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by ZAI
GLM-4.6 is a frontier-scale 355B parameter Mixture-of-Experts model with a 200K context window and 128K output capability. MIT licensed, making it the only model in its class that enterprises can self-host and deeply customize. Dominates LiveCodeBench v6 (#1, 82.8%), HLE (#1), excels at AIME 2025 (#3, 93.9%) and Terminal-Bench (#3, 40.5%). Near parity with Claude Sonnet 4 (48.6% win rate) while dramatically outperforming other open-source baselines. Purpose-built for agentic workflows, real-world coding, and tool-augmented problem-solving. Supports native tool calling during inference for complex multi-step tasks.
GLM-4.5 Air is a compact 106B parameter Mixture-of-Experts model with 12B active parameters, optimized for efficiency while maintaining strong performance. Scores 59.8 across 12 industry benchmarks with superior resource efficiency compared to full GLM-4.5. Features hybrid reasoning mode with 128K context, supports intelligent agent functions and tool calling. Released under MIT license with commercial use allowed. Ideal for deployment scenarios requiring balance between capability and computational cost.
GLM-4.5 is a 355B parameter Mixture-of-Experts foundation model with 32B active parameters, designed for intelligent agents. Features hybrid reasoning mode with configurable thinking enabled by default. Ranks 3rd place at 63.2 across 12 industry benchmarks among all proprietary and open-source models. Released under MIT license with 128K context, supports reasoning, coding, and intelligent agent functions including OpenAI-style tool calling. Incorporates MTP (Multi-Token Prediction) layers with speculative decoding for efficient inference.
GLM-4.7 is Z.ai's latest large language model with enhanced reasoning capabilities. Excels at mathematical problem solving, coding, and complex logical tasks. Features improved context understanding and multilingual support.
ZAI GLM 5.1 is a 744B parameter Mixture-of-Experts language model built with the GLM‑MoE DSA architecture. It excels at agentic engineering, achieving state-of-the-art performance on benchmarks such as HLE with tools (52.3), SWE‑Bench Pro (58.4) and AIME 2026 (95.3). The model supports extensive tool use and long‑horizon reasoning, with a large context window of up to 128K tokens. It is released under the MIT license.
ZAI's frontier 744B MoE model (40B activated) with 203K context. Excels at agentic engineering, coding (SWE-bench 77.8%), reasoning, and tool use. Built with asynchronous RL and MIT licensed.
by Google
Google Gemma 4 31B is a 31B parameter dense multimodal language model with a 256K context window. It processes text, images, and video inputs and generates text output, featuring a configurable thinking mode for step‑by‑step reasoning. The model achieves 85.2% on MMLU Pro, 80.0% on LiveCodeBench v6, and 88.4% on MMMLU, demonstrating strong performance across reasoning and multimodal benchmarks. Available under the Apache 2.0 license.
by NousResearch
NousResearch Hermes 4 405B is the flagship hybrid-mode reasoning model based on Meta's Llama-3.1-405B architecture. Trained on a massive ~60B token corpus with explicit <think> deliberation segments, it delivers frontier-level performance in math, code, STEM, logic, and creative tasks. Achieves SOTA on RefusalBench for helpful, uncensored responses aligned to user values. Supports advanced function calling, structured JSON outputs, and tool use with extreme steerability and reduced refusal rates.
NousResearch Hermes 4 70B is a frontier hybrid-mode reasoning model based on Llama-3.1-70B, trained on ~60B tokens across ~5M samples. Features explicit <think> deliberation segments with massive improvements in math, code, STEM, logic, and creativity. Achieves SOTA on RefusalBench for helpful, uncensored responses while maintaining alignment to user values. Supports schema-adherent structured JSON outputs, function calling, and tool use. Trained for extreme steerability with reduced refusal rates compared to previous Hermes versions.
by Mistral
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.
Larger Gemma model delivering high-quality chat and coding with efficient inference.
Gemma 3 27B IT is a cutting-edge multimodal vision-language model with 27 billion parameters, built on Gemini technology. Trained on 14 trillion tokens, it handles both text and image inputs with a 128K context window and supports 140+ languages. Excels at visual understanding, code generation, mathematical reasoning, and multilingual tasks. Achieves 78.6 on MMLU, 82.6 on GSM8K, 85.6 on DocVQA, and 76.3 on ChartQA. Lightweight enough for laptop deployment with strong safety improvements over previous Gemma versions.
by Black Forest Labs
Black Forest Labs FLUX.2 [klein] 4B is a lightweight, fast image generation model optimized for speed and efficiency. With 4 billion parameters, it delivers quick image generation while maintaining good quality. Perfect for rapid prototyping, bulk generation, and applications requiring low latency. Supports both text-to-image and image-to-image generation with excellent cost-efficiency.
by Meta
Moderation model providing robust safety classification and policy enforcement.
by Qwen
Qwen3 Coder 480B A35B Instruct is a specialized Mixture-of-Experts coding model with 480B total parameters and 35B activated. Optimized specifically for code generation, code understanding, debugging, and software engineering tasks. Features 262K native context for handling large codebases, strong performance on coding benchmarks including LiveCodeBench and HumanEval, and support for multiple programming languages. Excels at complex algorithmic problems, code refactoring, and technical documentation generation.
by OpenAI
GPT-OSS 120B is a powerful 117B parameter Mixture-of-Experts reasoning model with 5.1B active parameters, released under Apache 2.0. Features configurable reasoning effort (low/medium/high), full chain-of-thought visibility, and runs on a single 80GB GPU thanks to MXFP4 quantization. Native support for function calling, web browsing, Python code execution, and structured outputs. Designed for agentic tasks and complex reasoning with production-grade performance. Fully customizable for specialized use cases on single H100/MI300X.
GPT-OSS 20B is a compact 21B parameter Mixture-of-Experts model with 3.6B active parameters, designed for lower latency and local deployment. Runs within 16GB memory with configurable reasoning effort, full chain-of-thought access, and native agentic capabilities including function calling and structured outputs. Released under Apache 2.0 license, ideal for specialized fine-tuning on consumer hardware. Companion model to GPT-OSS 120B optimized for speed while maintaining strong reasoning capabilities.
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.
Qwen2-based text embedding model optimized for semantic similarity and retrieval tasks.
Meta's flagship 405B parameter model representing the pinnacle of open-source AI. Exceptional reasoning and comprehensive knowledge for demanding applications.