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Favicon for Ambient

Ambient

Browse models provided by Ambient

9 models

Tokens processed on OpenRouter

  • Favicon for moonshotai
    MoonshotAI: Kimi K2.7 CodeKimi K2.7 Code

    MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts architecture that accepts text and image input, and it always operates in a thinking mode, preserving full reasoning content across multi-turn conversations. With a 256K-token context window, it targets long-horizon coding, agentic task decomposition, and multi-turn dialogue. The model activates 32B parameters out of roughly 1T total.

    by moonshotaiJun 12, 2026262K context$0.95/M input tokens$4/M output tokens
  • Favicon for qwen
    Qwen: Qwen3.6 35B A3BQwen3.6 35B A3B

    Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated DeltaNet linear attention with standard gated attention layers, enabling efficient inference at a fraction of the compute cost. The model supports a 262K token native context window (extensible to 1M via YaRN) and accepts text, image, and video inputs. It includes integrated thinking mode with reasoning traces preserved across multi-turn conversations, function calling, and structured output. Released under the Apache 2.0 license.

    by qwenApr 27, 2026262K context$0.15/M input tokens$1/M output tokens
  • Favicon for qwen
    Qwen: Qwen3.6 27BQwen3.6 27B

    Qwen3.6 27B is a dense 27-billion-parameter language model from the Qwen Team at Alibaba, released in April 2026. It features hybrid multimodal capabilities — accepting text, image, and video inputs — and supports a 262,144-token context window. The model is designed for agentic coding and reasoning tasks, with particular strength in repository-level code comprehension, front-end development workflows, and multi-step problem solving. It includes a built-in thinking mode for extended reasoning and preserves thinking context across conversation history. Qwen3.6 27B supports 201 languages and dialects and is released under the Apache 2.0 license.

    by qwenApr 27, 2026262K context$0.32/M input tokens$3.20/M output tokens
  • Favicon for z-ai
    Z.ai: GLM 5.1GLM 5.1

    GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interactions, GLM-5.1 can work independently and continuously on a single task for more than 8 hours, autonomously planning, executing, and improving itself throughout the process, ultimately delivering complete, engineering-grade results.

    by z-aiApr 7, 2026203K context$1.40/M input tokens$4.40/M output tokens
  • Favicon for google
    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.

    by googleApr 2, 2026262K context$0.20/M input tokens$0.80/M output tokens
  • Favicon for qwen
    Qwen: Qwen3.5-35B-A3BQwen3.5-35B-A3B

    The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall performance is comparable to that of the Qwen3.5-27B.

    by qwenFeb 25, 2026256K context$0.14/M input tokens$1/M output tokens
  • Favicon for qwen
    Qwen: Qwen3 Next 80B A3B InstructQwen3 Next 80B A3B Instruct

    Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual use, while remaining robust on alignment and formatting. Compared with prior Qwen3 instruct variants, it focuses on higher throughput and stability on ultra-long inputs and multi-turn dialogues, making it well-suited for RAG, tool use, and agentic workflows that require consistent final answers rather than visible chain-of-thought. The model employs scaling-efficient training and decoding to improve parameter efficiency and inference speed, and has been validated on a broad set of public benchmarks where it reaches or approaches larger Qwen3 systems in several categories while outperforming earlier mid-sized baselines. It is best used as a general assistant, code helper, and long-context task solver in production settings where deterministic, instruction-following outputs are preferred.

    by qwenSep 11, 2025262K context$0.09/M input tokens$1.10/M output tokens
  • Favicon for openai
    OpenAI: gpt-oss-20bgpt-oss-20b

    gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for lower-latency inference and deployability on consumer or single-GPU hardware. The model is trained in OpenAI’s Harmony response format and supports reasoning level configuration, fine-tuning, and agentic capabilities including function calling, tool use, and structured outputs.

    by openaiAug 5, 2025131K context$0.029/M input tokens$0.14/M output tokens
  • Favicon for z-ai
    Z.ai: GLM 4.5 AirGLM 4.5 Air

    GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter size. GLM-4.5-Air also supports hybrid inference modes, offering a "thinking mode" for advanced reasoning and tool use, and a "non-thinking mode" for real-time interaction. Users can control the reasoning behaviour with the `reasoning` `enabled` boolean. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#enable-reasoning-with-default-config)

    by z-aiJul 25, 2025131K context$0.125/M input tokens$0.85/M output tokens