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Browse 558 models across providers, modalities, and use cases.
📄 Long Context
558 models · Page 6 of 16
A large language model with 164K token context window designed for developers and researchers who need strong reasoning capabilities, multilingual support, and efficient processing of long documents without the constraints of smaller context windows.
A highly capable frontier language model with 164K token context window designed for researchers, developers, and enterprises seeking strong performance across reasoning, coding, mathematics, and multilingual tasks with efficient inference.
A compact 7-billion parameter language model distilled from DeepSeek R1's reasoning capabilities, optimized for developers and researchers who need efficient reasoning and problem-solving on resource-constrained hardware while maintaining a 131K token context window for processing long documents.
This large language model excels at complex reasoning, multi-turn conversations, and long-form content generation with its 131K token context window, making it ideal for developers and enterprises needing cost-effective, high-performance instruction-following capabilities for production applications.
This 14-billion-parameter distilled model excels at reasoning tasks and general-purpose applications with a massive 131K token context window, making it ideal for developers and researchers seeking cost-effective performance on complex problem-solving, long-document analysis, and multi-turn conversations at just $1.60 per million input tokens.
A large language model with 671 billion parameters and 164K token context window designed for organizations requiring extended document analysis, complex reasoning tasks, and cost-effective processing at $1.25 per million input tokens.
This 14-billion-parameter language model with 131K token context window excels at processing long documents and complex reasoning tasks, making it ideal for researchers, developers, and enterprises needing extended context handling for document analysis, code generation, and multi-turn conversations.
This distilled reasoning model excels at complex problem-solving and multi-step analysis across a 131K token context, making it ideal for developers and enterprises seeking cost-effective ($2.00/1M input tokens) DeepSeek reasoning capabilities without the computational overhead of the full R1 model.
This lightweight 1.5B parameter model excels at fast, cost-effective reasoning tasks with a 131K token context window, making it ideal for developers and researchers seeking efficient inference for question-answering, summarization, and logic-based applications on resource-constrained systems.
Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...
Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It...
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,...
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...
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...
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...
Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception...
Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...
Qwen3-Max-Thinking is the flagship reasoning model in the Qwen3 series, designed for high-stakes cognitive tasks that require deep, multi-step reasoning. By significantly scaling model capacity and reinforcement learning compute, it...
Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table...
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...
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...
NVIDIA Nemotron 3 Nano 30B A3B is a small language MoE model with highest compute efficiency and accuracy for developers to build specialized agentic AI systems. The model is fully...
Our medium code-agentic model.
Our top-tier reasoning model for high-complexity tasks with the lastest version released November 2024.
Our small open-source code-agentic model.
Official pixtral-large-2411 Mistral AI model
Mistral Small 4.
