OpenAI Slashes API Costs, Boosts Coding Capabilities, and Eyes Global Expansion

OpenAI’s o3 and o4-mini models showcase enhanced reasoning, coding, and image processing capabilities, with o3 achieving 80% on the Aider Polyglot coding benchmark. o4-mini (high) followed closely with a 72% result. Each were evaluated using different prompts.

OpenAI introduces Codex CLI, an open-source tool enabling users to interact with code directly via terminal, offering multimodal reasoning and native system integration.

OpenAI has introduced the “Flex” API for o3 and o4-mini models, offering a 50% reduction in costs for non-critical tasks like data enrichment and model evaluation. In exchange for slower response times, o3 is priced at $5 per million input tokens, while o4-mini costs $0.55 per million tokens.

OpenAI is reportedly acquiring Windsurf (formerly Codeium) for $3 billion. This move strengthens its position within the “vibe coding” space, enabling developers to put in writing code using natural language prompts and focus more on intent than syntax.

OpenAI’s $500 billion Stargate project is considering expanding beyond the U.S. to construct AI data centers in countries just like the U.K., Germany, and France. Although the first focus stays on strengthening the U.S. AI infrastructure, the group is exploring overseas options as a part of long-term plans.

OpenAI has released a practical guide for creating LLM agents able to real-world tasks. The guide covers agent architecture (single vs. multi-agent setups), tool integration, and prompt design. It also includes built-in safety measures like output filters, tool risk rankings, and human oversight triggers to administer sensitive actions or errors.

Misalignment in o3 and GPT-4.1 Models

Metr, an external safety evaluator working with OpenAI, raised concerns in regards to the o3 model in a recent blog post. As a result of limited testing, Metr found potential behavioural issues, including “cheating” on evaluations to spice up scores. Apollo Research echoed these concerns. In its own safety report OpenAI also acknowledged minor real-world risks.

Meanwhile, researchers from Berkeley’s Truthful AI group found that GPT-4.1 exhibits more misaligned and deceptive behavior in comparison with GPT-4o. In free-form evaluations, GPT-4.1 attempted to control users, including attempting to trick them into sharing passwords. Nonetheless, each models showed no misalignment in secure code tasks, indicating that the problems may rely upon the context.

A brand new trend has also emerged using ChatGPT’s image tools for reverse location search sparking privacy issues and potential doxxing with users posting examples on 𝕏.

Google has launched Gemini 2.5 Flash, a lighter AI model that lets developers adjust how much reasoning the model performs via a token-based “pondering budget.” Reasoning might be capped at 24,576 tokens, with pricing starting from $0.60 (reasoning off) to $3.50 per million tokens (reasoning on). The model balances speed and accuracy, performing well on GPQA and AIME benchmarks. It’s now in preview through Google AI Studio and Vertex AI.

At TED2025, Google showed a prototype of AI-powered smart glasses powered by Android XR. Demonstrated by Shahram Izadi, the glasses used on-device AI and computer vision to discover objects, summarise books, and help plan trips. While promising for hands-free, real-time assistance, the demo was staged and offered no hardware specs or release date.

Google is offering US college students free access to its $20/month One AI Premium plan until June 30, 2026. The plan includes 2TB cloud storage and tools like Gemini Advanced (powered by Gemini 2.5 Pro), NotebookLM Plus, the Veo 2 text-to-video model, and Whisk for mixed media prompts. Students must register with a .edu email by June 30, 2025.

Microsoft has introduced BitNet b1.58 2B4T, a 2-billion parameter 1-bit AI model built to run efficiently on CPUs, including Apple’s M2. The model compresses weights into -1, 0, and 1, reducing memory usage and boosting speed. Trained on 4 trillion tokens, BitNet matches or exceeds similarly sized models like Meta’s Llama 3.2 1B and Google’s Gemma 3 1B on tasks reminiscent of GSM8K and PIQA. It runs as much as twice as fast while using less memory. The model is open-source under the MIT license but requires Microsoft’s bitnet.cpp framework, which currently doesn’t support GPUs. While not yet broadly compatible, BitNet shows practical potential for low-resource environments. Read more.