You’re not alone if you’re wondering how well Google’s latest AI coding model is performing. The company’s Gemini 3.5 Pro has faced criticism after failing to meet internal coding standards, leading to a delayed launch. This highlights the challenges of using AI for complex tasks like software development.
Why Gemini 3.5 Pro Is Under Fire
You might be asking why a company like Google is struggling with its AI coding model. Reports suggest that Gemini 3.5 Pro didn’t perform as expected in internal evaluations, prompting a postponement. This comes after Google’s $2.4 billion acquisition of Windsurf AI, a company known for its strong coding capabilities.
The criticism is part of a broader debate about how well AI models handle real-world coding tasks. BenchLM, a platform that tracks AI performance, recently released its rankings. According to the data, models like Claude Mythos 5 and GPT-5.6 Sol lead the pack with scores above 80%. Open-source models like Qwen3.8 Max and Kimi K3 also show strong results, offering good performance at lower costs.
The Complexity of Coding for AI
You might be surprised to learn that coding is more complex than simple text generation. AI models need a deep understanding of logic, syntax, and problem-solving — areas where even the best models can struggle. BenchLM’s data shows that only a few models consistently perform well across multiple benchmarks, and Gemini 3.5 Pro doesn’t seem to be one of them.
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This highlights a key challenge: training AI to handle the nuances of coding. Unlike other tasks, coding requires precision and a strong grasp of programming concepts. For now, it seems that Gemini 3.5 Pro isn’t meeting those standards.
Google’s Continued Push in AI Coding
You might be wondering if Google is giving up on its AI coding efforts. The answer is no. The company is still using Gemini in coding interviews, piloting a program where candidates use it as an approved assistant. Meta has also been experimenting with similar features, showing that AI is becoming a bigger part of the hiring process.
This raises an important question: if AI is being used to evaluate coders, how reliable is it? And what happens when the models themselves aren’t up to par? Google’s documentation for its Gemini Enterprise Agent Platform suggests that the company is working on improving how AI assistants are evaluated. The platform includes tools for training models using specific methodologies, which could help refine their coding abilities over time.
The Future of AI in Coding
You might be thinking about what this means for the future. While some developers argue that AI can assist with coding, it’s not a substitute for human expertise. Others see the potential for tools like Gemini to streamline workflows and reduce repetitive tasks.
So where does this leave Google? The company has a lot to prove, but it also has the resources and talent to push forward. Whether Gemini 3.5 Pro will live up to expectations remains unclear — but one thing is certain: the race for the best AI coding model isn’t slowing down.
