ChatGPT vs Claude vs Gemini: Which AI Model to Choose in 2026?
Published on April 22, 2026
ChatGPT or Claude? Claude or Gemini? Gemini or Mistral? If you're asking this question, you're approaching it from the wrong angle.
There is no "best AI model" in 2026. There is the best model for your task. A model that excels at writing may be average at code. A model that champions reasoning may be slow and expensive for a simple question.
This guide compares the four main models — GPT-5.5, Claude 4.7, Gemini 3.1 and Mistral — on real-world everyday use cases: writing, code, reasoning, image, document analysis. With up-to-date data, real benchmarks, and a pragmatic verdict.
This article is part of our prompt engineering series
It expands on technique #10 from our complete guide: How to Write Good Prompts — choosing the right model.
Models at a Glance
Before diving into the details, here's a quick overview of each model's strengths as of April 2026.
| Model | Publisher | Key Strength | Max Context | Solo Price |
|---|---|---|---|---|
| GPT-5.5 | OpenAI | Versatility, reasoning, plugins | 256k - 1M tokens | $20/month |
| Claude 4.7 Opus | Anthropic | Code, long-form writing, analysis | 200k - 1M tokens | $20/month |
| Gemini 3.1 Pro | Multimodal, factual knowledge | 1M tokens | $19.99/month | |
| Mistral Large | Mistral AI | Speed, conciseness, open-source | 128k tokens | ~€15/month |
Each model has its strengths. The table above is a starting point — the following sections detail performance by use case.
NB: Some limitations vary depending on the plan. For example, Claude 4.7 Opus has a 200k token context when used with a €20/month subscription, but can go up to 500k tokens in enterprise mode and even 1M via the API.
NB2: For the following analyses and benchmarks, some recent models (GPT 5.5, Claude 4.7) are not yet integrated. We will therefore use the previous versions (GPT 5.4, Claude 4.6) for the comparison.
Writing and Content Creation
For writing tasks (emails, articles, LinkedIn posts, professional documents) the models are not created equal.
Claude 4.7 is widely recognized as the best for long-form, structured writing. Its 1-million-token context allows it to maintain coherence across very long documents. It produces a natural style with nuance and depth.
GPT-5.5 is the most versatile. It follows style instructions precisely and excels in short to medium formats: emails, summaries, rewrites. Its tendency to be verbose can be an advantage or a drawback depending on the context.
Gemini 3.1 is the most factual. It tends to cite sources and stay close to the facts. It's a solid choice for content that requires accuracy (technical articles, reports).
Mistral shines through its conciseness. When you want a direct answer without frills, it's the most efficient.
| Need | Best Choice |
|---|---|
| Long blog article | Claude |
| Professional email | GPT or Claude |
| LinkedIn post | GPT |
| Factual summary | Gemini |
| Quick, direct answer | Mistral |
Code and Development
Code is one of the areas where differences are most measurable thanks to benchmarks.
Claude 4.7 Opus leads the SWE-bench ranking with a score of 87.6% — this is the reference benchmark that measures a model's ability to solve real bugs in open-source repositories. Developers favor it for refactoring, code review and complex function generation.
GPT-5.5 remains very strong, especially for quick code generation and concept explanation. Its plugin ecosystem (Code Interpreter, web access) makes it a complete development tool.
Gemini 3.1 has made significant progress on code and now rivals GPT for standard tasks. Its native integration with Google Colab and Android Studio is an advantage for developers in the Google ecosystem.
Mistral is a good choice for simple to medium code tasks, with the advantage of speed.
| Need | Best Choice |
|---|---|
| Solve complex bugs | Claude |
| Generate code quickly | GPT or Claude |
| Explain code | GPT |
| Android / Google Cloud development | Gemini |
| Simple, fast tasks | Mistral |
Reasoning and Analysis
Complex reasoning tasks — problem-solving, strategic analysis, mathematics, logic — are the playground of so-called "thinking" or "reasoning" models.
The LMSYS Chatbot Arena ranking, the reference for human evaluation, as of April 2026:
| Rank | Model | Elo Score |
|---|---|---|
| 1 | Claude 4.7 Opus | 1503 |
| 2 | Gemini 3.1 Pro | 1493 |
| 3 | GPT-5.4 | 1481 |
On the MMLU benchmark (general knowledge measure), Gemini 3.1 leads with 94.1%, followed by GPT-o1 (83.9%) and Claude 4.6 (89.1%). We can notice that GPT-5.4 score less (87.5%) than GPT-o1 on this benchmark.
