AI Coding Assistant Benchmark: A Horse Race of Models
Description
A bar chart from the Aider code editing benchmark, displaying the performance of various large language models. The vertical axis is labeled 'Percent completed correctly' and ranges from 0 to 90. The horizontal axis lists several AI models. The results show 'claude-3-5 sonnet-20241022' as the top performer with approximately 84% accuracy. It is followed by 'o1-preview' at around 79%, 'DeepSeek V2.5' at about 72%, and 'gemini-exp-1206' highlighted in a distinct red bar at roughly 69%. Two other Gemini models, 'gemini-exp-1114' and 'gemini-exp-1121', score lower, around 61% and 58% respectively. The technical context, as provided by the original post's caption, is that this chart represents a significant benchmark for AI coding capabilities. It reflects the ongoing 'model wars' among major AI players like Anthropic (Claude), OpenAI (o1), and Google (Gemini). For experienced engineers, this isn't just a chart; it's a snapshot of a rapidly evolving tool-space where today's leader could be tomorrow's legacy system
Comments
12Comment deleted
This benchmark shows which AI is best at editing code. The real-world benchmark is which AI is best at deciphering a three-year-old Jira ticket titled 'Fix the thing'
Nothing like a pastel bar chart to remind leadership that our entire Q3 roadmap depends on whichever model clears 70 % in a synthetic eval none of us volunteered to maintain
Gemini-exp-1206 in pink because it's embarrassed about being the only model update that made things worse - the classic 'we fixed the bug that was accidentally making it work' deployment
When your experimental model's version number goes up but the accuracy goes down - it's like deploying to production on a Friday, except the rollback takes three months and costs millions in GPU hours. The gemini-exp-1206 sitting there in pink is basically the architectural decision you defended in the design review that everyone's now too polite to mention in retros
Amazing how a 3% delta with no sample size or confidence intervals can trigger a platform-wide model swap, three ADRs, and a weekend of prompt‑router whack‑a‑mole
All these bars are probably inside the confidence interval we didn’t plot - yet procurement will mandate the pink one for ‘strategic alignment.’
Gemini-exp: Optimized for exponential hype, minimal completion
Claude's character limits kinda limit its coding usefulness tbh Comment deleted
Aider Code editing benchmark Aides for everyone! Comment deleted
Why are devs the only ones so passionate with making themselves obsolete 🤔 Comment deleted
That would require clients to a) know what they want and b) be able to clearly articulate that Comment deleted
Not agree, o1 made better results for me Comment deleted