Picking the right model for the job: a 2026 decision cheat sheet

A year into using AI seriously, you start to notice that the question "which model is best" is the wrong question. Different models are best at different things. The right framing is: which model is right for the task in front of me?

This article is a practical cheat sheet for that question, calibrated to the AI landscape as of 2026. It covers the major models, where each one shines, where each one struggles, and the simple decision rules to pick fast without overthinking.

The current landscape

As of mid-2026, the practical choices for a beginner-to-intermediate user are:

Closed-source frontier models:

• GPT family (OpenAI) — GPT-5 / GPT-5 Thinking is the current default in ChatGPT. Strong all-rounder, excellent at conversation, multimodal, image generation in-chat. Reasoning sibling for harder problems.
• Claude family (Anthropic) — Claude Sonnet 4.5 / Opus 4.5 is the current strong default. Often considered the best writing voice and one of the strongest long-document readers. Extended thinking mode for harder problems.
• Gemini family (Google) — Gemini 2.5 Pro / Flash. Deepest Google Workspace integration, very long context, strong multimodal including video.

Open-weights models (you can run them yourself, or via hosted providers):

• Llama family (Meta) — Llama 3.3 / 4. The default open-weights workhorse.
• Qwen family (Alibaba) — Qwen 3 series. Strong on coding and multilingual tasks.
• DeepSeek — DeepSeek V3 / R1. Notable for the R1 reasoning model and aggressive pricing.
• GPT-OSS (OpenAI's open release) — OpenAI's open-weights variant, designed to run on consumer hardware.
• Mistral — Mistral Le Chat, Codestral. Strong European-headquartered option, particularly good for code.

Specialised:

• Reasoning models — o3, GPT-5 Thinking, Claude Extended Thinking, DeepSeek R1, Gemini 2.5 Thinking. Slower, more expensive, much better at multi-step logic and analysis.
• Coding-tuned — Claude is widely considered the strongest pure coding model in 2026; Qwen-Coder and Codestral are the strongest open-weights coding models.
• Multimodal-heavy — Gemini 2.5 leads on video and very long context; GPT family leads on in-chat image generation.

We will skip the open-weights options for the rest of this article (they have their own piece) and focus on the three frontier closed-source families plus the reasoning sibling models, which is what most beginners actually choose between.

Match the model to the task

The honest decision tree:

Drafting, brainstorming, conversation, summaries, everyday questions. Use the fast default model — GPT-5, Claude Sonnet 4.5, or Gemini 2.5. They are all excellent for this and the differences are smaller than the marketing would have you believe.

Serious writing — voice matters, nuance matters. Claude. By a noticeable margin, in many users' experience.

Hard analytical work — multi-step reasoning, planning, complex decisions, math, careful logic. A reasoning model. GPT-5 Thinking, Claude Extended Thinking, o3, or DeepSeek R1.

Code that is moderately complex. Claude or the default GPT. For agentic coding tasks (where the model writes, runs, and debugs code in a loop), Claude Code is the standard.

Anything multimodal — images, video, voice, mixing media. Gemini for video and the longest context windows; GPT-5 with image generation for slide decks and quick visuals; Claude for analysis of uploaded images and documents (but not for generating new images).

Anything where you need extreme integration with Google Workspace. Gemini, no question — its native Gmail / Docs / Drive integration is in another league.

Anything where you need integration with Microsoft 365. Microsoft Copilot, which uses OpenAI models under the hood but is wired into Outlook / Word / Excel / Teams.

Research with sources. Perplexity, or any of the three frontier models with web search turned on. Perplexity's defaults are best for cited research.

Very long documents (>100,000 words / very long codebases). Claude or Gemini, both of which handle long context noticeably better than the GPT family in 2026.

The decision rule

Most of the time the decision is simpler than this list makes it look. Two questions:

1. Is this task hard? Multi-step, requires careful logic, the answer matters precisely? → Reasoning model.
2. What domain? Writing → Claude. Anything Google → Gemini. Anything Microsoft → Copilot. Anything visual / image / mixed → GPT or Gemini. Otherwise → whichever default you already pay for.

