
Ramp has launched its own AI model routing service, dubbed Router, that lets users and companies use and switch between various large language models via an API.
Ramp, a corporate expense management platform, launched Router, an AI model routing service that allows users to access and switch between various large language models through a single API. The service is free through the end of this year, though users pay separately for the actual AI model inference costs, and offers access to models from multiple providers including OpenAI, Anthropic, and DeepSeek. Router includes features like routing strategies that let customers choose models based on preferences, benchmarks, or cost considerations, along with a dashboard showing token spending and latency details. For Ramp, this move into the AI inference market represents an opportunity to serve its existing customers while potentially building relationships with AI providers and gaining new business for its core expense management products.
Better embeddings help AI systems understand meaning more accurately, improving search, recommendation, and classification tasks across applications.

IBM has rolled out the newest models in its family of open-weight large language models designed to be downloaded and self-hosted. The newly launched Granite 4.2 comes in 3B, 8B, and 30B parameter variants. Like previous versions, IBM is taking a decoder-only approach here. These new releases offer a 128,000-token context window natively. The 8B and 30B variants (not the 3B one) also go through an agentic reinforcement learning block; they were trained for expanded capabilities like using the t

IBM has released Granite 4.2, a family of open reasoning language models in 3B, 8B, and 30B parameter sizes. Unlike earlier Granite releases, which were instruction-following assistants, Granite 4.2 is built around explicit reasoning. Every model can emit a chain of thought before answering, and every model exposes a thinking / non-thinking switch plus a low-effort mode that spends a short reasoning budget on easy questions. The models are decoder-only dense transformers, pre-trained from scrat
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