In practice, the three models are very close on reasoning. The difference often comes down to the clarity of explanation rather than the accuracy of the result. Claude tends to detail its reasoning, GPT to be more concise, and Gemini to cite sources.
Thinking Mode
Recent models offer a "deep reflection" mode (Thinking/Reasoning) that significantly improves results on complex problems. This mode is slower but more accurate — ideal for strategic analyses or mathematical problems.
Vision and Multimodal
Image and document analysis has become a standard feature, but not all models are equal.
Gemini 3.1 is the undisputed leader in multimodal. Designed from the ground up as a native multimodal model (not a module added as an afterthought), it excels at analyzing images, videos and complex documents. Its 1-million-token context window allows it to analyze very long documents.
Claude 4.7 offers solid vision capabilities, particularly effective for PDF document and table analysis. Its 1-million-token window makes it performant on large documents.
GPT-5.5 offers competent vision with the advantage of integration into the OpenAI ecosystem (DALL-E, plugins).
| Need | Best Choice |
|---|---|
| Analyze an image or video | Gemini |
| Read and summarize a long PDF | Claude or Gemini |
| Extract data from a table | Claude |
| Describe an image in detail | Gemini or GPT |
Image Generation
Image generation has made spectacular progress in 2026. This benchmark shows the current leaders :
GPT-Image (integrated into ChatGPT) is currently the leader for text-to-image generation. Quality, coherence and instruction-following are above the competition for most use cases.
Gemini can also generate images, but with generally lower quality and control than GPT-Image.
Claude does not generate images natively.
Beyond these integrated models, specialized models like Flux and Nano Banana offer complementary styles and capabilities. The cost is also a factor to consider: GPT-Image performs better than Gemini but costs almost 3x more for each generated image.
Access all image models
On Haloon, you have access to GPT-Image, Flux, Nano Banana and other image generation models. To master image prompts, check out our complete image generation guide.
The Real Cost: Price Comparison
This is where the math gets interesting. If you use multiple models (and you should), subscriptions add up fast.
| Setup | Monthly Cost |
|---|---|
| ChatGPT Plus only | $20/month |
| Claude Pro only | $20/month |
| Gemini Advanced only | $19.99/month |
| ChatGPT + Claude | $40/month |
| ChatGPT + Claude + Gemini | ~$60/month |
| Haloon (all models) | €15/month |
With a single Haloon subscription, you get access to GPT-5.5, Claude 4.7, Gemini 3.1, Mistral and many more — for less than a single ChatGPT Plus subscription.
Beyond the price, it's also about productivity: one conversation history, one interface, no need to switch between tabs.
Our Verdict: Which Model for Which Task?
After comparing each model's strengths, here's our recommendation by task:
| Task | 1st Choice | 2nd Choice |
|---|---|---|
| Long-form writing (articles, reports) | Claude | GPT |
| Emails and short texts | GPT | Claude |
| Code and debugging | Claude | GPT |
| Complex reasoning / math | Claude | Gemini |
| Image and video analysis | Gemini | GPT |
| Factual research with sources | Gemini | GPT |
| Image generation | GPT-Image | Flux / Nano Banana |
| Quick, concise answers | Mistral | GPT |
| Long document analysis | Claude | Gemini |
The reality is that no single model dominates across all domains. Moreover, each new version reshuffles the cards and the strengths of each provider (OpenAI, Anthropic, Google, Mistral, etc.) evolve every 2 to 3 months. Add to that the fact that some benchmarks are very subjective and results can vary depending on the prompts used, your personal preferences, etc...
The most effective setup in 2026 is to have access to all models and choose the right tool for each task.
The Haloon trick: compare in one click
On Haloon, the Reprompt button lets you send the same message to another model in one click. It's the fastest way to find the model that best answers your need — without juggling between tabs. For the price of a single subscription, you get access to all of them.
Summary
| Model | #1 Strength | Relative Weakness | Ideal For |
|---|---|---|---|
| GPT-5.5 | Versatility | Sometimes verbose | Daily use, images |
| Claude 4.7 | Code + writing | No images | Dev, long-form writing |
| Gemini 3.1 | Multimodal + facts | Less natural writing | Research, visual analysis |
| Mistral | Speed | Less powerful reasoning | Simple, fast tasks |
Go further
- How to Write Good Prompts — 10 techniques to get the most out of any model
- The Persona Pattern — how to get expert-level answers
- How to Generate Beautiful Images with AI — 7 techniques for effective image prompts