That covers 90% of cases. The remaining 10% are either edge cases (specialised coding, research with sources, very long context) or matters of taste.

When to use a reasoning model — and when not to

Reasoning models — the "Thinking," "Extended Thinking," "o-series," "R1" variants — are the headline AI innovation of 2024–2026. They internally generate intermediate reasoning before answering, which makes them dramatically better at multi-step problems but slower and more expensive.

The mistake most beginners make is using them for everything, then complaining that AI feels slow. The opposite mistake is never using them and missing out on much better answers for hard tasks.

Use a reasoning model when:

• The problem has more than one step (planning, multi-stage analysis, multi-criteria comparison).
• The cost of being wrong is significant (financial, legal, professional decisions).
• You are debugging something that needs careful walk-through (a buggy spreadsheet formula, a strange piece of code, a contract clause).
• You are doing math, especially with units, dates, or precision.
• You are writing or reviewing complex code.

Stick with the fast model when:

• The task is conversational (chat, brainstorming).
• You are drafting or rewriting text where the voice matters.
• You are summarising or translating.
• You want to iterate fast — three drafts in a minute beats one perfect one in five.
• The answer is obvious to a smart 12-year-old.

A useful heuristic: if you would not pay a senior analyst to spend twenty minutes on it, do not use the reasoning model. If you would, do.

How to actually test this for yourself

Reading about model strengths is not a substitute for using them on your own work. Spend an afternoon doing the same task in two or three models side by side. Pick three tasks you actually do:

1. Draft a real email or message (compare voice, fluency).
2. Summarise a real document (compare faithfulness to the source, structure of the summary).
3. Do a real analytical task (compare depth, accuracy, willingness to say "I'm not sure").

After three head-to-head tests on tasks you care about, you will have an opinion calibrated to your work — not to a benchmark someone else cares about. This is the only kind of test that matters.

The cost angle

A practical note. As of 2026, the math looks roughly like:

| Tier | Cost / month | What you get | | --- | --- | --- | | Free | €0 | Limited usage of one default model | | Single subscription (ChatGPT Plus / Claude Pro / Gemini Advanced) | ~€20 | Strong access to one ecosystem | | Two subscriptions | ~€40 | Access to two ecosystems for picking the right tool per task | | Pro tier (ChatGPT Pro, Claude Max) | ~€200 | Unlimited use of the most capable models | | Pay-as-you-go via API (through any router) | Variable | Highest flexibility, no fixed cost |

For an individual professional in 2026, two subscriptions (most commonly ChatGPT Plus + Claude Pro) is the sweet spot. You get the best writing model and the best multimodal/image model, and you can use each for what it is genuinely good at.

If your employer covers a Microsoft 365 Copilot or Gemini Advanced license through work, use that for work tasks (the data handling is friendlier) and your personal subscriptions for personal use.

A common mistake to avoid

The single most expensive mistake people make in 2026: defaulting to one model out of habit and using it for everything. The person who only ever uses GPT-5 misses out on Claude's writing. The person who only ever uses Claude misses out on Gemini's NotebookLM. The person who only ever uses the fast model misses out on the reasoning models' depth.

The fix is not to switch obsessively. The fix is to build a small habit: when you are about to send a hard prompt, pause for two seconds and ask, "Is this the right tool for this?" If the answer is "I default to GPT but this is a long-document task that would suit Claude better," switch tabs. The cost is two seconds; the benefit is a noticeably better answer.

A small recap, model by model

A one-line summary you can keep in your head:

• GPT-5 / GPT-5 Thinking — the broadest all-rounder; best in-chat image generation; reasoning sibling is excellent.
• Claude Sonnet 4.5 / Opus 4.5 — best for writing, long documents, careful reasoning, and code.
• Gemini 2.5 Pro / Flash — best for Google Workspace integration, very long context, video, and multimodal tasks.
• Reasoning models (Thinking variants) — when the problem is hard and the answer matters precisely.
• Perplexity — when you want grounded research with sources.

Use the right one for the task and the experience of working with AI in 2026 is materially better than using one model for everything. The cost of learning the differences is small; the payoff is real on every hard task you do.