mixture-of-experts

LG AI Research has released K-EXAONE 2.0, a 750-billion-parameter mixture-of-experts language model with 37B active parameters, a 262,144-token context window, and support for 10 languages. The model is open-weighted under Apache 2.0 and claims competitive results against Qwen3.5, GLM-5.1, and DeepSeek-V4 Pro on reasoning, coding, and long-context benchmarks.

Thinking Machines has released Inkling Small, an open-weights reasoning model with 276 billion total parameters but only 12 billion active. According to Artificial Analysis, it scores nearly as high as the company's larger Inkling model while using roughly a third of the parameters and far fewer output tokens per task.

Thinking Machines Lab has released Inkling Small, an open-weight multimodal mixture-of-experts model with 12B active parameters out of 276B total and a 524K token context window. The model targets reasoning, coding, agentic workflows, and multilingual use cases at $0.58 per 1M input tokens and $1.44 per 1M output tokens.

DeepSeek has released V4 Flash 0731, a sparse mixture-of-experts model with 13B active parameters out of 284B total and a 1049K token context window. The model targets coding, reasoning, and agent workflows, priced at $0.14 per million input tokens and $0.28 per million output tokens via OpenRouter.

Moonshot AI has released Kimi K3, a 2.8-trillion-parameter open-weight mixture-of-experts model with a 1-million-token context window and native multimodal support. Unsloth has published Dynamic 2.0 quantized versions on Hugging Face, claiming improved accuracy over other quantization methods.

Moonshot AI has released Kimi K3, an open-weight 2.8-trillion-parameter mixture-of-experts model with 104B activated parameters, a 1,048,576-token context window, and native multimodal support. The company describes it as the world's first open 3T-class model, built on a new Kimi Delta Attention architecture.

Poolside has released Laguna S 2.1, a mixture-of-experts coding model with 8 billion active parameters out of 118 billion total, its third coding model release in three months. The company claims it outperforms open-weight models 10 to 20 times its size on agentic coding benchmarks like Terminal-Bench 2.1 and DeepSWE.

Thinking Machines Lab released Inkling, a mixture-of-experts model with 975B total parameters and 41B active parameters, trained on 45 trillion tokens across text, images, audio, and video. The Apache 2.0-licensed model supports up to 1M context and debuts alongside Inkling-Small (276B-A12B), marking what observers call the strongest US-based open-weight release to date.

Moonshot AI released Kimi K3 on July 16, 2026, a 2.8 trillion parameter mixture-of-experts model that ranks #2 on the Vals AI index and #3 on Artificial Analysis's Intelligence Index. The company will release the model's weights on July 27, making it the strongest open-weight model to date, surpassing all previous open releases including DeepSeek R1.

Meituan has released LongCat 2.0, a sparse mixture-of-experts language model with 48 billion active parameters out of 1.6 trillion total. The model features a 1,049,000 token context window and costs $0.30 per 1M input tokens and $1.20 per 1M output tokens.

Thinking Machines Lab released Inkling, a Mixture-of-Experts transformer with 975B total parameters and 41B active parameters, trained on 45 trillion tokens of text, images, audio and video. The Apache-2.0 licensed model is designed as a base for fine-tuning rather than a frontier model.

Thinking Machines Lab released Inkling, a 975-billion-parameter mixture-of-experts model that uses 41 billion active parameters per task. The open-weight model was trained on 45 trillion tokens across text, image, audio, and video, marking the first public release from Mira Murati's AI startup.

Amazon SageMaker AI now supports serverless fine-tuning for NVIDIA Nemotron 3 Nano (30B parameters, 3B active) and Nemotron 3 Super (120B parameters, 12B active). The integration includes supervised fine-tuning, reinforcement learning with verifiable rewards (RLVR), and reinforcement learning from AI feedback (RLAIF).

Poolside has released Laguna XS 2.1, a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token and a 262,144-token context window. The model achieves 70.9% on SWE-bench Verified and 63.1% on SWE-bench Multilingual, representing a 5.4% improvement over its predecessor on multilingual coding tasks.

Nex AGI has released Nex-N2-Mini, an open-source agentic mixture-of-experts model with a 262K-token context window. The model accepts text and image inputs and is priced at $0.025 per 1M input tokens and $0.10 per 1M output tokens.

Amazon Web Services has added three MiniMax models to Amazon Bedrock: M2, M2.1, and M2.5. The newest model, M2.5, uses a mixture-of-experts architecture with 230 billion total parameters and 10 billion active per token, trained specifically for agent-native execution and coding tasks.

Mistral AI has released Leanstral 1.5, an open-source 119B parameter mixture-of-experts model designed specifically for Lean 4 proof assistance. The model features 128 experts with 4 active per token (6.5B activated parameters), a 256k token context window, and multimodal input capabilities.

