TechCrunch AIMay 26, 2026, 6:33 PMJulie Bort

OpenRouter more than doubles valuation to $1.3B in a year

OpenRouter raised a $113M Series B led by CapitalG at a reported $1.3B post-money valuation.

OpenRouter, an AI gateway startup founded in 2023, raised a $113 million Series B led by CapitalG. The round reportedly values the company at about $1.3 billion post-money, more than doubling from its estimated $547 million valuation after its June 2025 Series A. The company says it now offers access to over 400 models, has 8 million global users, and processes 100 trillion tokens per month.

OpenRouter has completed a $113 million Series B, led by CapitalG, the growth investment fund under Alphabet (Google's parent company). TechCrunch, citing reports, notes that OpenRouter's post-money valuation in this round is about $1.3 billion; compared with the roughly $547 million post-money valuation that PitchBook estimated after its $40 million Series A in June 2025, that means it has more than doubled in just one year. The article frames OpenRouter's growth in the context of shifting patterns of AI use: the AI industry's focus is moving from training large models toward inference, and then further toward agent applications. In this environment, enterprises and developers need not just a single model, but infrastructure that can switch among multiple models depending on the task, control costs, and improve reasoning ability or accuracy. OpenRouter is positioned precisely as an AI gateway, letting users access models from different model providers through a single entry point. The company says the platform currently offers more than 400 models, covering providers such as Anthropic, Google, OpenAI, xAI, and DeepSeek; and it claims to already have 8 million global users, processing 100 trillion tokens per month, equivalent to about 25 trillion tokens per week. This also means its weekly processing volume grew from 5 trillion to 25 trillion tokens in six months, a fivefold increase. The article's core observation is that OpenRouter's success shows AI models are gradually becoming interchangeable, invisible task engines, rather than a single vendor that enterprises must commit to for the long term. For developers, AI product teams, and enterprise buyers, this means a multi-model architecture may already have shifted from a backup strategy to a mainstream direction: different models are dynamically selected based on cost, speed, reasoning ability, accuracy, and task type. This does not mean any single model company loses its importance, but it shows the market is seeking ways to avoid vendor lock-in, similar to the lock-in problems enterprises encountered in the past with SaaS procurement.

Full summary

Free shows the 3-line summary; Pro unlocks the full deep summary (~300 words) so you never have to click through.

See Pro plans →

Want the original English / full article?

Read on TechCrunch AI →

Summaries are AI-generated; the original article is authoritative.