List of All LLM Models
Discover and compare 500+ large language models with real-time rankings, benchmarks, and community votes.
Mistral: Ministral 3 3B 2512
By Mistral AI
The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.
Release Date
02 Dec 2025
Context Size
131.07K
Mistral: Mistral Large 3 2512
By Mistral AI
Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.
Release Date
01 Dec 2025
Context Size
262.14K
Arcee AI: Trinity Mini
By arcee-ai
Trinity Mini is a 26B-parameter (3B active) sparse mixture-of-experts language model featuring 128 experts with 8 active per token. Engineered for efficient reasoning over long contexts (131k) with robust function calling and multi-step agent workflows.
Release Date
01 Dec 2025
Context Size
131.07K
DeepSeek: DeepSeek V3.2 Speciale
By DeepSeek
DeepSeek-V3.2-Speciale is a high-compute variant of DeepSeek-V3.2 optimized for maximum reasoning and agentic performance. It builds on DeepSeek Sparse Attention (DSA) for efficient long-context processing, then scales post-training reinforcement learning to push capability beyond the base model. Reported evaluations place Speciale ahead of GPT-5 on difficult reasoning workloads, with proficiency comparable to Gemini-3.0-Pro, while retaining strong coding and tool-use reliability. Like V3.2, it benefits from a large-scale agentic task synthesis pipeline that improves compliance and generalization in interactive environments.
Release Date
01 Dec 2025
Context Size
163.84K
DeepSeek: DeepSeek V3.2
By DeepSeek
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism that reduces training and inference cost while preserving quality in long-context scenarios. A scalable reinforcement learning post-training framework further improves reasoning, with reported performance in the GPT-5 class, and the model has demonstrated gold-medal results on the 2025 IMO and IOI. V3.2 also uses a large-scale agentic task synthesis pipeline to better integrate reasoning into tool-use settings, boosting compliance and generalization in interactive environments. 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)
Release Date
01 Dec 2025
Context Size
131.07K
Prime Intellect: INTELLECT-3
By prime-intellect
INTELLECT-3 is a 106B-parameter Mixture-of-Experts model (12B active) post-trained from GLM-4.5-Air-Base using supervised fine-tuning (SFT) followed by large-scale reinforcement learning (RL). It offers state-of-the-art performance for its size across math, code, science, and general reasoning, consistently outperforming many larger frontier models. Designed for strong multi-step problem solving, it maintains high accuracy on structured tasks while remaining efficient at inference thanks to its MoE architecture.
Release Date
27 Nov 2025
Context Size
131.07K
TNG: R1T Chimera
By tngtech
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).
Release Date
26 Nov 2025
Context Size
163.84K
Black Forest Labs: FLUX.2 Flex
By black-forest-labs
FLUX.2 [flex] excels at rendering complex text, typography, and fine details, and supports multi-reference editing in the same unified architecture. Pricing is as follows, [per the docs](https://bfl.ai/pricing?category=flux.2): We charge $0.06 for each megapixel on both input and output side.
Release Date
25 Nov 2025
Context Size
67.34K
Black Forest Labs: FLUX.2 Pro
By black-forest-labs
A high-end image generation and editing model focused on frontier-level visual quality and reliability. It delivers strong prompt adherence, stable lighting, sharp textures, and consistent character/style reproduction across multi-reference inputs. Designed for production workloads, it balances speed and quality while supporting text-to-image and image editing up to 4 MP resolution. Pricing is as follows, [per the docs](https://bfl.ai/pricing?category=flux.2): Input: We charge $0.015 for each megapixel on the input (i.e. reference images for editing) Output: The first megapixel is charged $0.03 and then each subsequent MP will be charged $0.015.
Release Date
25 Nov 2025
Context Size
46.86K
Anthropic: Claude Opus 4.5
By Anthropic
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.
Release Date
24 Nov 2025
Context Size
200K
Bert-Nebulon Alpha
By OpenRouter
This model was an early testing version of Mistral Large 3. Try the official launch of Mistral Large 3 [here](/mistralai/mistral-large-2512) 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.
Release Date
24 Nov 2025
Context Size
256K
AllenAI: Olmo 3 32B Think
By Ai2
Olmo 3 32B Think is a large-scale, 32-billion-parameter model purpose-built for deep reasoning, complex logic chains and advanced instruction-following scenarios. Its capacity enables strong performance on demanding evaluation tasks and highly nuanced conversational reasoning. Developed by Ai2 under the Apache 2.0 license, Olmo 3 32B Think embodies the Olmo initiative’s commitment to openness, offering full transparency across weights, code and training methodology.
Release Date
21 Nov 2025
Context Size
65.54K
AllenAI: Olmo 3 7B Instruct
By Ai2
Olmo 3 7B Instruct is a supervised instruction-fine-tuned variant of the Olmo 3 7B base model, optimized for instruction-following, question-answering, and natural conversational dialogue. By leveraging high-quality instruction data and an open training pipeline, it delivers strong performance across everyday NLP tasks while remaining accessible and easy to integrate. Developed by Ai2 under the Apache 2.0 license, the model offers a transparent, community-friendly option for instruction-driven applications.
