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NextBit 的网站图标

NextBit

浏览以下提供商提供的模型: NextBit (服务条款)

7 个模型

OpenRouter 处理的 token 数

  • z-ai 的网站图标
    Z.ai: GLM 5.3 FlashGLM 5.3 Flash

    GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while reducing compute overhead.

    作者: z-ai2026年8月26日1.05M 上下文US$0.12/M 输入 tokenUS$0.45/M 输出 token
  • google 的网站图标
    Google: Gemma 4 26B A4B Gemma 4 26B A4B
    15% off

    Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at a fraction of the compute cost. Supports multimodal input including text, images, and video (up to 60s at 1fps). Features a 256K token context window, native function calling, configurable thinking/reasoning mode, and structured output support. Released under Apache 2.0.

    作者: google2026年4月3日262K 上下文US$0.0765/M 输入 tokenUS$0.255/M 输出 token
  • google 的网站图标
    Google: Gemma 4 31BGemma 4 31B

    Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function calling, and multilingual support across 140+ languages. Strong on coding, reasoning, and document understanding tasks. Apache 2.0 license.

    作者: google2026年4月2日262K 上下文US$0.15/M 输入 tokenUS$0.40/M 输出 token
  • qwen 的网站图标
    Qwen: Qwen3 14BQwen3 14B

    Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for tasks like math, programming, and logical inference, and a "non-thinking" mode for general-purpose conversation. The model is fine-tuned for instruction-following, agent tool use, creative writing, and multilingual tasks across 100+ languages and dialects. It natively handles 32K token contexts and can extend to 131K tokens using YaRN-based scaling.

    作者: qwen2025年4月28日132K 上下文US$0.10/M 输入 tokenUS$0.22/M 输出 token
  • google 的网站图标
    Google: Gemma 3 12BGemma 3 12B

    Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 12B is the second largest in the family of Gemma 3 models after Gemma 3 27B

    作者: google2025年3月13日131K 上下文US$0.075/M 输入 tokenUS$0.15/M 输出 token
  • google 的网站图标
    Google: Gemma 3 27BGemma 3 27B

    Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities, including structured outputs and function calling. Gemma 3 27B is Google's latest open source model, successor to Gemma 2

    作者: google2025年3月12日131K 上下文US$0.08/M 输入 tokenUS$0.35/M 输出 token
  • google 的网站图标
    Google: Gemma 2 27BGemma 2 27B

    Gemma 2 27B by Google is an open model built from the same research and technology used to create the Gemini models. Gemma models are well-suited for a variety of text generation tasks, including question answering, summarization, and reasoning. See the launch announcement for more details. Usage of Gemma is subject to Google's Gemma Terms of Use.

    作者: google2024年7月13日8K 上下文US$0.65/M 输入 tokenUS$0.65/M 输出 token