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The AI Price War Arrives: OpenAI and Anthropic Cut Costs on the Same Afternoon
Two rival launches, about 90 minutes apart, with the same pitch: strong models that are cheaper to run.
On Tuesday, the two biggest names in AI shipped new models within roughly an hour and a half of each other. Neither led with a leap in raw intelligence. Both led with price.
Anthropic went first with Claude Opus 5.5. The company says it performs at the level of Claude Fable 5.1 on most work and costs about 40% less to run than Opus 5 on typical workloads. Only part of that comes from the price tag: per-token rates fell 20%, to $4 per million input tokens and $20 per million output tokens. The rest, Anthropic says, comes from the model using fewer tokens to finish a job. Cache reads, a big share of costs for coding and agent work, dropped 60%.
OpenAI followed with GPT-6 Sol and GPT-6 Luna, two models that sit below its flagship, GPT-6 Astra. Sol is priced at $2 per million input tokens and $10 per million output tokens; Luna at $0.10 and $0.50. OpenAI says both cost half of what their GPT-5.6 predecessors charge under current promotional pricing.
Why it matters: For businesses and developers that call these models thousands or millions of times a day, small per-token differences add up quickly. Cheaper capable models make more tasks worth automating and more experiments worth running.
Two different plays: OpenAI is spreading its lineup across very different price points, from a premium flagship down to a budget model for high-volume work. Anthropic is keeping one top-tier model and making it cheaper to operate.
The catch: The launch-day numbers are the companies' own. Both increasingly compare cost per task rather than cost per token, which is harder for outsiders to check. Real-world bills will depend on the job, reasoning settings, caching and how much output an app actually needs.
The bigger picture: Pressure is coming from below, as low-cost open-weight models from Chinese developers such as Alibaba and DeepSeek handle more everyday work, according to The Next Web. The next phase of AI competition looks less like a race for the smartest model and more like a race to make smart models affordable at scale.
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