Release Date
21 Nov 2025
Context Size
65.54K
AllenAI: Olmo 3 7B Think
By Ai2
Olmo 3 7B Think is a research-oriented language model in the Olmo family designed for advanced reasoning and instruction-driven tasks. It excels at multi-step problem solving, logical inference, and maintaining coherent conversational context. Developed by Ai2 under the Apache 2.0 license, Olmo 3 7B Think supports transparent, fully open experimentation and provides a lightweight yet capable foundation for academic research and practical NLP workflows.
Release Date
21 Nov 2025
Context Size
65.54K
Google: Nano Banana Pro (Gemini 3 Pro Image Preview)
By Google
Nano Banana Pro is Google’s most advanced image-generation and editing model, built on Gemini 3 Pro. It extends the original Nano Banana with significantly improved multimodal reasoning, real-world grounding, and high-fidelity visual synthesis. The model generates context-rich graphics, from infographics and diagrams to cinematic composites, and can incorporate real-time information via Search grounding. It offers industry-leading text rendering in images (including long passages and multilingual layouts), consistent multi-image blending, and accurate identity preservation across up to five subjects. Nano Banana Pro adds fine-grained creative controls such as localized edits, lighting and focus adjustments, camera transformations, and support for 2K/4K outputs and flexible aspect ratios. It is designed for professional-grade design, product visualization, storyboarding, and complex multi-element compositions while remaining efficient for general image creation workflows.
Release Date
20 Nov 2025
Context Size
65.54K
xAI: Grok 4.1 Fast
By xAI
Grok 4.1 Fast is xAI's best agentic tool calling model that shines in real-world use cases like customer support and deep research. 2M context window. Reasoning can be enabled/disabled using the `reasoning` `enabled` parameter in the API. [Learn more in our docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#controlling-reasoning-tokens)
Release Date
19 Nov 2025
Context Size
2M
Google: Gemini 3 Pro Preview
By Google
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.
Release Date
18 Nov 2025
Context Size
1.05M
Thenlper: GTE-Base
By thenlper
The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.
Release Date
18 Nov 2025
Context Size
512
Thenlper: GTE-Large
By thenlper
The gte-large embedding model converts English sentences, paragraphs and moderate-length documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for information retrieval, semantic textual similarity, reranking and clustering tasks. Trained via multi-stage contrastive learning on a large domain-diverse relevance corpus, it offers excellent performance across general-purpose embedding use-cases.
Release Date
18 Nov 2025
Context Size
512
Intfloat: E5-Large-v2
By intfloat
The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic search, reranking, and similarity-scoring tasks.
Release Date
18 Nov 2025
Context Size
512
Intfloat: E5-Base-v2
By intfloat
The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, similarity scoring, retrieval and clustering.
Release Date
18 Nov 2025
Context Size
512
Intfloat: Multilingual-E5-Large
By intfloat
The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized for multilingual retrieval, cross-language similarity, and large-scale data search.
Release Date
18 Nov 2025
Context Size
512
Sentence Transformers: paraphrase-MiniLM-L6-v2
By sentence-transformers
The paraphrase-MiniLM-L6-v2 embedding model converts sentences and short paragraphs into a 384-dimensional dense vector space, producing high-quality semantic embeddings optimized for paraphrase detection, semantic similarity scoring, clustering, and lightweight retrieval tasks.
Release Date
18 Nov 2025
Context Size
512
Sentence Transformers: all-MiniLM-L12-v2
By sentence-transformers
The all-MiniLM-L12-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, clustering, and similarity-scoring.
Release Date
18 Nov 2025
Context Size
512
BAAI: bge-base-en-v1.5
By baai
The bge-base-en-v1.5 embedding model converts English sentences and paragraphs into 768-dimensional dense vectors, delivering efficient, high-quality semantic embeddings optimized for retrieval, semantic search, and document-matching workflows. This version (v1.5) features improved similarity-score distribution and stronger retrieval performance out of the box.
Release Date
18 Nov 2025
Context Size
512
Sentence Transformers: multi-qa-mpnet-base-dot-v1
By sentence-transformers
The multi-qa-mpnet-base-dot-v1 embedding model transforms sentences and short paragraphs into a 768-dimensional dense vector space, generating high-quality semantic embeddings optimized for question-and-answer retrieval, semantic search, and similarity-scoring across diverse content.
Release Date
18 Nov 2025
Context Size
512
BAAI: bge-large-en-v1.5
By baai
The bge-large-en-v1.5 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-fidelity semantic embeddings optimized for semantic search, document retrieval, and downstream NLP tasks in English.
Release Date
18 Nov 2025
Context Size
512
BAAI: bge-m3
By baai
The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.
Release Date
18 Nov 2025
Context Size
8.19K
Sentence Transformers: all-mpnet-base-v2
By sentence-transformers
The all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and text ranking.
Release Date
17 Nov 2025
Context Size
512
Sentence Transformers: all-MiniLM-L6-v2
By sentence-transformers
The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering, similarity scoring, and text ranking.
Release Date
17 Nov 2025
Context Size
512
Showing page 7 of 25 with 737 models total