Tech Insights 2025 Week 17
Johan Sanneblad
CEO @ TokenTek | Helping Businesses Automate with AI Agents & Generative AI
Last week OpenAI launched five new models: GPT-4.1 (including mini and nano variants), o3 and o4-mini, together with a new command-line tool called Codex CLI. You now have six models to choose from in ChatGPT: GPT-4o, GPT-4o with scheduled tasks, GPT-4.5, o3, o4-mini, and o4-mini-high. If you use the API you can add GPT-4.1, GPT-4.1-mini and GPT-4.1-nano to that list. So which one should you use? In my own experience and based on what I have read on multiple forums, GPT-4o is still the best model for everyday productivity. Ask it about documents, work with texts, get feedback, and use the memory function. It's quick and very good at most tasks. If you have difficult questions and want the agent to act autonomously - analyze images or documents, collect data over the Internet, write Python code for analysis, or even write Python code to create full Powerpoint documents, use o3. It's amazingly good at complex tasks! Just note that you only have 50 requests per week on a Plus account. GPT-4.5 should probably never have been released, it's slow and not fine-tuned well and will soon be removed and retired. Once your o3 requests are up then use o4-mini for the rest of the week (you get 50 messages per day for o4-mini-high and 150 messages per day for o4-mini). And if you use the API for coding you should definitely be using a combination of o3 for difficult tasks and GPT-4.1 for everyday coding requests.
To summarize, if you use ChatGPT on the web, use them in the following order:
If you use ChatGPT in the API for programming:
Among other news this week: JetBrains finally launched their Junie AI agent for their IDEs, so if you love PyCharm and Rider you now have something that at least resembles Cursor or GitHub Copilot in functionality. Microsoft announced Computer Use support for Copilot Studio, and Wikipedia launched a huge structured dataset for AI Training to combat scraper bots. Finally, if you have the need for custom classifiers for tasks like spam detection, moderation, intent recognition, and sentiment analysis, Mistral just launched their Classifier Factory that makes the process so much easier.
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THIS WEEK'S NEWS:
OpenAI Releases New Reasoning Models: o3 and o4-mini
The News:
What you might have missed (1): One major feature of o3 and o4-mini is that they can reason with images in their chain of thought. This means that instead of just trying to "translate" an image into a meta description and then switch over to text-based reasoning internally, o3 and o4-mini are able to reason about details in the image, searching for information online related to specific parts before answering in full. This is a complete game changer for complex image and document understanding, and also means the model is outstandingly good at geoguessing.
What you might have missed (2): For both o3 and o4-mini, OpenAI introduced Flex processing, which means that if you are OK with longer response times you will pay a significantly cheaper per-token price. I like this approach much better than the "thinking budget" approach that Google went with for Gemini 2.5 Flash (see below).
What you might have missed (3): OpenAI o3 scored 136 (116 in offline mode) on the Mensa Norway IQ test, the highest score ever recorded by an AI.
My take: These are the first models released by OpenAI that can function as agents, independently executing tasks by determining when and how to use the appropriate tools. They can browse the web, code in Python, do visual analysis and create images. User reports on forums and Reddit have been mixed, where some users report amazing performance and while others say they get worse results than GPT 3.5. It seems these models are very good at problem solving specific tasks, but not so good at creating high volumes of quality source code. For programming I'd recommend you stick with with GPT-4.1 or Claude 3.7 and maybe use these models for specific things like refactoring or solving difficult problems in your code base, preferably in combination with the new Codex CLI tool (see below). If you are developing AI agents these models should be on the top of your list.
Read more:
OpenAI's o3/o4 Models Show Significant Progress Toward Automating AI Research Engineering
The News:
My take: We are very quickly approaching a future where AI systems can contribute meaningfully to their own development. The 44% success rate on internal pull requests for o3 shows these models are approaching the capability to handle real engineering tasks, not just test cases. Today's models are still way too inconsistent to just let loose autonomously on a code base, but with increased context windows and higher success rates on internal pull request tasks we are quickly reaching a future where AI models will help significantly in creating future AI models.
Read more:
OpenAI Launches GPT-4.1 for Developers
The News:
My take: This is the first time OpenAI has released models with a 1 million context window, which is extremely useful in agentic settings. Performance-wise GPT 4.1 is behind o3, o4-mini, Gemini 2.5 Pro and Claude 3.7 on tests such as SWE-bench Verified. Compared to o3 and o4-mini mentioned above that excel at problem solving, the GPT-4.1 series is primarily targeted for controlled programming, i.e. generating large amounts of high quality software code. Preliminary user feedback has been good, with most people saying it's on par with Claude 3.7 and in some cases even better for coding.
Read more:
OpenAI Launches Codex CLI: Open-Source Terminal-Based Coding Agent
The News:
My take: OpenAI Codex CLI is very similar to Claude Code, released in February, but with multimodal inputs. Both tools run in the terminal and are mainly used for specific tasks like creating new projects or trying to solve difficult issues in a large code base. I still mostly use Cursor and Claude 3.7 for my daily tasks, but sometimes switch to Claude Code for tasks that require the better context understanding. From what I have read about Codex CLI it works very similar to Claude Code, and with OpenAI in discussions about buying Windsurf they will probably go the same route there - Codex CLI for specific tasks that require huge contexts and Windsurf for your day to day coding.
