LLM API Price Drops: How to Cut Costs by 50%

OpenAI and Anthropic have slashed LLM API prices, with GPT-4o input dropping from $5 to $2.50 per million tokens and output from $15 to $10, while Claude 3.5 Haiku now costs $0.25 per million input tokens, enabling users to cut costs by up to 50% through model migration and prompt optimization.

LLM API Price Drops: How to Cut Costs by 50% Claude /en/tags/claude/ 3.5 Sonnet. Here’s what actually changed and how you can lock in those savings right now. OpenAI slashed GPT-4o input pricing from $5 to $2.50 per million tokens, and output dropped from $15 to $10. Anthropic matched by cutting Claude 3.5 Sonnet input by 50% and output by a similar margin. The real kicker: Claude 3.5 Haiku now costs $0.25 per million input tokens, making it competitive with much smaller models. If you’re building anything real-world with LLMs, you can’t ignore these numbers. Here’s my step-by-step approach to immediately benefit: 1. Audit your current model usage. Check which endpoints you call most. If you’re using GPT-4 Turbo for casual chat, migration to GPT-4o new pricing is a no-brainer. 2. Switch model strings in code. For Python, change: Old: expensive model="gpt-4-turbo" New: same quality, half price model="gpt-4o" For Anthropic, switch to Claude 3.5 Sonnet latest : model="claude-3-5-sonnet-20241022" 3. Optimize prompts for token efficiency. With lower prices, you might relax, but each token still costs. Use this practical tutorial tweak: add “Answer concisely” in system prompts and trim few-shot examples to the minimum viable set. 4. Enable response streaming. Both providers offer streaming. It doesn’t reduce token count, but it improves perceived latency. I use stream=True in my OpenAI calls and stream=True in Anthropic’s messages API.5. Set up budget alerts. I use API dashboards to notify when spend exceeds thresholds. With the price cuts, my old limits were too low — I doubled them and saw no bill spike. The biggest surprise was how much I could push Haiku. For document summarization, I switched from Sonnet to Haiku and fine-tuned the prompt. Quality remained close, but cost per document dropped from $0.015 to $0.002. Over 50,000 documents a month, that’s real money. Of course, price cuts alone don’t fix poor prompt engineering. If your prompts are bloated, even cheap tokens add up. So pair this pricing shift with a deep dive into message structure — trim system instructions, remove redundant context, and use output formatters to force short answers. I wrote a complete guide on this for my team, and the combination of cheaper models + Sam Altman's White House Talks: A Call to Decelerate AI? 6h ago /en/news/4379/ AI Safety: Why Sandbox Escapes Are a Wake-Up Call 13h ago /en/news/4338/ Claude Code: My Take on the Rogue Agent Incident 14h ago /en/news/4328/ The Death of the Open Paper: Why AI Startups Stopped Publishing 17h ago /en/news/4308/ Frontier AI Development: The Case for Coordinated Governance 17h ago /en/news/4305/ GLM-5.2 Now Tops Open-Weight Charts 2d ago /en/news/4019/ Next GPT-5.6 Sol: When the Model Tunes Its Own Inference → /en/news/4416/ All Replies （0） No replies yet — be the first