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NextBit의 파비콘

NextBit

다음 제공업체의 모델 둘러보기: NextBit (서비스 약관)

7 모델

OpenRouter에서 처리된 토큰

  • z-ai의 파비콘
    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.

    제작: z-ai2026년 8월 26일1.05M 컨텍스트US$0.12/M 입력 토큰US$0.45/M 출력 토큰
  • google의 파비콘
    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.

    제작: google2026년 4월 3일262K 컨텍스트US$0.0765/M 입력 토큰US$0.255/M 출력 토큰
  • google의 파비콘
    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.

    제작: google2026년 4월 2일262K 컨텍스트US$0.15/M 입력 토큰US$0.40/M 출력 토큰
  • qwen의 파비콘
    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.

    제작: qwen2025년 4월 28일132K 컨텍스트US$0.10/M 입력 토큰US$0.22/M 출력 토큰
  • google의 파비콘
    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

    제작: google2025년 3월 13일131K 컨텍스트US$0.075/M 입력 토큰US$0.15/M 출력 토큰
  • google의 파비콘
    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

    제작: google2025년 3월 12일131K 컨텍스트US$0.08/M 입력 토큰US$0.35/M 출력 토큰
  • google의 파비콘
    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.

    제작: google2024년 7월 13일8K 컨텍스트US$0.65/M 입력 토큰US$0.65/M 출력 토큰