room shown only from outside the window has invented data. Production prompts should mandate uncertainty flags; evaluation should verify those flags appear when warranted.

Audience and use match. A code review meant for a junior engineer that reads like an internal post-mortem misses the audience slot, even if technically correct. Walk the original audience specification and check fit.

Two patterns formalize this evaluation for production work. LLM-as-judge rubrics pass the response back to a different model with an explicit rubric (score 1-5 on instruction faithfulness, source grounding, output shape compliance, uncertainty handling, audience match; flag any unsupported claim). LLM-as-judge inherits some of the same failure modes as the model it judges but catches a meaningful fraction of beautiful-sounding wrong answers human reviewers miss at scale. The SurePrompts Quality Rubric is the rubric we use for the prompts themselves; the same shape works for evaluating multimodal outputs. Self-critique loops generate, critique against a named rubric, then revise — small marginal cost relative to shipping a hallucinated extraction. For agentic workflows that loop perception, action, and reflection, see the agentic prompt stack.

Coherence is not correctness. A multimodal model that confidently describes a chart that is not in the image is the most dangerous failure mode in this category — the prose passes a casual read. The evaluation has to be sharper than it is for text-only work, not looser.

What's Next

The frontier is moving from single-call multimodal to multimodal agents — perception loops where a model sees, decides, acts, and observes the result before its next decision. Claude's interleaved thinking applied to multimodal work, GPT-4o's Code Interpreter as a tool that processes the file the model just read, Gemini's native video understanding plugged into agentic frameworks that navigate long-form content interactively. The single-shot multimodal prompt is becoming the inside of a loop, not the whole interaction.

Combine multimodal input with reasoning models for analysis-heavy work where deliberation matters — Claude extended thinking on a long contract, o3 on a complex chart-plus-text problem, Gemini Deep Think across a video and supporting documents. Combine it with image and video generation when the workflow loops perception and synthesis — analyze a screenshot, generate a redesign, evaluate the result. And put all of it inside the broader frame of context engineering — the 2026 discipline that treats every prompt, multimodal or not, as a deliberate composition of context.

Multimodal input prompting in 2026 is a brief-writing discipline with a perception layer attached. Pick the right model for the modality. Compose the five-slot brief — modality, instruction, context, output shape, success criteria. Frontload the instruction, attach the media, close with the criteria. Evaluate the answer against what the model actually saw, not against vibe. The repeatable multimodal workflow that ships a correct answer the third time, every time — that is what scales.