In the rapidly evolving field of artificial intelligence, each model update draws significant attention from industry professionals. The recent release of Google DeepMind's Gemini-exp-1121 has ignited a profound discussion about the evolution of AI capabilities.
Remarkably, this new model arrived just one week after the debut of Gemini-exp-1114, with Google announcing on November 21 that Gemini-exp-1121 demonstrated substantial improvements in coding, reasoning, and visual capabilities. The model quickly ascended to the top of the Arena leaderboard, with evaluation results showing superior performance across most metrics except style control.
The Timing Puzzle
This rapid succession of releases raises questions about Google's strategy. More intriguingly, OpenAI coincidentally updated GPT-4o to version GPT-4o-2024-11-20 just one day before Gemini-exp-1121's launch. This timing has fueled speculation about whether DeepMind strategically waited for OpenAI's move to position its new model as more competitive.
Testing Methodology
To assess Gemini-exp-1121's true capabilities, we conducted rigorous testing focused on logical reasoning and programming performance. Our evaluation leveraged the 302.AI platform for comprehensive assessment, building on previous observations of the model's impressive visual capabilities during Grok-vision-beta testing.
Accessing Gemini-exp-1121 on 302.AI
Chatbot Interface:
- Navigate to the 302.AI platform and select "Use Robot"
- Choose "Chatbot" and locate "Gemini-exp-1121" under the Gemini category in model options
- Enable real-time preview in settings to monitor model outputs
API Access:
- Select "Use API" and enter the "API Supermarket"
- Choose "Large Language Models" then "Gemini"
- Find Gemini-exp-1121's API interface and select "View Documentation" or "Online Experience"
Comparative Testing: Three Models Face Off
We conducted head-to-head comparisons between Gemini-exp-1121, its predecessor Gemini-exp-1114, and OpenAI's GPT-4o-2024-11-20, focusing on logical reasoning and programming capabilities.
1. Mathematical Comprehension
Prompt: "A 20cm brick sits on the ground. I place a 30cm flowerpot on top. The pot contains 10cm of soil with a 5cm seedling growing from it. What's the total height from ground to seedling tip?"
Results:
- GPT-4o-2024-11-20: Correct analysis but incorrect final answer
- Gemini-exp-1121: Clear reasoning with correct answer (35cm)
- Gemini-exp-1114: Failed to comprehend the problem
2. Logical Reasoning
Results:
- GPT-4o-2024-11-20: Correct analysis with detailed explanation
- Gemini-exp-1121: Incorrect analysis leading to wrong answer
- Gemini-exp-1114: Clear, correct analysis with proper explanation
3. Programming Capability
Prompt: "Create a 2048 game using front-end code in a single file."
Results:
- Gemini-exp-1114: Functional game with keyboard controls, though lacking polish
- Gemini-exp-1121: Static interface without functionality
- GPT-4o-2024-11-20: Keyboard-operable game with basic aesthetics but missing key features
- o1-preview (comparison): Complete implementation with start button, score tracking, and full functionality
Analysis and Conclusions
The testing reveals several key insights:
Mathematical Understanding: Gemini-exp-1121 demonstrated superior performance in mathematical comprehension, correctly analyzing contextual relationships rather than simply summing numbers.
Logical Reasoning: Surprisingly, the previous version (Gemini-exp-1114) outperformed its successor in logical analysis, suggesting potential regression in this capability.
Programming Performance: Gemini-exp-1121's inability to generate functional code raises concerns about its programming capabilities compared to both its predecessor and competing models.
Performance Regression or Experimental Limitations?
The apparent superiority of Gemini-exp-1114 in certain domains prompts questions about Google's development priorities. The findings suggest potential trade-offs in model optimization, where advancements in some areas may have come at the expense of others.
As experimental models, both Gemini versions demonstrate the inherent instability of cutting-edge AI development. The industry should monitor whether Google addresses these inconsistencies in future iterations.
Analyst Perspective: Balanced Evaluation of AI Progress
This case study underscores the importance of comprehensive, multi-dimensional evaluation when assessing AI models. Performance metrics can vary significantly across different capabilities, and experimental models often exhibit unpredictable behaviors.
The Gemini-exp-1121 release also invites reflection on AI development strategies. The pursuit of benchmark performance must be balanced with considerations of stability, reliability, and generalizability. These findings contribute valuable insights to ongoing discussions about responsible AI advancement.