ChatGPT in 2026: GPT-5.2, memory, and multimodal insights

ChatGPT arrived as a cultural and technical milestone in 2022, and the 2026 landscape finds it still among the most capable and broadly useful consumer AI chatbots — but in a world where rivals, product economics, and legal scrutiny have reshaped what “best” actually means. The PCMag Australia review you shared praises ChatGPT for accurate, detailed replies, strong creative writing, good customization and the most comprehensive memory implementation of the major chatbots — while also noting important limits: occasional factual errors, fewer built-in productivity features than some competitors, and a middling value proposition compared with Google’s Gemini. That assessment is a useful snapshot of ChatGPT’s strengths and trade-offs as of early 2026. / Overview
ChatGPT today is a multi-modal, multi-tiered product built around OpenAI’s GPT-5.x family and the evolving ChatGPT product platform. The product blends:

• conversational chat across text and voice,
• file and image handling,
• image synthesis and editing,
• video generation (Sora),
• an agent/automation layer, and
• an extensibility ecosystem (custom GPTs and plugins).

OpenAI’s public rollout of the GPT-5.2 family formalized three operating variants — Instant, Thinking, and Pro — that map to latency, cost, and fidelity trade-offs for real-world tasks. The company touts notable gains in long-context understanding, tool-calling (agentic workflows), coding, and vision capabilities in GPT‑5.2.
At the same time, competitors have pushed hard on integration or specialized strengths: Google’s Gemini plays to Google Workspace integration and multimodal creation, Microsoft’s Copilot doubles down on in‑app automation for Microsoft 365 and tenant governance, and Anthropic’s Claude emphasizes large context windows and safety-first behaviour. That ecosystem pressure explains many of ChatGPT’s recent product moves and commercial choices, from feature parity to new pricing tiers. The PCMag Australia review captures this competitive context while detailing where ChatGPT e it falls short.

---

What GPT‑5.2 Actually Brings: verified technical highlights​

The Instant / Thinking / Pro split — why it matters​

OpenAI designed GPT‑5.2 as a family of variants to let products balance speed and quality. The company describes:

• GPT‑5.2 Instant for fast, high-throughput tasks,
• GPT‑5.2 Thinking for heavier, multi-step reasoning and long-context work, and
• GPT‑5.2 Pro for the highest-fidelity, mission‑critical outputs.

Those variants are now integrated into ChatGPT and the API with staged availability for paid tiers. OpenAI’s announcement highlights improvements in code repair, long-document comprehension, and visual understanding as the primary, load‑bearing advances.

Long-context performance — what’s verified​

One of the most meaningful technical gains is long-context handling. OpenAI’s public materials and benchmark reporting show GPT‑5.2 Thinking operating effectively on document-scale inputs (hundreds of thousands of tokens in controlled evaluations). Vendor benchmarks in the GPT‑5.2 release note near‑complete accuracy in targeted long‑context evals out to very large token counts, and OpenAI explicitly describes new endpoints and techniques (Responses /compact and API-specific configurations) to help workflows that extend beyond normal context windows. These are substantive, measurable improvements relative to earlier releases.
Caveat: the exact usable context in ChatGPT’s consumer UI still depends on plan and model mode, and public documentation and third‑party reporting have shown variations in how context windows are described for “Instant” vs “Thinking.” When evaluating long‑document workflows you should confirm the plan-level limits you will actually get in your tenant or account.

Vision, tool-calling, and agentic behavior​

GPT‑5.2 also explicitly improves visual understanding (chart reading, UI comprehension, diagram reasoning) and the reliability of tool-calls and agentic sequences. That is an important step for workflows that mix file ingestion, web lookups, and multi-step automation. The improvement is real and confirmed by OpenAI’s benchmarks; it does not mean hallucinations disappear — it means the failure modes are narrower and often more interpretable.

---

Plans, Pricing, and Value — what’s changed and what to watch​

The product tiering in early 2026 is clearer and somewhat more varied than in previous years:

• Free: limited access to flagship models and core features.
• ChatGPT Go: a lower‑cost mid tier ($8/month) that provides expanded usage of GPT‑5.2 Instant but — crucially — may include ads in some markets. This new plan is OpenAI’s attempt to broaden paid adoption while preserving Plus as the ad‑free premium. Multiple independent outlets reported the global Go rollout and the ad tests.
• Plus ($20/month): the baseline premium plan, granting access to the “Thinking” model in many cases, extended limits, voice and video features, and early feature access. OpenAI’s pricing pages continue to list Plus at $20/month.
• Pro ($200/month): high-throughput access, access to the strongest reasoning modes and broader Sora limits; priced for power users and small teams.
• Business ($25/user/mo annual) and Enterprise (custom): organizational features, admin controls, non‑training-by-default context windows. These tiers are where enterprises can materially reduce data‑training risk and obtain contract commitments.

