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Favicon for PrimaLabs

PrimaLabs

Browse models provided by PrimaLabs (Terms of Service)

3 models

Tokens processed on OpenRouter

  • Favicon for moonshotai
    MoonshotAI: Kimi K3.1 ProKimi K3.1 Pro

    Kimi K3.1 Pro is Moonshot AI's flagship open-weight multimodal reasoning model and a post-trained successor to Kimi K3, with a 1M-token context window. It is built for long-horizon coding, 3D creation, financial research, and end-to-end agentic knowledge work, with native image and video understanding.

    by moonshotaiOct 23, 20261.05M context$1.80/M input tokens$9/M output tokens
  • Favicon for deepseek
    DeepSeek: DeepSeek V4.1 Flash 1017DeepSeek V4.1 Flash 1017

    DeepSeek V4.1 Flash 1017 is the October 17, 2026 snapshot of DeepSeek V4.1 Flash, served by DeepSeek under the deepseek-flash API model name. V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on output from a 552B-parameter backbone, an asymmetric split that keeps per-token compute low relative to the model's total size. Image understanding is native to the architecture, with visual and text embeddings trained jointly from the start of pre-training rather than added afterward as in the earlier experimental V4 Flash Vision Exp. It is suited for coding, terminal, and computer-use agents, along with long-horizon tasks that must run to completion across many steps and long-context analysis. Compressed KV caching cuts cache memory to roughly a quarter of the previous Flash generation, significantly reducing costs on agentic workloads. DeepSeek positions it as the cost-efficient tier of the V4.1 family and reports that it exceeds V4 Pro on performance, speed, and task completion time.

    by deepseekOct 17, 20261.05M context$0.90/M input tokens$3.60/M output tokens
  • Favicon for xiaomi
    Xiaomi: MiMo-V2.6-ProMiMo-V2.6-Pro

    MiMo-V2.6-Pro is the flagship foundation model developed by Xiaomi. Built at a scale of over 1T parameters, it is designed to push the ceiling of capability for the most demanding workloads. The model features a 1M-token context window and native multimodal capabilities. Optimized for agentic workflows, it delivers top-tier performance across coding, visual, general, and research scenarios, excelling at complex, long-horizon tasks with robust generalization across a diverse range of agent harnesses.

    by xiaomiSep 21, 20261.05M context$4/M input tokens$8/M output tokens