Why a decision tree instead of just "use the best one"?

Because "the best one" depends on the task, and most readers ask the question for a specific task type rather than in the abstract. A decision tree lets you answer in two questions ("is the document long? is the output structured?") instead of reading a 2,000-word review.

What if my task doesn't fit the tree?

Default to Claude 4.7 — it's the safest single-model choice in 2026 for general work. If the default doesn't produce a good answer, try GPT-5 next. Gemini 2.5 Pro is the third choice in the general case; first choice for image-text and Google Workspace tasks.

Does this change with new model releases?

The structure of the tree (long-doc → Claude, structured-output → Claude, vision-text → Gemini, broad-utility → GPT) has been stable since late 2024. The names at the leaves will change as models rev. The branching questions probably won't.

When to use Claude vs GPT vs Gemini (a decision tree)

If you've ever stalled at the model-picker because "I don't know which is best for this", a decision tree saves you the meta-question. The tree below maps task categories to recommended models in mid-2026. Two yes/no questions and you have your answer — no more "let me try this in all three" tax on every prompt.

The decision tree

Is the input longer than 30 pages?
├── Yes  → Claude 4.7
└── No
    Is the input an image with text (screenshot, scan, table)?
    ├── Yes  → Gemini 2.5 Pro
    └── No
        Is the output strictly structured (JSON, format-bound, instruction-heavy)?
        ├── Yes  → Claude 4.7
        └── No
            Is the task agentic (multi-step tool use, autonomous workflow)?
            ├── Yes  → GPT-5
            └── No
                Does the task involve image generation, audio, or live web?
                ├── Yes  → GPT-5
                └── No
                    Default → Claude 4.7 (with GPT-5 as second choice)

Three honest defaults: Claude for long-doc and structured work, Gemini for vision-text, GPT for agentic and multimodal output. For everything else, Claude is the safest single-model bet in 2026 — but the gap is small and reasonable people disagree.

Why these branches

Long input → Claude. Claude 4.7's 200K context is the only one in this group available across all plan tiers. GPT-5 and Gemini have similar nominal context windows, but the retrieval quality past page 30-40 is noticeably worse in our tests.

Image with text → Gemini. Gemini 2.5 Pro's OCR + table extraction is the most accurate of the three. Claude and GPT can do it; Gemini does it better with less hand-holding.

Structured output → Claude. Claude follows exact output constraints more reliably. If you say "respond with valid JSON only, no preamble", Claude obeys; GPT-5 sometimes adds a one-line explanation before the JSON, and Gemini occasionally drifts.

Agentic / tool use → GPT-5. GPT-5 plans multi-step tool sequences with the least friction. Claude is competent here but tends to over-narrate its plan, which slows agent loops.

Image gen / audio / web → GPT-5. GPT-5 has native image generation, voice mode, and browsing built in. Claude has none of these as of mid-2026; Gemini has image gen and some Google-Search-flavored browsing.

The shortcut for multi-model users

If you've decided this kind of per-task model picking is too much overhead — and you'd rather have a tool that just makes the switch easy — oran.chat puts all three behind one composer with a per-message model picker. The decision tree above becomes a one-click choice instead of a "pick a tab" dance.

What's next

This decision tree is the practical complement to the more detailed Claude 4.7 vs GPT-5 and Claude vs Gemini 2.5 Pro head-to-heads — read those if you want the rubric behind the branches. The full landscape across seven tools is in The 7 best ChatGPT alternatives in 2026 (tested). More multi-model thinking in Comparisons.