Dans April 2026, AI companies released new tools very quickly, almost like running a marathon at sprint speed. This can feel confusing or overwhelming. But the main change is important: these tools are not only “chatbots” anymore. They are becoming work tools that can create things we use every day, handouts, summaries, visuals, forms, and drafts for decisions.
The question “Which AI model is best?” usually comes up during real tasks. For example: writing a patient handout that is easy to understand, building a study web page, or testing a new intake form before funding deadlines. The danger is that AI can produce something that looks clean and confident, before we have checked if it is correct. So we need strong clinical habits: be clear about uncertainty, save versionset double-check with trusted sources and real users.
Here are the recent updates people are talking about. OpenAI released ChatGPT Images 2.0 on Le 21 avril 2026, and also published a safety document explaining risks of realistic or misleading images. Anthropique released Claude Opus 4.7 and introduced Claude Design (a “canvas” tool for making visual assets) as a research preview on April 17, 2026. Google released Gemini 3.1 Pro (Aperçu) on February 19, 2026et Gemini 3.1 Flash Lite (Preview) on March 3, 2026.
Model comparison
- Prix = estimated cost to use the model (per 1 million tokens).
- Speed = how fast it writes (tokens per second).
- Latency (TTFT) = how long it waits before it starts answering (lower feels faster).
- Intelligence Index = a benchmark score (higher usually means stronger reasoning), but it’s not the only thing that matters.
| Model | Company | Context window | Intelligence Index | Price (USD / 1M tokens) | Output speed (tokens/s) | Latency (TTFT, s) |
| GPT-5.5 (hauteur) | OpenAI | 922k | 60 | 11.25 | 74 | 63.19 |
| GPT-5.5 (élevé) | OpenAI | 922k | 59 | 11.25 | 78 | 28.01 |
| Claude Opus 4.7 (max.) | Anthropique | 1M | 57 | 10.00 | 48 | 17.57 |
| Gemini 3.1 Pro (Aperçu) | 1M | 57 | 4.50 | 116 | 21.53 | |
| Gemini 3.1 Flash Lite (Précédent) | 1M | 34 | 0.56 | 313 | 5.08 |
A key shift is that AI now creates “objects we think with.” That means not only text, but also prototypes, slide decks, intake screens, and structured case summaries. These outputs can help a team work faster and collaborate better. But they can also “freeze” early assumptions: if something is easy to generate, it may become easy to test, fund, and deploy, even if it is not the best option clinically.
This is why cost and speed matter, not only “how smart” the model seems. Some models may be strong at reasoning but feel slow in real work because they take longer to start responding. In clinic-related workflows, if a tool feels slow, teams often stop using it, even if it is technically better.
So what is the “best” model? A practical way to decide is to think about your main risk. If your biggest risk is conceptual or factual mistakes, you might accept higher cost or slower performance, and then add careful human review before anything reaches a client. If your biggest problem is volume (too many notes, forms, translations), a faster and cheaper model can be reasonable, if you use templates, rules, and review steps.
The biggest ethical risk starts when AI creates something that looks “finished,” like a polished handout, a slide deck, or a clean user interface. When something looks professional, people trust it more, sometimes too quickly. That is why responsibility stays with humans: say when AI helped, keep track of prompts/versions/sources, and test materials with real users (clients, families, staff). If AI shapes care pathways, then accessibility, language, cultural fit, and data handling become clinical quality issues, not just tech details.
The updates will keep coming. The safest stance is not “never use AI,” and not “trust it because it’s new.” It is: generate fast, but interpret slowly. Choose tools based on where errors could cause harm, and place checks exactly where harm would concentrate.
If you want to follow updated numbers for price/speed/latency, the comparison source used here is Artificial Analysis’ leaderboard: https://artificialanalyse.ai/leaderboards/modèles