DeepReinforce has released Ornith-1.0, an MIT-licensed model designed for agentic coding tasks with variants ranging from 9B to 397B parameters. Built on top of Apache 2.0-licensed Gemma 4 and Qwen 3.5 base models, the company claims it achieves state-of-the-art performance among open-source models of comparable size on coding benchmarks.

DeepSeek has released two Mixture-of-Experts models: V4-Pro with 1.6 trillion parameters (49B activated) and V4-Flash with 284B parameters (13B activated), both supporting 1 million token context windows. V4-Pro requires only 27% of inference FLOPs and 10% of KV cache compared to V3.2 at 1M token context, trained on over 32 trillion tokens.

Poolside has released Laguna M.1, a 225B total parameter Mixture-of-Experts model with 23B activated parameters per token, designed for agentic coding tasks. The model scores 74.6% on SWE-bench Verified and 63.1% on SWE-bench Multilingual, released under Apache 2.0 license.

Mistral AI has launched Forge, a platform that allows enterprises to train custom AI models on their proprietary data including codebases, compliance policies, and operational documentation. The system supports both dense and mixture-of-experts architectures with pre-training, post-training, and reinforcement learning capabilities.

Mistral has released Mistral 3, including Mistral Large 3—a sparse mixture-of-experts model with 41B active and 675B total parameters—and three Ministral 3 edge models (3B, 8B, 14B). All models are released under Apache 2.0 license with multimodal capabilities and are available today on multiple platforms.

Google DeepMind released DiffusionGemma, a 26B parameter mixture-of-experts model that generates text using discrete diffusion instead of autoregression. The model processes blocks of 256 tokens in parallel, achieving generation speeds exceeding 1100 tokens per second on H100 GPUs in low-batch settings.

Nvidia has released Nemotron 3 Ultra, a 550-billion parameter mixture-of-experts model with 55 billion active parameters and support for up to 1 million token context windows. The model uses a hybrid Transformer-Mamba architecture and is designed specifically for long-running agentic workflows including agent orchestration, coding agents, and complex enterprise tasks.

Google DeepMind released Gemma 4, a family of open-weight multimodal models ranging from 2.3B to 30.7B parameters. The flagship 12B Unified model eliminates separate encoders, processing text, images, audio, and video directly through a single decoder-only transformer with up to 256K token context window.

JetBrains has released Mellum2-12B-A2.5B-Thinking, a reasoning-augmented assistant model with 131,072-token context window and 64 Mixture-of-Experts architecture that activates 8 experts per token. The model emits explicit chain-of-thought reasoning inside <think> blocks before providing final answers.

StepFun has released Step-3.7-Flash, a 198B-parameter sparse Mixture-of-Experts vision-language model that activates 11B parameters per token and delivers up to 400 tokens per second. The model supports a 256K context window, three selectable reasoning levels, and is priced at $0.20 per million input tokens (cache miss) and $1.15 per million output tokens.

Mistral AI has released Mistral Small 4, a 119B total parameter mixture-of-experts model with 256K context window and native multimodal capabilities. The model uses 128 experts with 4 active per token (6B active parameters) and is released under the Apache 2.0 license, marking Mistral's first unified model combining reasoning, multimodal, and coding capabilities.

Mistral AI has released Mistral 3, consisting of Mistral Large 3—a sparse mixture-of-experts model with 675B total parameters and 41B active parameters—and three Ministral 3 models at 3B, 8B, and 14B parameters. All models are released under the Apache 2.0 license with multimodal capabilities including image understanding.

Cohere has released Command A+ as an open source model under Apache 2.0 license. The sparse mixture-of-experts architecture features 25 billion active parameters out of 218B total parameters, supports 128K input context length, and includes vision capabilities alongside tool use and reasoning features.

Cohere has released Command A+, an open-source sparse mixture-of-experts model with 218 billion total parameters and 25 billion active parameters. The model features W4A4 quantization allowing deployment on a single Nvidia B200 GPU, supports 128K input context, and includes built-in chain-of-thought reasoning with vision capabilities.

Tencent has released Hy3 preview, a Mixture-of-Experts model with a 262,144 token context window priced at $0.066 per million input tokens and $0.26 per million output tokens. The model features three configurable reasoning modes—disabled, low, and high—designed for agentic workflows and production environments.

Allen Institute for AI released EMO, a 1B-active, 14B-total-parameter mixture-of-experts model trained on 1 trillion tokens. The model uses 8 active experts per token from a pool of 128 total experts, and can maintain near full-model performance while using just 12.5% of its experts for specific tasks.

Zyphra has released ZAYA1-8B, a mixture-of-experts language model with 760M active parameters and 8.4B total parameters. The model scores 89.1% on AIME 2026, competitive with models exceeding 100B parameters, while maintaining efficiency for on-device deployment.

Google DeepMind has released a Multi-Token Prediction assistant model for Gemma 4 26B A4B that achieves up to 2x decoding speedup through speculative decoding. The model uses 3.8B active parameters from a 25.2B total parameter MoE architecture with 128 experts and a 256K token context window.

