Loading model information...
Loading model information...
Provided by OpenRouter
Olmo 3.1 32B Think is a large-scale, 32-billion-parameter model designed for deep reasoning, complex multi-step logic, and advanced instruction following. Building on the Olmo 3 series, version 3.1 delivers refined reasoning behavior and stronger performance across demanding evaluations and nuanced conversational tasks. Developed by Ai2 under the Apache 2.0 license, Olmo 3.1 32B Think continues the Olmo initiative’s commitment to openness, providing full transparency across model weights, code, and training methodology.
65,536 tokens$0.150/M$0.500/MOlmo 3.1 32B Think is a large-scale, 32-billion-parameter model designed for deep reasoning, complex multi-step logic, and advanced instruction following. Building on the Olmo 3 series, version 3.1 delivers refined reasoning behavior and stronger performance across demanding evaluations and nuanced conversational tasks. Developed by Ai2 under the Apache 2.0 license, Olmo 3.1 32B Think continues the Olmo initiative’s commitment to openness, providing full transparency across model weights, code, and training methodology.
Performance may vary based on query complexity, context length, and task type. Consider using higher-tier models for production-critical applications.
Try these prompts to explore AllenAI: Olmo 3.1 32B Think's capabilities:
Explain quantum computing in simple terms like I'm 10 years old
Write a compelling email asking for a meeting to discuss a project proposal
Help me brainstorm creative solutions for improving team productivity
Tip: Customize these prompts to fit your specific needs and use cases.
This model requires credits to use. AllenAI: Olmo 3.1 32B Think offers advanced capabilities and high-performance features for production-grade applications.
Credits required for premium models. Free models are available without credits.
Similar models you might be interested in
GLM-5 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading closed-source models. With advanced agentic planning, deep backend reasoning, and iterative self-correction, GLM-5 moves beyond code generation to full-system construction and autonomous execution.
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.
Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per token, delivering performance comparable to models with 10 to 20x higher active compute, which makes it well suited for cost-sensitive, always-on agent deployment. The model is trained with a strong agentic focus and performs reliably on long-horizon coding tasks, complex tool usage, and recovery from execution failures. With a native 256k context window, it integrates cleanly into real-world CLI and IDE environments and adapts well to common agent scaffolds used by modern coding tools. The model operates exclusively in non-thinking mode and does not emit <think> blocks, simplifying integration for production coding agents.
Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token. It is a reasoning model that is incredibly speed efficient even at long contexts.