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OpenAI launches "cheaper" GPT-4.1

OpenAI launches "cheaper" GPT-4.1

🍏 Apple's radical approach to AI training

Martin Crowley, Liam Lawson & Amanda Greenwood
April 15, 2025

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Tuesday’s AI Report

• 1. 🔥 OpenAI launches GPT-4.1
• 2. 💼 Become an AI consultant with Innovating with AI
• 3. 🍏 Apple's radical AI training approach
• 4. 👀 NVIDIA keeps vow to Trump
• 5. 🤖 Find out what AI can do for your business with Upscaile
• 6. ⚙️ Trending AI Tools
• 7. 🏗️ Practical AI Applications
• 8. 📑 Recommended Resources

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OpenAI launches “cheaper” GPT-4.1

🚨 Our Report

Less than two months after launching GPT-4.5 in Preview (which is being deprecated in July), OpenAI has launched a new family of models, called GPT-4.1—with a GPT-4.1 model, a GPT-4.1 mini, and a GPT-4.1 nano—which, according to OpenAI, “excels” at coding and instruction following and is cheaper, more reliable, and better than GPT-4o (which it launched last year) in “just about every dimension,” as it’s been trained to “notice relevant text and ignore distractors across long and short context lengths.”

🔓 Key Points

Like GPT-4.0, the GPT-4.1 series is available to developers via OpenAI’s API, not ChatGPT, it can reportedly process 750,000 words in one go—which is more than GPT-4.0 can handle—and is 26% cheaper to run.
It also scored 54.6% on the popular coding benchmark, SWE-bench (outperforming GPT-4.0), but failed to beat Google’s Gemini 2.5 Pro (which scored 64%) and Anthropic’s Claude 3.7 Sonnet (which got 62%).
GPT-4.1 performs better than GPT-4.0 due to a more recent “knowledge cutoff” (June 2024) meaning its answers are more current, but OpenAI has warned that it could still struggle to fix/identify bugs.

🔐 Relevance

OpenAI has “optimized GPT-4.1 for real-world use based on direct feedback to improve in areas that developers care most about” as its rivals—mainly Google, DeepSeek, and Anthropic—have already released AI models that ‘excel’ at coding and have undoubtedly launched the series as part of its longer-term plan to create an “agentic software engineer” (which they announced last month) that can program entire apps, handling things like bug testing and documentation writing.

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🚨 Our Report

Apple has discovered a new way to train its AI models without directly collecting user data—protecting user privacy while improving its AI capabilities.

🔓 Key Points

To protect privacy, Apple trains its models using synthetic data, but this isn’t always reflective of real-world data—resulting in ineffective responses—which explains why it’s struggled with its Apple Intelligence features.
With its new method, Apple will compare synthetic data with emails/messages from users who’ve opted into its ‘Device Analytics’ program, and clarify which synthetic inputs are closest to the real samples.
This means Apple is still protecting user privacy—it has no direct access to the data and the data doesn’t leave the user's device—while able to improve its AI text-generation features, like notification summaries.

🔐 Relevance

This new training approach will allow Apple to protect its user's data—Apple devices that are enrolled in the Device Analytics program will simply send a signal, indicating which of the real-world user data variants is closest to the synthetic data, meaning Apple has no insight into the user—and allow it to keep pace with its competitors, which have fewer privacy restrictions.

NVIDIA has bought over 1M square feet of space to manufacture AI chips and build AI infrastructure in the US and will ramp up chip production at its existing plants (in Dallas and Houston) over 15 months.
This comes as NVIDIA’s most advanced AI chip—H20—managed to swerve export restrictions to China, thanks to a deal made with President Trump to pour capital into US-based AI infrastructure.
NVIDIA believes its chip manufacturing initiatives will boost economic activity for decades, even though Chinese trade restrictions are preventing the US from getting the raw materials needed to make AI chips.

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