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Elephant Alpha is a 100B-parameter text model focused on intelligence efficiency, delivering strong reasoning performance while minimizing token usage. It supports a 256K context window with up to 32K output...
a quiet day
Hunter Alpha is a 1 Trillion parameter + 1M token context frontier intelligence model built for agentic use. It excels at long-horizon planning, complex reasoning, and sustained multi-step task execution, with the reliability and instruction-following precision that frameworks like OpenClaw need. **Note:** All prompts and completions for this model are logged by the provider and may be used to improve the model.
Healer Alpha is a frontier omni-modal model with vision, hearing, reasoning, and action capabilities. It brings the full power of agentic intelligence into the real world: natively perceiving visual and audio inputs, reasoning across modalities, and executing complex multi-step tasks with precision and reliability. **Note:** All prompts and completions for this model are logged by the provider and may be used to improve the model.
Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation of the Gemini 3 series, it combines high-precision reasoning across text, image, video, audio, and code with a 1M-token context window. Reasoning Details must be preserved when using multi-turn tool calling, see our docs here: https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning. The 3.1 update introduces measurable gains in SWE benchmarks and real-world coding environments, along with stronger autonomous task execution in structured domains such as finance and spreadsheet-based workflows. Designed for advanced development and agentic systems, Gemini 3.1 Pro Preview improves long-horizon stability and tool orchestration while increasing token efficiency. It introduces a new medium thinking level to better balance cost, speed, and performance. The model excels in agentic coding, structured planning, multimodal analysis, and workflow automation, making it well-suited for autonomous agents, financial modeling, spreadsheet automation, and high-context enterprise tasks.
There's too much going on!
We have Opus 4.5 at home
This is a cloaked model provided to the community to gather feedback. A reasoning model designed for speed. It is built for coding assistants, real-time conversational applications, and agentic workflows. Default reasoning effort is set to medium for fast responses. For agentic coding use cases, we recommend changing effort to high. Note: All prompts and completions for this model are logged by the provider and may be used to improve the model.
Pony is a cutting-edge foundation model with strong performance in coding, agentic workflows, reasoning, and roleplay, making it well suited for hands-on coding and real-world use. **Note:** All prompts and completions for this model are logged by the provider and may be used to improve the model.
Riverflow V2 Fast is the fastest variant of Sourceful's Riverflow 2.0 lineup, best for production deployments and latency-critical workflows. The Riverflow 2.0 series represents SOTA performance on image generation and editing tasks, using an integrated reasoning model to boost reliability and tackle complex challenges. Pricing is $0.02 per 1K output image and $0.04 per 2K output image. Does not support 4K image output. Additional features: - Custom font rendering via font_inputs ($0.03/font, max 2) - Image enhancement via super_resolution_references ($0.20/reference, max 4) See the image generation docs for details: https://openrouter.ai/docs/features/multimodal/image-generation Note: Sourceful imposes a 4.5MB request size limit, therefore it is highly recommended to pass image URLs instead of Base64 data.
The simplest way to get free inference. openrouter/free is a router that selects free models at random from the models available on OpenRouter. The router smartly filters for models that support features needed for your request such as image understanding, tool calling, structured outputs and more.
China takes another huge leap ahead in open models
Transform your natural language requests into structured OpenRouter API request objects. Describe what you want to accomplish with AI models, and Body Builder will construct the appropriate API calls. Example: "count to 10 using gemini and opus." This is useful for creating multi-model requests, custom model routers, or programmatic generation of API calls from human descriptions. **BETA NOTICE**: Body Builder is in beta, and currently free. Pricing and functionality may change in the future.
TNG-R1T-Chimera is an experimental LLM with a faible for creative storytelling and character interaction. It is a derivate of the original TNG/DeepSeek-R1T-Chimera released in April 2025 and is available exclusively via Chutes and OpenRouter. Characteristics and improvements include: We think that it has a creative and pleasant personality. It has a preliminary EQ-Bench3 value of about 1305. It is quite a bit more intelligent than the original, albeit a slightly slower. It is much more think-token consistent, i.e. reasoning and answer blocks are properly delineated. Tool calling is much improved. TNG Tech, the model authors, ask that users follow the careful guidelines that Microsoft has created for their "MAI-DS-R1" DeepSeek-based model. These guidelines are available on Hugging Face (https://huggingface.co/microsoft/MAI-DS-R1).
Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and reasoning benchmarks, and improved robustness to prompt injection. The model is designed to operate efficiently across varied effort levels, enabling developers to trade off speed, depth, and token usage depending on task requirements. It comes with a new parameter to control token efficiency, which can be accessed using the OpenRouter Verbosity parameter with low, medium, or high. Opus 4.5 supports advanced tool use, extended context management, and coordinated multi-agent setups, making it well-suited for autonomous research, debugging, multi-step planning, and spreadsheet/browser manipulation. It delivers substantial gains in structured reasoning, execution reliability, and alignment compared to prior Opus generations, while reducing token overhead and improving performance on long-running tasks.
This model was an early testing version of Mistral Large 3. Try the official launch of Mistral Large 3 here This is a cloaked model provided to the community to gather feedback. A general-purpose multimodal model (text/image in, text out) designed for reliability, long-context comprehension, and adaptive logic. It is engineered for production-grade assistants, retrieval-augmented systems, science workloads, and complex agentic workflows. **Note:** All prompts and completions for this model are logged by the provider and may be used to improve the model.
Gemini 3 Pro is Google’s flagship frontier model for high-precision multimodal reasoning, combining strong performance across text, image, video, audio, and code with a 1M-token context window. Reasoning Details must be preserved when using multi-turn tool calling, see our docs here: https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks. It delivers state-of-the-art benchmark results in general reasoning, STEM problem solving, factual QA, and multimodal understanding, including leading scores on LMArena, GPQA Diamond, MathArena Apex, MMMU-Pro, and Video-MMMU. Interactions emphasize depth and interpretability: the model is designed to infer intent with minimal prompting and produce direct, insight-focused responses. Built for advanced development and agentic workflows, Gemini 3 Pro provides robust tool-calling, long-horizon planning stability, and strong zero-shot generation for complex UI, visualization, and coding tasks. It excels at agentic coding (SWE-Bench Verified, Terminal-Bench 2.0), multimodal analysis, and structured long-form tasks such as research synthesis, planning, and interactive learning experiences. Suitable applications include autonomous agents, coding assistants, multimodal analytics, scientific reasoning, and high-context information processing.
This model was an early snapshot of Grok 4.1 Fast with reasoning disabled. Try the official launch of Grok 4.1 Fast here This is a cloaked model provided to the community to gather feedback. A frontier non-reasoning model that excels at tool calling, with a 1.8M context window and multimodal support. **Note:** All prompts and completions for this model are logged by the provider and may be used to improve the model.
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