open-weights

Google DeepMind released the Gemma 4 E4B assistant model using Multi-Token Prediction (MTP) architecture that accelerates inference by up to 2x through speculative decoding. The 4.5B effective parameter model supports 128K context windows and handles text, image, and audio input with pricing not yet disclosed.

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.

Mistral AI released Mistral Medium 3.5, a 128B parameter dense model with a 256k context window that unifies instruction-following, reasoning, and coding capabilities. The model features configurable reasoning effort per request and a vision encoder trained from scratch for variable image sizes.

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.

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.

Alibaba Qwen released Qwen3.6-27B, a 27B parameter dense model that claims flagship-level coding performance surpassing their previous 397B MoE model across major coding benchmarks. The full model is 55.6GB compared to 807GB for the predecessor.

Alibaba Qwen released Qwen3.6-27B, a 27-billion parameter model with a 262,144 token context window extensible to 1,010,000 tokens. The model claims 77.2% on SWE-bench Verified and 53.5% on SWE-bench Pro, with open weights available on Hugging Face.

Open-weight models from Google, Alibaba, Microsoft, and Nvidia have crossed a threshold from research projects to enterprise-grade systems. The shift reflects a growing divide: frontier models from OpenAI and Anthropic are too expensive and pose data security risks for most enterprises, while open alternatives now deliver sufficient capability at a fraction of the cost.

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.

Z.ai has released GLM-5.1, a 754-billion parameter open-weight model matching the size of its predecessor GLM-5. The model demonstrates improved ability to generate complex, multi-part outputs like HTML pages with SVG graphics and CSS animations, available via Hugging Face and OpenRouter.

Google DeepMind released the Gemma 4 family of open-weights multimodal models in four sizes: E2B (2.3B effective parameters), E4B (4.5B effective), 26B A4B (3.8B active parameters), and 31B dense. All models support text and image input with 128K-256K context windows; E2B and E4B add native audio capabilities. Models feature reasoning modes, function calling, and multilingual support across 140+ languages.

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 released Gemma 4, a new line of open-weights models available in sizes from 2 billion to 31 billion parameters, under a permissive Apache 2.0 license. The release includes multimodal capabilities, support for 140+ languages, native function calling, and a 256,000-token context window for the larger variants.

Google DeepMind released Gemma 4, a family of open-weights multimodal models in four sizes: E2B (2.3B effective), E4B (4.5B effective), 26B A4B (3.8B active), and 31B (30.7B parameters). All models support text and image input with 128K-256K context windows, while E2B and E4B add native audio capabilities and reasoning modes across 140+ languages.

Google DeepMind released the Gemma 4 family of open-weights multimodal models in four sizes: E2B (2.3B effective), E4B (4.5B effective), 26B A4B (25.2B total, 3.8B active), and 31B dense. All models support text and image input with 128K-256K context windows, reasoning modes, and native function calling for agentic workflows.

Google DeepMind released the Gemma 4 family of open-weights models ranging from 2.3B to 31B parameters, featuring up to 256K token context windows and native support for text, image, video, and audio inputs. The flagship 31B model scores 85.2% on MMLU Pro and 89.2% on AIME 2026, with a smaller 26B MoE variant requiring only 3.8B active parameters for faster inference.

Mistral has released Voxtral TTS, a 4-billion-parameter text-to-speech model that can clone voices from just three seconds of reference audio across nine languages. The model delivers 70ms latency for typical 10-second samples and outperformed ElevenLabs Flash v2.5 in naturalness tests. Voxtral is available via API at $0.016 per 1,000 characters and as open-weights on Hugging Face.

Mistral AI released Voxtral-4B-TTS-2603, an open-weights text-to-speech model designed for production voice agents. The 4B-parameter model supports 9 languages, 20 preset voices, achieves 70ms latency at concurrency 1 on a single NVIDIA H200, and requires only 16GB GPU memory.

NVIDIA Nemotron 3 Super, a hybrid Mixture of Experts model with 120B parameters and 12B active parameters, is now available as a fully managed model on Amazon Bedrock. The model supports up to 256K token context length and claims 5x higher throughput efficiency over the previous Nemotron Super and 2x higher accuracy on reasoning tasks.

Mistral has released Leanstral, a 120B-parameter code verification agent built with the Lean programming language, claiming it outperforms larger open-source models and offers significant cost advantages over Anthropic's Claude suite. The model achieves a pass@2 score of 26.3—beating Claude Sonnet by 2.6 points—while costing $36 to run compared to Sonnet's $549.