PCMag Australia’s review notes that ChatGPT’s value can be “questionable” relative to some competitors, especially when base tiers of rivals include advanced reasoning or additional features that ChatGPT gates behind more expensive plans. That remains true: the “what you get” at $20 vs $8 is different in capability and throughput.
Practical note: pricing and limits are subject to frequent change. OpenAI and other vendors have been actively adjusting quotas, adding mid-tier plans (Go) and experimys verify plan details in the billing flow or admin console when making a purchasing decision.

---

Feature deep dive: where ChatGPT excels and where it trails​

Strengths — the areas reviewers (including PCMag AU) consistently praise​

• Conversational fidelity and personality: ChatGPT is widely regarded as personable and easy to customize with personality sliders, memory, and persistent instructions. That combination improves continuity and iterative workflows.
• Customization and memory: ChatGPT’s memory implementation is among the most complete tested — it can recall prior messages, user preferences, and resume multi-session context more reliably than many competitors.
• Deep research outputs: ChatGPT’s deep‑research feature can produce long, multi‑source reports that many users find engaging and useful for exploratory research tasks. The product’s research flows and the OpenAI “deep research” tooling compete well with Google and Microsoft features, even if some export and collaboration conveniences are missing.
• Multimodal synthesis: ChatGPT now integrates image generation, editing, and Sora video generation. The Sora 2 launch improved physical plausibility, allability in video generation — capabilities that are advancing fast in the industry.
• Ecosystem breadth: custom GPTs and a large plugin ecosystem make ChatGPT flexible for many hobbyist and professional workflows.

Weaknesses and operational limits​

• Occasional hallucinations: despite improved reasoning, factual errors still occur. The product remains a tool that needs human verification for high-stakes outputs. The PCMag AU review underscores this point and recommends external checks for mission-critical facts.
• Productized productivity tooling: compared with Microsoft Copilot’s in‑app “apply changes” actions or Gemini’s exports to Google Docs, ChatGPT often requires extra manual steps to turn generated content into a final artifact. Integration workarounds and plugins mitigate this, but they add complexity.
• Video and image artifacts: Sora 2 is a significant improvement, but generated videos still show distortions, audio sync issues, and text rendering problems at times — requiring iteration and prompt engineering to get satisfactory results. Independent testing and editorial reviews confirm that AI video still needs careful calibration.
• Context and quota confusion: public documentation and third‑party reporting show varied descriptions of context windows per plan and per model mode. While GPT‑5.2 Thinking is designed for very large contexts, the consumer UI and plan-specific limits can be confusing; verify your exact contract or account capabilities before committing to a long‑document workflow.

---

Privacy, security, and legal risk — an unvarnished assessment​

Two related concerns should shape any decision to adopt ChatGPT for sensitive work:

• Data collection and model training — default behavior
  OpenAI collects user content and analytics data. Historically, user content has been used to improve models unless a user or enterprise opts out or buys a plan that provides non‑training guarantees. Enterprise and Business tiers include contractual language that can stop business data from being used to train models by default, but consumer tiers do not automatically have those protections. That nuance is central for organizations with regulatory obligations.
• Historic security incidents and disclosure practices
  Reporting established that OpenAI experienced an internal incident in 2023 in which an unauthorized party accessed an employee forum and extracted internal discussion about AI systems; the company did not characterize this as a customer data breach and did not publicly disclose it at the time. That incident and subsequent account compromise attempts underscore the reality: the vendor has been a target, and prior disclosures have raised governance questions. These are not theoretical risks — they are operational realities organizations should factor into risk assessments.

Practical guiregulated or highly sensitive PII, trade secrets, or legal strategy into consumer ChatGPT accounts.

• For organizational use, insist on business/enterprise contracts with explicit non‑training, data residency, and audit provisions.
• Maintain human verification for legal, financial, medical, or compliance decisions; keep logs and require human sign-off for automated agent actions.

