API Pricing in 2026: A Race to the Bottom or a New Equilibrium?
Input token prices have dropped 80% in 18 months. We analyze what this means for developers and the models competing on cost.
GPTUni Team
The cost of running AI inference has dropped sharply. In mid-2024, a frontier-class model like GPT-4 charged $30 per million input tokens. Today, comparable models are available for under $3, and mid-tier alternatives cost as little as $0.10. This is an 80%+ reduction in 18 months.
Several factors are driving the decline. Mixture-of-Experts architectures have reduced the compute needed per token by activating only a fraction of model parameters. Hardware improvements, including NVIDIA's Blackwell GPUs and custom chips from Google and Amazon, have lowered the cost of running inference at scale. And competition from Chinese labs like DeepSeek, Qwen, and Moonshot AI has applied downward pressure on margins.
For developers, the practical impact is significant. An application that processes 10 million tokens per day now costs roughly $1-3 with a model like Qwen3.5 397B or DeepSeek V3, compared to $300 a year ago with GPT-4. This changes the economics of AI-powered features from expensive add-ons to commodity infrastructure.
The question is whether prices will continue falling. The major providers are burning through capital to subsidize usage, and it remains unclear whether current pricing is sustainable. But for now, developers have more options at lower costs than at any point in the history of AI APIs.
The most affordable frontier-class options as of February 2026: - DeepSeek V3 0324: $0.27 input, $1.10 output per million tokens - Qwen3.5 397B: $0.15 input, $1.00 output per million tokens - Gemini 2.0 Flash: $0.10 input, $0.40 output per million tokens - Llama 4 Scout (self-hosted): Hardware cost only