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Open-source multimodal AI with 1T parameters, 256K context, and agent swarm capabilities at 1/10th the cost of proprietary models.
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Kimi K2.5 is an open-source multimodal AI model featuring 1 trillion parameters, a 256K context window, and built-in agent swarm capabilities, delivering cost-efficient reasoning and coding at one-tenth the cost of proprietary models.
Kimi K2.5 is a Mixture-of-Experts (MoE) multimodal model developed by Moonshot AI. It processes text, images, videos, and PDFs natively and produces structured outputs including code, analysis, and agent coordination. The model runs on cloud APIs and locally via open-weight deployment. It is designed for long-context understanding and autonomous multi-agent workflows.
Developers access Kimi K2.5 via cloud API or by downloading the open-weight model for local deployment. The model accepts multimodal inputs through a unified interface, processes them with its MoE transformer, and outputs structured responses. For agent workflows, users define task decomposition schemas that Kimi K2.5 uses to spawn and coordinate sub-agents autonomously.
Kimi K2.5 is available as open-source software (free weights) and via a cloud API with pay-per-token pricing at $0.39 per million tokens. No subscription is required for API access.
The model weights are open-source and free to download. The cloud API charges $0.39 per million tokens, which is significantly cheaper than most proprietary alternatives.
Kimi K2.5 performs multimodal understanding (text, images, video, PDFs), generates code from visuals, and orchestrates autonomous agent swarms for complex task decomposition.
Yes, the model weights are openly available and support INT4 quantization, allowing deployment on consumer-grade hardware with reduced memory footprint.
Kimi K2.5 offers similar multimodal and reasoning capabilities at roughly one-tenth the API cost, with the added benefit of open-weight access and agent swarm features.
Moonshot AI is the company behind Kimi K2.5, focused on developing open-source multimodal models and agentic AI infrastructure.