Post by Angad Yennam

AI engineers and tech leaders you're probably overpaying for LLMs by 10–100×. I spent the last week mapping 30 foundation model platforms across pricing, benchmarks, and real API costs. The gap is staggering. Here's what the data actually shows: DeepSeek V3.2 scores 90.8 on MMLU nearly matching GPT-5.2's 93.1 at $0.28/M tokens vs $1.25/M. That's a 4.5× performance-per-dollar advantage before you even optimize your stack. Kimi K2.5 hits 76.8 on SWE-Bench (coding). Claude Opus 4.6 leads at 80.9. Both beat every model from 18 months ago that cost 20× more. The real insight? Benchmark leaders and cost leaders are converging fast. The "pay premium for quality" rule is breaking down in 2026. If you're architecting RAG pipelines, fine-tuning workflows, or running inference at scale your model selection decision is now a financial engineering problem, not just a technical one. I mapped the full breakdown: free tiers, API ladders, SWE-Bench, MMLU, and sovereign AI options (Aleph Alpha for EU, on-device for Apple/Samsung) all 30 platforms, foundation models only. What's your current go-to model stack and have you run a cost-vs-benchmark audit recently? #deshawindia #LLM #AIEngineering #MachineLearning #DeepLearning #RAG #AIFinance #DataScience #QuantFinance #Innovation #NeurIPS #GenerativeAI #Transformers JPMorganChase The D. E. Shaw Group