📉 Switching from Claude to DeepSeek saved 62% of the budget
Firetiger has successfully transitioned its agentic workflows from Claude models to DeepSeek. To compensate for the difference in quality, developers applied specialized prompt engineering to address DeepSeek's issues with ignoring context. As a result, DeepSeek v4 Pro's accuracy reached 92%, nearly matching Claude Sonnet 4.6 (94%).
🌍 This case proves the possibility of effective LLM arbitrage: transitioning to cheaper models is possible without critical loss of quality when using deep evaluations (evals) and optimizing prompts for the specific weaknesses of the new model.
👤 A practical example of optimizing AI costs for business. It shows how to technically implement a transition from expensive flagship models to efficient alternatives through instruction refinement.
Source 1: https://blog.firetiger.com/migrating-from-claude-to-deepseek-without-breaking-everything/