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by MiniMax
MiniMax M2 is a powerful MoE model with 200B total / 10B active parameters. Optimized for reasoning and coding tasks with excellent performance in multilingual scenarios. Features 128K context window and efficient inference.
MiniMax M2.7 is a MiniMaxM2 architecture model with an undisclosed parameter count, optimized for advanced reasoning and agentic workflows. It excels at complex tool use, self‑evolution, and professional software engineering tasks, achieving a 66.6% medal rate on MLE Bench Lite and a 56.2% score on SWE Bench Pro. The model also attains an ELO of 1495 on GDPval‑AA, surpassing other open‑weight models. Available under an Other license.
MiniMax M2.5 is a state-of-the-art reasoning MoE model with 229B total / 10B active parameters. Extensively trained with reinforcement learning across 200,000+ real-world environments, achieving SOTA performance in coding (80.2% SWE-Bench Verified), agentic tool use, search, and office productivity tasks. Features 197K context window, efficient MoE inference, and strong multilingual support.
MiniMax M2.1 is a state-of-the-art MoE model with 230B total / 10B active parameters, optimized for agentic coding and complex multi-step workflows. Excels at multilingual programming, tool use, and long-horizon planning. Matches Claude Sonnet 4.5 on code benchmarks and exceeds it in multilingual scenarios. Features 196K context window with FP8 efficiency. Released under Modified-MIT license for commercial use.
by Mistral
Mistral Small 3.2 24B Instruct is a multimodal instruction-tuned model supporting both vision and text with 24B parameters and 128K context. Major improvements over 3.1 include better instruction following (84.78%), 2x reduction in repetition errors, and robust function calling. Achieves 65.33% on Wildbench v2, 43.1% on Arena Hard v2, 92.90% on HumanEval Pass@5. Vision benchmarks: 87.4% ChartQA, 94.86% DocVQA, 62.50% MMMU. Supports up to 10 images per prompt with integrated vision-based function calling.
by intfloat
intfloat E5-Mistral-7B-Instruct is a state-of-the-art instruction-following embedding model built on Mistral 7B architecture. Combines strong language understanding from Mistral with specialized embedding training for retrieval tasks. Features instruction-based embedding generation allowing natural language queries to guide semantic search. Excels at complex retrieval scenarios, multi-hop reasoning in document search, and instruction-guided similarity tasks. Provides significantly improved zero-shot retrieval performance compared to traditional embedding models.
Mistral Small 4 is a 119B-parameter Mixture-of-Experts model (128 experts, 4 active per token, 6.5B active parameters) that unifies instruct, reasoning, and coding capabilities into a single multimodal model. It accepts text and image inputs, supports function calling, structured outputs, and configurable reasoning effort (none for fast responses, high for deep step-by-step reasoning). With a 256K context window and Apache 2.0 license, it delivers 40% lower latency and 3x higher throughput compared to Mistral Small 3.
by BAAI
BAAI BGE Multilingual Gemma2 is a multilingual dense retrieval embedding model built on Gemma 2 architecture, supporting 100+ languages for cross-lingual semantic search and retrieval. Delivers strong performance across diverse language families including English, Chinese, Spanish, Arabic, Hindi, and many more. Ideal for multilingual search systems, cross-lingual document retrieval, international content recommendation, and global knowledge bases. Trained on large-scale multilingual data with balanced language representation.
BAAI BGE-M3 is a versatile multilingual embedding model supporting dense, sparse, and multi-vector retrieval in a unified architecture. Handles 100+ languages with strong cross-lingual capabilities and flexible retrieval modes for different use cases. Features hybrid retrieval combining dense embeddings for semantic similarity, sparse representations for lexical matching, and multi-vector approaches for fine-grained relevance. Ideal for multilingual search engines, hybrid retrieval systems, and complex information retrieval scenarios requiring multiple matching strategies.
by Sentence Transformers
Sentence Transformers Paraphrase Multilingual MPNet is a multilingual sentence embedding model based on MPNet architecture, supporting 50+ languages for cross-lingual semantic similarity and paraphrase detection. Trained on large-scale paraphrase datasets across multiple languages enabling strong cross-lingual transfer. Ideal for multilingual paraphrase detection, semantic textual similarity, cross-lingual search, and international content deduplication. Provides balanced performance across diverse language families with proven track record in sentence-transformers ecosystem.
by Qwen
Image generation model from the Qwen series with advanced text rendering and precise image editing capabilities.
intfloat Multilingual E5 Large is a powerful multilingual dense retrieval embedding model supporting 100+ languages with strong cross-lingual capabilities. Features 1024-dimensional embeddings optimized for semantic search, document retrieval, and text similarity across diverse language families. Pre-trained on large-scale multilingual data with contrastive learning for robust cross-lingual transfer. Ideal for international search systems, multilingual document retrieval, and global content recommendation platforms requiring high-quality semantic understanding.
intfloat Multilingual E5 Large Instruct is an instruction-tuned multilingual embedding model combining strong cross-lingual capabilities with instruction-following for guided retrieval. Supports 100+ languages with natural language instructions to customize embedding behavior. Features enhanced zero-shot retrieval performance through instruction-based query understanding. Ideal for complex multilingual search scenarios, domain-specific retrieval tasks, and applications requiring adaptive semantic understanding across languages.