Google DeepMind has released Gemma 4, a family of open-weight multimodal models including a 31B dense model with 256K context window and four size variants ranging from 2.3B to 30.7B effective parameters. The release includes Multi-Token Prediction (MTP) draft models that achieve up to 2x decoding speedup through speculative decoding while maintaining identical output quality.

Poolside has released Laguna XS.2, a 33B total parameter Mixture-of-Experts model with 3B activated parameters per token, designed for agentic coding. The model features a 131,072-token context window, scores 68.2% on SWE-bench Verified, and is available under Apache 2.0 license with free API access.

Xiaomi has released MiMo-V2.5-Pro, an open-source Mixture-of-Experts model with 1.02 trillion total parameters and 42 billion active parameters. The model supports up to 1 million tokens context length and claims 99.6% on GSM8K and 86.2% on MATH benchmarks.

Alibaba Cloud has released Qwen3.6 Max Preview, a proprietary frontier model built on sparse mixture-of-experts architecture with approximately 1 trillion total parameters. The model supports a 262,144-token context window and features integrated thinking mode for multi-turn reasoning, priced at $1.30 per million input tokens and $7.80 per million output tokens.

DeepSeek released V4 in two versions: a 284 billion parameter Flash model and a 1.6 trillion parameter Pro model with 49 billion active parameters. According to DeepSeek, the models use 9.5x-13.7x less memory than V3 through compressed attention mechanisms and FP4/FP8 mixed precision, while supporting a 1 million token context window.

Chinese AI lab DeepSeek has released preview versions of DeepSeek V4 Flash and V4 Pro, mixture-of-experts models with 1 million token context windows. The V4 Pro has 1.6 trillion total parameters (49 billion active), making it the largest open-weight model available, while both models significantly undercut frontier model pricing.

DeepSeek released two preview models: V4 Pro (1.6T total parameters, 49B active) and V4 Flash (284B total, 13B active), both with 1 million token context windows. V4 Pro is priced at $1.74/M input tokens and $3.48/M output—42% cheaper than Claude Sonnet 4.6—while V4 Flash at $0.14/$0.28 per million tokens undercuts all small frontier models.

DeepSeek released two Mixture-of-Experts models: V4-Flash with 284B total parameters (13B activated) and V4-Pro with 1.6T parameters (49B activated). Both models support one million token context windows and use a hybrid attention architecture that requires only 27% of the inference FLOPs compared to DeepSeek-V3.2 at 1M token context.

Tencent has released Hy3-preview, a 295-billion-parameter Mixture-of-Experts model with 21 billion active parameters and a 256K context window. The model scores 76.28% on MATH and 34.86% on LiveCodeBench-v6, with particularly strong performance on coding agent tasks.

Arcee AI has released Trinity-Large-Thinking, a 400-billion-parameter open-weight reasoning model with a mixture-of-experts architecture that activates only 13 billion parameters per token. The model matches Claude Opus 4.6 on agent benchmarks like Tau2 and PinchBench but lags on general reasoning tasks. The company spent approximately $20 million—roughly half its total venture capital—to train the model on 2,048 Nvidia B300 GPUs over 33 days.

Arcee AI released Trinity-Large-Thinking, a 398B-parameter sparse Mixture-of-Experts model with approximately 13B active parameters per token, post-trained with extended chain-of-thought reasoning for agentic workflows. The model achieves 94.7% on τ²-Bench, 91.9% on PinchBench, and 98.2% on LiveCodeBench, generating explicit reasoning traces in <think>...</think> blocks before producing responses.

Google DeepMind released Gemma 4, an open-weights multimodal model family in four sizes (2.3B to 31B parameters) with context windows up to 256K tokens. All models support text and image input, with audio native to E2B and E4B variants. The Gemma 4 31B dense model scores 85.2% on MMLU Pro, 89.2% on AIME 2026, and 80.0% on LiveCodeBench—significant improvements over Gemma 3.

Google DeepMind released Gemma 4, an open-weights multimodal model family in four sizes (E2B, E4B, 26B A4B, 31B) with context windows up to 256K tokens and native reasoning capabilities. The 26B A4B variant uses Mixture-of-Experts architecture with 3.8B active parameters for efficient inference. All models support text, image input and handle 140+ languages with Apache 2.0 licensing.

Google DeepMind has released Gemma 4, a family of open-weights multimodal models ranging from 2.3B to 31B parameters with support for text, images, video, and audio. The models feature context windows up to 256K tokens, built-in reasoning modes, and native function calling for agentic workflows.

Google DeepMind has released Gemma 4, an open-weights model family in four sizes (2.3B to 31B parameters) with multimodal capabilities handling text, images, video, and audio. The 26B A4B variant uses mixture-of-experts to achieve 4B active parameters while supporting 256K token context windows and native reasoning modes.