AI is becoming a bargain hunter's market, with a few luxury models on top

AI and ML

Inference is become a commodity except for frontier models

The price of AI tokens is fluctuating widely, with some becoming cheaper and others more expensive, leaving users of AI services struggling to assess if the price is right.Aman Panjwani, an AI engineer based in India, says that GPT-4-class model output cost about $20 per million tokens in late 2022. Today, equivalent capability costs about $0.40, a 55x decline in less than four years, he said, citing Introl's December 2025 unit-economics analysis."When DeepSeek released its R1 reasoning model in January 2025 at $0.55 per million input tokens and $2.19 output – against OpenAI's o1-preview at $15 and $60, launched just four months earlier – the entire market repriced overnight," Panjwani said in an analysis provided to The Register. "A 97 percent discount tends to do that."

During this same period, prices for cutting-edge frontier models surged. "OpenAI doubled the price of GPT-5.5 to $5 input and $30 output per million tokens – that's on its own pricing page," said Panwani. "Google's Gemini Flash 3.5 arrived three to six times more expensive than the model it replaced."

The recent release of Anthropic Claude Sonnet 5 continues that trend. Even though its per-token price is lower than Claude Opus 4.8, it uses more tokens to produce the same results. Anthropic's Mythos and Fable models are also quite costly, when available.Also this year, Anthropic moved corporate customers away from per-seat pricing to metered pricing and limited permitted uses of its subsidized subscription plans. Panjwani argues such moves show the token market is splitting in two, with commodity inference heading toward zero even as frontier inference costs rise.Ameya Kanitkar, CTO of Larridin, an AI measurement platform, said that about six months ago, AI costs were a primary concern because companies spent between $20 and $100 per month per LLM subscription. But around February, AI services vendors companies began pushing for more AI usage at a time when the models were getting better and could handle more complex agentic work that takes longer to complete."On average we have seen the cost go up about 10x between January and now, especially in engineering ops," Kanitkar said in an interview with The Register. It's a change he attributes to the shift toward longer, agentic tasks and metered pricing.