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. “The new trend that is emerging is that the open source models, open weight models are actually not that far behind the frontier models,” he said. “And now the costs are hitting the balance sheet, which are not the real costs, companies have started truly thinking, ‘okay, how can we actually adjust these costs?'” Kanitkar said he’s seeing companies spending between 10 and 20 percent of their labor cost – e.g. $2,000 to $4,000 per month for a software engineer paid $200,000 annually – on tokens. But, he added, higher spending doesn’t necessarily mean higher productivity. Between 15 and 30 percent of AI users among Larridin’s clients account for more than 50 percent of total AI spend, and often that spending does not correlate with gains in output, according to a company spokesperson. When Larridin plotted token spend against developer productivity, it found an inflection point at about 35 to 40 percent of client spending where burning more tokens failed to boost productivity. Using that point as a token limit for employees can cut AI costs by 40 percent without changing anything else, Kanitkar said. Open weight models offer another cost lever. Kimi 2.6/2.7 and GLM 5.2, Kanitkar said, “are almost at parity with Opus4.7 or 4.8. And they are 10x cheaper in theory, or about 5x cheaper in practice. They tend to be a little bit slow and they tend to consume more tokens on the pure token basis – that costs more, but the token cost is low.” Kanitkar said he’s now seeing almost 75 percent of companies use multiple models. Switching back and forth, he said, is more difficult for customer-facing agentic work, but for software development, it’s much more viable. Even so, price isn’t always the most important consideration. Larridin data shows that enterprises still direct almost half of their AI spending toward Anthropic’s Opus model because it handles complex engineering and reasoning tasks well. ®