This week's edition covers affordable advanced reasoning with Gemini's Deep Think mode, Claude 4's new extended thinking features, research revealing how AI teammates boost individual output by 60%, and retrieval practice techniques that improve learning outcomes by 30%.
🧠 Gemini's Deep Think Mode Competes with OpenAI's Reasoning Models
What it is: Google's new "Deep Think" mode for Gemini 2.5 Pro uses "parallel thinking techniques" to reason through problems step-by-step before responding, putting it in direct competition with OpenAI's new O3 reasoning model.
What's new: The benchmarks show Gemini holding its own against much more expensive competitors. On the AIME 2025 mathematics competition, Deep Think scored 83% (vs OpenAI O3's 88.9% and Claude's 49.5%). More telling is the cost difference: Gemini costs $2.50 per million input tokens compared to O3's $10.00—making it four times cheaper for similar reasoning performance. The system shows its work by thinking through multiple reasoning paths before settling on answers, and you can watch this process happen in real-time.
Why it matters: This democratizes access to advanced reasoning capabilities. Instead of paying premium prices for top-tier reasoning models, you get 90%+ of the performance at a fraction of the cost. For complex analysis, debugging code, or working through multi-step problems, you now have an affordable option that actually shows its reasoning process. The transparency means you can follow the logic and catch potential errors, rather than trusting a black box.
🤖 Claude 4 Delivers Extended Thinking and Better Coding Performance
What it is: Anthropic's latest AI models—Claude Opus 4 and Claude Sonnet 4—represent the company's most capable AI assistants to date. Claude is one of the leading conversational AI platforms that competes directly with ChatGPT and other large language models.
What's new: The standout feature is "extended thinking"—Claude can now take longer to reason through complex problems before responding, similar to how humans might pause to think through difficult questions. Both models can also use tools like web search during this thinking process. Claude Opus 4 leads on coding benchmarks, scoring 72.5% on SWE-bench (a test of real software engineering tasks), while Claude Sonnet 4 offers a significant upgrade from the previous version with improved instruction-following and reasoning capabilities.
Why it matters: Extended thinking could change how we approach complex tasks with AI assistance. Instead of getting quick but potentially shallow responses, users can now request deeper analysis on challenging problems—whether that's debugging code, analyzing complex documents, or working through multi-step reasoning tasks. The improved coding capabilities make these models more valuable for anyone learning programming or working on technical projects, not just professional developers.
More AI everywhere doesn’t mean better results. Small teams of AI experts beat mass licenses. Attackers now use AI to run cybercrime at machine speed. Leaders can’t keep up. Tools still forget us. The winners? Focused expertise and hybrid AI, not AI everywhere.
You've built something and you need to know if it works. So you do what's sensible—you ask an LLM to grade it. Factual accuracy, code quality, agent outputs. The machine judges the machine, and you get a number you can act on. Except that number
Imagine you have a bunch of teams, some with AI, some without, and some where everyone gets their own AI. Researchers ran a big experiment with over 400 people to see what actually happens when you mix and match humans and AI in different ways.
Here’s what they found:
