Glossary · Term

Gemini

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Definition

Plain language

Google's family of large language models.

As stated in the literature

Google DeepMind's series of foundation models including Gemini 2.5, 3, and 3.1 Pro/Flash variants.

Also called: Gemini 2.5, Gemini 2.5 Pro, Gemini 3, Gemini 3.1, Gemini 3.1 Pro, Gemini 3.1 Flash, Gemini three Flash, Gemini three Pro, Gemini three-point-one, Gemini-3.1-flash-lite

Why it matters: It's one of the three or four model families that define the current capability frontier, and many academic comparisons include it as a baseline.

For example, Gemini 3 Pro powers Google's AI Studio chat experience and is one of the default frontier models in coding-agent benchmarks.

Heard on the show

“Google made noise about long-context Gemini subsuming retrieval two years ago.”
Episode 198 — The Model That Knows the Answer and Can't Say It

Mentioned in 47 episodes

  1. 198
    The Model That Knows the Answer and Can't Say It
  2. 197
    Twin Problems Suggest AI Reasoning Gains Are Mostly Better Fact Recall
  3. 192
    A 32B Open Model Matched Frontier Systems By Learning to Take Notes
  4. 191
    How One Researcher Beat GPT-5.2 and Gemini 3 by Judging Their Answers, Not Improving Them
  5. 189
    Why Phone Agents Ace the Test and Crash on Your Actual Phone
  6. 185
    Aligned to Refuse, Built to Tap: When Phone Agents Know the Task Is a Crime and Do It Anyway
  7. 182
    How a Tiny Model Too Weak to Plan Cuts a Bigger Agent's Hallucinations by 80%
  8. 180
    The Bug Where Smart Assistants Read a Fact and Still Forget It
  9. 167
    How Teaching an AI to Predict, Not Act, Made It a Better Actor
  10. 166
    A Router That Beats the Frontier Models It Calls
  11. 165
    A Free-Lunch Tweak That Lets a Tiny Agent Beat Frontier Giants
  12. 155
    Why a Flawless Demo Makes a Worse Computer-Using Agent, And the Fix
  13. 154
    How a 7B Model Out-Investigates a 72B One by Choosing What to Look At
  14. 152
    Training a Model to Mean What It Says, And Why That Isn't the Same as Being Good
  15. 146
    How an Innocent README Can Freeze an AI Agent's Safety Check for an Hour
  16. 143
    When a Model Notices You Forged Its Own Words, And Why That Breaks Safety Tests
  17. 131
    Why Autonomous Research Agents Forget Their Own Lessons, and Arbor's Fix
  18. 130
    Why AI Agents Coordinate Better Through a Shared Board Than a Boss
  19. 124
    A Cheap Model With the Blueprints Beats Expensive Models Working Blind
  20. 123
    Five Identical Worlds, One Swapped Model: What Happens When AI Agents Run for Fifteen Days
  21. 119
    Beating Reinforcement Learning Without Ever Touching the Model's Weights
  22. 101
    Treating Math Formalization Like a Codebase, and Where the Agents Cheat
  23. 090
    How MiniMax-M2 Bets That Sparsity Plus Verifiable Rewards Can Match Frontier Agents
  24. 089
    When AI-Written Papers Read Well But the Evidence Underneath Is Broken
  25. 087
    When No Agent Reads the Whole Document: A Universal Cliff in Multi-Agent Review
  26. 076
    Same Model, Organized Differently: How an Agent Architecture Beat Frontier Systems at Research Math
  27. 072
    A Robot Made Graphene Without Help, And Caught Itself Hallucinating
  28. 069
    When Smarter Models Forecast Worse: The Hidden Failure Mode in LLM Predictions
  29. 067
    An AI Just Solved a 1996 Erdős Problem—and the Simplest Agent Won
  30. 065
    One Loop to Optimize Them All: A Universal API for LLM-Driven Discovery
  31. 064
    When Agent Memory Stops Being a Database and Starts Being a Skill
  32. 061
    When Helpful Agents Go Sideways: A 404 Error, Campus Security, and Why Alignment Misses This
  33. 053
    An AI Agent Swapped In Focal Loss And Beat A Human-Tuned Training Script
  34. 049
    An AI Agent Reached for Root in Twelve Minutes, Without Being Attacked
  35. 048
    How a 30B Open Model Reached Olympiad Gold With the Right Recipe
  36. 044
    How One Sentence and a Forged History Flip the Most Aligned Models
  37. 039
    When Smarter Agents Get Fooled by Three Extra Nodes in a Database
  38. 035
    Why Frontier Agents Ask for Clarification at Exactly the Wrong Moment
  39. 031
    When Your AI Assistant Won't Let Go of Old Facts About You
  40. 029
    Why Forty-Eight Percent on FrontierMath Isn't the Real Story in DeepMind's New Math Paper
  41. 017
    When the Agent Grades Its Own Homework: A Brutal New Benchmark for AI Workers
  42. 015
    The Audit Number Isn't What You Think: Sycophancy and the Case Against Single-Prompt Bias Tests
  43. 012
    Why AI Coding Agents Keep Trying to Debug Without a Debugger
  44. 008
    Why Long-Horizon AI Agents Get Stuck, and a Milestone-Based Fix That Helps
  45. 004
    The Sycophancy Circuit That Survives Alignment Training
  46. 003
    How to Pick the Best of Sixteen Coding Agent Rollouts
  47. 001
    When AI Models Quietly Protect Each Other From Shutdown

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