Loading...
Browse 421 models across providers, modalities, and use cases.
🌐 All Models
421 models · Page 5 of 12
This 8-billion parameter language model with an 8K token context window is optimized for ranking and relevance tasks in enterprise search and information retrieval applications, making it ideal for Salesforce users and developers who need to implement semantic ranking, query-document matching, and result prioritization in customer relationship and business intelligence platforms.
This instruction-following model with 262K token context excels at handling long documents, complex reasoning tasks, and detailed conversations, making it ideal for developers and enterprises needing efficient processing of extended content without sacrificing performance.
This compact 1.5B parameter instruction-tuned model excels at lightweight conversational tasks and quick inference for developers and organizations seeking cost-effective AI with a 33K token context window, ideal for resource-constrained environments and budget-sensitive applications.
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-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the...
Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default. It’s designed for hard multi-step problems; math proofs, code synthesis/debugging, logic, and agentic...
Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...
Rnj-1 is an 8B-parameter, dense, open-weight model family developed by Essential AI and trained from scratch with a focus on programming, math, and scientific reasoning. The model demonstrates strong performance...
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-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...
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,...
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 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.
This lightweight 1.5B parameter language model excels at instruction-following tasks with a 33K token context window, making it ideal for developers and organizations seeking fast, efficient inference for text generation, summarization, and question-answering applications on resource-constrained devices or cost-sensitive deployments.
Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a "thinking" mode for...
Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique...
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...
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...
Microsoft Phi-4 14B — small language model achieving state-of-the-art results on reasoning tasks.
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...
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...
One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge
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...
Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...
gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks like content classification, LLM filtering, and trust...
Mistral Small 4.
