D3 · ChatGPT Surfaces and Features

This domain is worth 14% of the mock – roughly 8 of 60 items. It tests one skill above all: given a task, pick the right model and the right feature instead of doing everything in a plain chat with the default model. The exam does not reward memorising a feature list; it rewards matching a capability to a need – search for current facts, deep research for a sourced report, a project for recurring context, data analysis for real computation, and the cheapest capable model for the job.

What you need to know

ChatGPT is not one thing; it is a model picker plus a toolbox. The model choices in September 2026 range from the low-cost GPT-5.6 Luna through Terra and Sol (and Sol Pro), a lightweight GPT-5 Thinking Mini, up to GPT-6 Pro (powered by GPT-6 Astra) on higher-tier plans. On top of the model you choose features: Projects, canvas, Search, deep research, study mode, memory, file uploads, data analysis, image generation, voice, GPTs, scheduled tasks, Sites, workspace agents and company knowledge. Choosing well is the difference between a fast, reliable result and a slow, ungrounded one.

Learning objectives

By the end of this page you should be able to:

1. Select an appropriate ChatGPT model for a task by capability and cost.
2. Distinguish Search from deep research and know when each applies.
3. Choose between a plain chat, a Project, canvas, and memory for holding context.
4. Route computation to data analysis and use file uploads correctly.
5. Pick the right feature for images, voice, recurring tasks and internal-data tasks.
6. Apply a single decision table to select a surface under time pressure.

3.1 Choosing a ChatGPT model

The models trade capability, speed and cost. Pick the least powerful one that clears the task.

Legacy models remain available, but for new work choose from the current lineup. The instinct to reserve is “use the most powerful model for everything” – it is slow and expensive for tasks Terra or Luna handle perfectly.

3.2 Search versus deep research

Both reach beyond the model’s knowledge cutoff, but they answer different needs.

need current info?
   │
   ├─ a quick fact / a few links ─────────► Search
   └─ a thorough, sourced report ─────────► deep research

Both are the answer to the D1/D2 knowledge-cutoff problem: when a task needs current information, add a retrieval feature rather than trusting model memory.

3.3 Holding context: chat vs Project vs canvas vs memory

A frequent D3 item asks where a piece of context should live.

• Put stable, reusable material (a style guide, product facts, custom instructions) in a Project.
• Use canvas when you are drafting and revising a single artefact and want to edit inline rather than re-paste.
• Use memory for lightweight preferences (“I work in EU timezone”, “prefer British spelling”), not for bulk documents. Memory is expanding, and memory-with-past-chats is noted as coming soon; do not overload it.

3.4 Files and data analysis

File uploads let the model read documents, spreadsheets, images and more. Data analysis goes further: it runs real code on your data to compute, chart and transform – the correct home for the exact arithmetic that the raw model gets wrong (D2).

3.5 Study mode, image generation and voice

Study mode is worth calling out because it maps onto the Academy’s own teaching stance: it acts as a tutor rather than an answer vending machine, which is exactly the posture the assessment rewards.

3.6 GPTs, scheduled tasks, Sites and workspace agents

These are the “beyond a single chat” surfaces.

Company knowledge is the internal-data counterpart to Search: instead of the open web, it grounds answers in your organisation’s connected sources – the right choice when the correct answer is inside the company, not on the internet.

where does the answer live?
   │
   ├─ open web, current ───────► Search / deep research
   ├─ inside our org's systems ─► company knowledge
   ├─ in this one document ─────► file upload
   └─ in the model's training ──► plain chat (then verify)

Decision framework

Use the surface-selection table: read down the “I need to…” column, take the surface on the right.

The value: it converts a fuzzy “how do I do this in ChatGPT?” into a one-line lookup that stops you defaulting to a plain chat with the top model for everything.

Common mistakes

Scenario challenge

Scenario. Amara, a marketing lead, has four tasks before end of day: (1) confirm a competitor’s just-announced pricing, (2) produce a five-page sourced brief on the category’s regulatory outlook, (3) clean and total a 500-row campaign spend spreadsheet, and (4) answer a question about her own company’s internal brand guidelines that live in a connected internal drive. She is about to do all four in a single plain chat with GPT-6 Pro because “it’s the smartest”.

Expert reasoning trace.

1. Task 1 needs current facts. A just-announced price is past any cutoff; a plain chat would either refuse or hallucinate. Search returns a quick, cited answer.
2. Task 2 is a multi-source report. Search is too shallow; deep research browses many sources and produces the organised, cited synthesis she needs.
3. Task 3 is exact computation on 500 rows. The model would guess a plausible-but-wrong total; data analysis runs real code on the file, so the totals and charts are trustworthy.
4. Task 4’s answer lives inside the company. Web search would miss it; company knowledge grounds the answer in the connected internal source.
5. Model choice. GPT-6 Pro is over-powered and slow for the extraction/summarisation parts; Terra (or Luna for the simple extraction) is more cost-effective. Reserve the top model for genuinely hard reasoning, which none of these four strictly require.

Exam-correct outcome: Search for the price, deep research for the brief, data analysis for the spreadsheet, company knowledge for the internal question – and a right-sized model per task rather than GPT-6 Pro for everything in one chat.

Assessment traps

Practice questions

Key takeaways

• Pick the least powerful model that clears the task: Luna for high-volume simple work, Terra as the all-rounder, Sol/Sol Pro for complex work, GPT-6 Pro (Astra) for the hardest reasoning.
• Search for a quick current fact with links; deep research for a multi-source sourced report.
• Hold reusable context in a Project, iterate one document in canvas, keep small preferences in memory.
• Route exact computation to data analysis; verify summaries from file uploads against the source.
• Use company knowledge for internal answers, Search for the open web.
• GPTs, scheduled tasks, Sites and workspace agents extend ChatGPT beyond a single chat.
• The right question is always “where does the answer live and what is the least effort to reach it reliably?”