Choose ChatGPT if…
- Broad general capability across reasoning, code and everyday tasks
- The deepest tooling ecosystem: code execution, data analysis, image generation, voice
Broad tooling against the largest context and the deepest multimodal reach.
ChatGPT has the richer tool ecosystem and the more consistent everyday answer. Gemini has the larger context window, the broadest multimodal understanding, and search grounding built in.
Last updated 22 September 2026
The quick answer
Start with the job you need to do. You do not need a single winner.
Rileva’s answer: use Auto when you want the right model chosen for the task, or Council when you want both perspectives compared.
At a glance
The facts that matter most, side by side.
| Feature | ChatGPTOpenAI | GeminiGoogle |
|---|---|---|
| Consumer price | Free tierPlus around $20/monthPro tier for heavy use | Free tierGoogle AI plans from around $20/month |
| Free tier | Yes, with lower limits and a smaller model | Yes, generous relative to peers |
| Flagship model family | GPT-5 series | Gemini 3 family |
| Web & research | Built-in browsing with citations | Google Search grounding built in |
| Coding | Strong across languages, with a code interpreter and agent-style tooling | Strong, with particularly good long-context code reading |
| Files & documents | File uploads, spreadsheets and data analysis in-chat | Very large context windows; PDFs, slides and sheets |
| Voice & multimodal | Voice conversations, image input and image generation | The broadest: image, audio and video understanding, plus image generation |
| Long-context work | Large context on paid tiers; strongest on structured, staged work | Industry-leading context size for mixed-format material |
Same prompt
A clear example of how the two models approach the same brief.
The prompt
“Here are 40 pages of board slides and a recorded 20-minute walkthrough. Summarise the three risks management is downplaying.”
Works cleanly through the slides and produces a sharp, well-argued three-risk summary, but asks for a transcript of the recording rather than working from the audio itself.
Takes both the slides and the recording together, and notes that one risk is stated more cautiously on the call than in the deck — a difference only visible across both formats.
How their thinking differed
The gap was input handling, not reasoning: Gemini could read the mixed material directly, while ChatGPT needed the recording converted first.
Illustrative example, not benchmark data.
Balance
Honest strengths and the trade-offs that come with them.
By task
Choose a task to see where each model’s working style fits.
Comfortable across most stacks, and unusually good at multi-step technical work where it can run code, inspect the result and iterate. Strong at debugging from a stack trace and at turning a vague requirement into a plan.
Good general coding, and especially useful when the task means reading a very large amount of code or mixed material at once before changing anything.
Browses the live web and cites what it used. Best when the question needs synthesis across several sources rather than a single authoritative lookup.
Grounded in Google Search, which makes it a practical choice for fresh, factual lookups and for questions where recency matters more than depth.
Gemini grounds in Google Search; ChatGPT browses and cites. The difference shows most on very recent factual questions.
Fast, clear and consistent, and excellent at structured formats — briefs, summaries, outlines. Distinct voice usually needs explicit direction.
Competent and quick, and strong when the writing has to be built from supplied material — reports, summaries, structured documents.
Handles uploaded files and spreadsheets well, and can compute over them rather than only reading them. Very long inputs are better split into staged passes.
Its clearest advantage: very large mixed inputs, including PDFs, slides and images, handled together without splitting them up.
If the input is a large mixed set — PDFs, slides, images, video — Gemini usually needs less preparation work.
Produces a wide spread of options quickly and organises them without being asked. Good for divergence, then narrowing.
Wide-ranging and fast, and willing to bring in current context from search rather than working only from training data.
Rileva
Auto chooses for youRileva routes your task to the model that fits it.
Council compares themSeveral models answer, then Rileva shows where they agree and differ.
FAQ
Keep reading
ChatGPT vs Claude
Different strengths. Different workflows. Compare them by task.
ChatGPT vs Grok
A polished general assistant against a blunt, real-time one.
Claude vs Gemini
The careful writer against the widest context window.
Claude vs Grok
Considered and careful against fast and direct.
Gemini vs Grok
The broadest input against the freshest information.