---

How ChatGPT compares in common workflows​

Research and writing​

ChatGPT is strong as a first-draft assistant and ideation engine. Its deep‑research reports are long and often engaging; however, they do not always provide the export and source‑management conveniences that competitors sometimes offer (e.g., Gemini’s Google Docs export). Use ChatGPT for synthesis and drafting, pair it with citation‑first tools (Perplexity, citation plugins) for provenance-sensitive work.

Coding and engineering​

GPT‑5.2 Thinking improves on repository‑scale debugging and patch generation. For production code, combine ChatGPT suggestions with test suites and CI gates. If you need tenant-level automation that edits files in-place across an organizati’s deeper Office/tenant hooks may be preferable.

Creative media (images & video)​

Image generation is competitive with market leaders for many prompts; Sora 2’s video work shows progress but remains iterative and imperfect in many scenarios. For high-stakes or client‑facing video production, budget time for multiple passes and human review.

Agents and automation​

ChatGPT Agents are functional and useful for some multi-step tasks, but in testing they can be slower and less reliable than doing the task manually or using tightly integrated automation. Agent reliability is improving but is not yet a proven time-saver for all tasks. Buyers seeking guaranteed, auditable automation should prefer enterprise-grade agent frameworks with strong telemetry and policy controls.

---

Recommendations for readers and IT teams​

• Individuals: Try ChatGPT Go if you want a low‑cost way to access GPT‑5.2 Instant features, but expect ads in some regions; upgrade to Plus if you need the deeper reasoning (Thinking) model and ad‑free use. Confirm exact quotas in the app before relying on the plan for heavy workflows.
• Freelancers and creators: Use ChatGPT for drafts and ideation; allocate time for iteration on Sora video outputs. For client work, never deliver unverified factual or legal claims without human checks.
• Small teams and startups: If you need connectors to internal data, consider ChatGPT Business for “no training by default” protections; still validate contract language on data handling and retention. Test agent workflows in a sandbox before moving to production.
• Enterprises and regulated industries: Require enterprise contracts with explicit non‑training clauses, retention and retention‑policy settings, audit logs, SAML/SCIM integration, and SLAs. Consider Microsoft Copilot or a comparable tenant‑first product if deep Office automation and tenant governance are mandatory.

---

Strengths, risks, and an honest verdict​

ChatGPT’s core strengths in early 2026 are clear: it remains a versatile generalist with excellent conversational polish, strong customization, and a broad ecosystem of plugins and custom GPTs. GPT‑5.2’s advances in long‑context reasoning and visual understanding are meaningful and expand the product’s real-world use cases. Sora 2 makes video generation more credible than betier makes paid access more affordable for many users.
But clear risks remain:

• Accuracy cannot be guaranteed; hallucinations still happen.
• Privacy and training: consumer data is still subject to default collection for model improvement unless you opt out or are on a contract that states otherwise.
• Operational fragility: agentic automation and video generation require iteration and guardrails; do not assume "set-and-forget" reliability.
• Commercial complexity: plan limits, context windows, and ad experiments mean the practical value of a plan can differ from advertised features; confirm the exact behavior in your account or contract.

The PCMag Australia review is balanced in this light: praising ChatGPT’s accuracy, creative skills, and memory while pointing out the product’s competitive gaps and the need for verification on mission-critical outputs. That framing remains accurate and useful for potential users evaluating the product today.

---

Final takeaways — how to make the most of ChatGPT in 2026​

• Use ChatGPT as a force multiplier for drafting, prototyping, and ideation — but treat any factual or regulatory content as draft output that requires human verification.
• Match plan to need: Go ($8) is a sensible entry point for heavier casual use; Plus ($20) and Pro ($200) are for power users who need Thinking/Pro quality and higher throughput. Always confirm current plan features in the billim])
• For organizational use, insist on enterprise contracts that explicitly address training, retention, auditability, and SLAs. Never put regulated or highly sensitive data into consumer accounts.
• Expect to iterate: image and video generation are improving fast but still require prompt engineering and multiple generations to be client‑ready.
• Keep a toolchain mindset: pair ChatGPT with citation-first research tools when provenance matters, and with in‑app automation tools when you need changes applied directly to Office or Google Workspace documents.

ChatGPT in 2026 is not the only tool you should use, but it is one of the most flexible — and when paired with good verification practices, appropriate plan choices, and a realistic understanding of current limitations, it remains an indispensable creative and productivity assistant. The PCMag Australia review’s balanced verdict — praise for capability, caution on value and mistakes — is both fair and actionable for readers considering ChatGPT today.

---

Source: PCMag Australia ChatGPT