Read more:
JetBrains Launches Junie AI Coding Agent and Updates AI Assistant with Free Tier
The News:
My take: If you enjoy working in the JetBrains environments (like PyCharm or Rider) you probably have been glancing at GitHub Copilot or Cursor for their AI features. Julie is very similar to these tools, and is using Claude as the backend. Junie does not however seem to have specialized small models integrated in the environment for diffs and multiline autocomplete like Cursor, and several users on forums have complained about the performance of Junie. As a result the release seems pretty underwhelming. There is no mention about how many fast requests you have per month, so I am guessing it only uses slow requests (which seem to be the case when looking at early user feedback and the price of just $100 per year). Junie does not seem to support something like .cursorrules and .cursorignore, which are critical to make AI agents do exactly what you want. Still it's better than nothing, so if you're a JetBrains user then go ahead and give it a go. Just remember to be strict in your prompting and always review the code before merging. If you have used both Cursor and Junie please give me a DM and let me know your experiences.
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Recommended by LinkedIn
Microsoft Introduces "Computer Use" in Copilot Studio for UI Automation
The News:
My take: Computer Use sounds cool in theory until you try it out for real. Computer Use does behave better than the LLM on which it is based, and right now we have no LLMs that are good enough for the high-precision and high-performance computer use that Copilot Studio promises. I am guessing Microsoft uses GPT-4o or 4.1 to drive this feature (they haven't said anything about this), and based on that I can guess how well it will work (i.e. not good at all). The feature is not publicly available, it's only available to select testers that apply for early access. So take this news with a grain of salt, and let's see how it performs next month when Microsoft will demo it at Microsoft Build 2025 on May 19.
Read more:
Google Rolls Out Gemini 2.5 Flash Preview with Hybrid Reasoning Capabilities
The News:
My take: This is a new take for models where you now have a hybrid reasoning model that can be precisely controlled. Have a simple task? Set the thinking budget to zero to disable reasoning completely. Or set it to an arbitrary value to increase thinking capacity. While I understand how Google reasoned about this, I am not really sure this is the right way forward. I can see how it might work if you have an LLM that performs the same task over and over. But that is rarely the case in agentic environments. Sometimes it might need to think harder, and if your budget is cutoff then the agent won't be able to. So the only times this option is really usable is when you have an agent perform very similar tasks with the same documents expecting it to solve issues in the exact same way. Some users have proposed that you could use a pre-processing step where another model would be trained to decide how much reasoning budget a specific question requires. I don't think this is a good design decision and would rather have the LLM API decide the budget based on difficulty.
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Wikipedia Releases Structured Dataset for AI Training to Combat Scraper Bots
The News:
My take: If you need high quality content for model training and have considered to go scraping Wikipedia, then now hopefully this dataset will do the job for you. The dataset itself is around 113GB in size, available in English and French, and is based on the Wikimedia Enterprise html snapshots. Let's hope this helps to reduce the agent traffic to Wikipedia, and kudos to Kaggle for hosting it!
Read more:
Kling AI 2.0 Launches with Advanced Video Generation and Editing Capabilities
The News:
My take: Just when you thought text-to-video generation was getting ridiculously good with Google Veo2 and Runway Gen-4, Kling 2.0 is released showing a win-loss ratio of 182% against Google Veo2 and 178% against Runway Gen-4. The platform now ranks first in the Image to Video category with an Arena ELO benchmark score of 1,000, surpassing competitors like Google Veo 2 and Pika Art. The new Multi-modal Visual Language (MVL) framework is pretty cool, and if you have a few minutes I'd recommend you check out their web page for details. Early user feedback from forums have been really good, with the main negative being the price of around $2 for a 10 second clip, with many saying it's unaffordable for casual users. Kling however already has over 22 million users, with over 15,000 developers integrating their API in various apps.
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Mistral AI Launches Classifier Factory for Custom AI Classifiers
The News:
My take: Without Classifier Factory, creating a custom classifier is a complex, technical process requiring extensive expertise. You'd need to collect and preprocess large amounts of data, handle missing values and outliers, transform data into numerical format, select relevant features, choose an appropriate model architecture, tune numerous hyperparameters, train the model (which could take days), evaluate performance, deploy the model on your own infrastructure, and continuously maintain it as data patterns evolve. With Mistral's Classifier Factory, the process is much simplified: you prepare your data in a standardized JSON Lines format (with examples of text and corresponding labels), upload it to Mistral's platform, select the pre-optimized ministral-3b model, adjust just two hyperparameters (training steps and learning rate), and let Mistral handle the training and deployment automatically. Definitely check this one out if you are training your own classifiers today!
Meta to Train AI Models on EU Users' Public Content
The News:
My take: I really do not like these opt-out mechanisms for AI training and would much rather prefer opt-in, but I guess no-one would do that. If you are a Facebook or Instagram user you got a similar email to the one above last week, and I'm very curious if you even noticed that this was the email you should have interacted with to opt out of AI training from all your publicly available comments or posts. Did you catch it, and did you interact with it? Did you opt out? I'm curious, please leave a comment!
Claude Adds Research Feature and Google Workspace Integration
The News:
My take: Claude Research is like Deep Research in ChatGPT but with one important addition: [It can also search your internal work context]( Claude can search across both your internal work context and the web to help you make decisions and take action faster than before.)! Ask it to go through your latest meeting notes and add links to external services that might be relevant to the discussion, or to go through your latest market plan and evaluate it based on the most recent research. If you have ever copy/pasted information to ChatGPT Deep Research you know how much easier it would be if the LLM automatically could access the right documents (it actually builds up a vector database of your entire Google Drive). I can see why most organizations would not want to enable this, but for those daring enough to go for it there is a huge productivity potential here. Preliminary user feedback shows it works great for:
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Narrative Director, Writer and Copywriter
3wI’ve already used o3 for Python code, 4o just couldn’t handle. My custom writer toolkit now has several unique features that just wouldn’t be possible for me to develop on my own and cost weeks of devtime for fairly mundane work anyway. Now I can get those devs to help me with much more robust and qualified tasks, such as backend support, backup solutions and collaborative features - the difficult stuff. Whereas the basic product I could design myself. It’s a scenario where everybody wins.