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rrecting in the background — using raw speed to generate depth of thought, directly elevating reasoning quality. Second, it completely unleashes the productivity ceiling of Coding Agents. Before, having AI write code meant developers painfully waiting in front of screens, bottlenecked by inference latency. At 1000 tps, code generation speed and production efficiency undergo a paradigm-level acceleration. Most importantly, trillion-parameter models can now enter real-time decision loops. Millisec

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June 4, 2026 NVIDIA Nemotron 3 Ultra is now available on Ollama’s cloud. It’s a 550 billion parameter (55B active) open model from NVIDIA built for long-running, agentic workflows with fast and affordable performance across hundreds of tool calls. Model highlights Built for long-running agents: Tuned for agent orchestration, coding agents, deep research, and complex enterprise workflows that run across hundreds of steps; 1M token context: Keep entire codebases, long tool histories, and research

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Contents We are excited to announce that SGLang and Miles support NVIDIA Nemotron 3 Ultra on Day 0. Agentic AI systems are moving from short prompt-response interactions to persistent workflows that plan, use tools, inspect results, recover from failures, and continue working across long task horizons. These agents need strong reasoning, fast inference, long-context understanding, and reliable tool use in the same deployment stack. Nemotron 3 Ultra is built for this class of workloads. Part of t

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‑Voice‑2, MAI‑Transcribe‑1.5, and MAI‑Image‑2.5 models in action – showcasing natural, fluid, expressive dialogue. In the demo, you can engage in a three‑way conversation with two agents and even generate images using MAI‑Image‑2.5. It’s a practical preview of how MAI multimodal models work together to build powerful, customizable voice agents. Try DuoAI now Note: DuoAI is not meant to showcase the capabilities of the underlying LLM – that component is modular and can be swapped as needed. You c

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trol, and its available in 15 languages with lots more coming soon. We’re also announcing Voice-2-Flash – which provides the best value and speed for ultra latency-sensitive Voice Agents, the big thing in 2026. Next up is our text foundation model, MAI-Thinking-1, our first reasoning model.7 It’s exceptionally strong in our target use cases of reasoning and SWE tasks. It’s a 35B active parameter MoE with a 256K context window. That means it competes in the medium-sized weight class, where it’s c

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our blind human side-by-side evaluations. We trained it from the ground up on clean data, without distillation from third-party models; MAI-Code-1-Flash is an inference-efficient agentic coding model. This model is tailor-made for and deeply integrated into GitHub Copilot, VS Code and the Microsoft stack, and, with 5 billion active parameters, is comparable to Haiku but cheaper; MAI-Image-2.5 including its ultra-efficient Flash variant, supports both world-class text-to-image and image editing,

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at in multi-speaker audio – essential for meetings, interviews, and call center analytics; A native streaming API, enabling real-time transcription for live applications and voice agents, moving beyond the current batch-first approach; Expanded language support – giving each new language the same depth of accuracy and robustness as the existing 43 languages You can also explore the models directly in the MAI Playground. Build the Future With Us We’re a lean, fast-moving lab made up of some of th

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rd-party models. The model is rolling out to GitHub Copilot individual users in Visual Studio Code in the model picker and under the default auto picker. Features and capabilities Agentic coding in real developer environments, trained and designed for GitHub Copilot harness, to work better together; Adaptive thinking, stays concise for simple requests and spends more reasoning budget on complex tasks; Strong instruction-following across single-turn and multi-turn scenarios MAI-Code-1-Flash is de

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Qwen3.7-Plus: Multimodal Agent Intelligence | Qwen Blog Publication About Try Qwen Chat Qwen3.7-Plus: Multimodal Agent Intelligence May 21, 2026 · 16 min · 3307 words · Qwen Team | Translations: 简体中文 DISCORD Today we introduce Qwen3.7-Plus — a multimodal agent model that unifies vision and language into a single, versatile agent foundation. Building on Qwen3.7’s strong text backbone, Qwen3.7-Plus delivers a comprehensive upgrade in vision-language capabilities while retaining full agentic streng

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MiniMax M3 is officially released today. M3 reaches frontier-level performance on specialized tasks such as coding and agentic work. It uses MSA (MiniMax Sparse Attention), a new attention architecture proposed by our team, and supports ultra-long context windows of up to 1M tokens. To much anticipation, it is also a natively multimodal model that supports image and video input and can operate a desktop computer. These three capabilities are now table stakes for closed-source frontier models. M3

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Our latest coding model, grok-build-0.1 , is now available via the xAI API in public beta. grok-build-0.1 is a coding model specifically trained for agentic coding tasks, including web development, debugging, and MCP support. It's the same model that powers Grok Build . The model is served at a blazing-fast 100+ tokens/second and is priced at $1/m tokens in and $2/m tokens out. Outside of coding, it’s also a speedy, economical option for general-purpose agentic and tool calling use cases. Copy c

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three times cheaper than it was for previous models. Opus 4.8’s capabilities The table below shows how Opus 4.8 compares to its predecessor and to other models on tests of coding, agentic skills, reasoning, and practical knowledge work tasks. More details and a much wider range of capability evaluations are provided in the Claude Opus 4.8 System Card. Collaborating with Opus 4.8 Early testers have found Claude Opus 4.8 to be more reliable and sharper in its judgment when it’s performing agentic

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ons, chaining tool calls, and following complex instructions on all devices; Compressed performance. Competitive with much larger dense and MoE models on instruction following and agentic tasks; Unmatched throughput. Fastest in its size class on both CPU and GPU inference, with day-one support for llama.cpp, MLX, vLLM, and SGLang What changed since LFM2-8B-A1B Compared to LFM2-8B-A1B, this new version expands the context window from 32,768 to 128,000 tokens. This allows the model to process long

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Mistral has moved coding agents to the cloud, enabling parallel, asynchronous task execution via Mistral Medium 3.5—a new 128B open-weight model optimized for long-horizon coding and productivity work. The update introduces remote agents in Mistral Vibe and Le Chat, along with a new Work mode for multi-step tasks like research and cross-tool workflows, all while maintaining human oversight for sensitive actions. Available today on Pro, Team, and Enterprise plans, Mistral Medium 3.5 is priced at

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Qwen3.7: The Agent Frontier | Qwen Blog Publication About Try Qwen Chat Qwen3.7: The Agent Frontier May 16, 2026 · 17 min · 3543 words · Qwen Team | Translations: 简体中文 DISCORD Today we introduce Qwen3.7-Max , our latest proprietary model designed for the agent era. Qwen3.7-Max is built to be a versatile agent foundation — equally capable of writing and debugging code, automating office workflows, and sustaining autonomous execution across hundreds or thousands of steps. What sets Qwen3.7-Max apa

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Today, we’re releasing Command A+ open-source. A mixture-of-experts (MoE) model, Command A+ is an efficient, versatile, and privately deployable LLM built for high-performance agentic tasks with minimal compute overhead. Born from a year of deploying North with our customers, it surpasses every previous generation in the Command series and unifies their capabilities into a single scalable model. Now freely available under an Apache 2.0 license, Command A+ advances Cohere’s mission to make sovere

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The metrics on 3.5 Flash are major: 4x faster than other frontier models in output tokens per second Outperforms our previous 3.1 Pro model on nearly all benchmarks Shows massive improvement on coding and agentic benchmarks like Terminal-Bench 2.1 (76.2%), GDPval-AA (1656 Elo) and MCP Atlas (83.6%)

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avg@1 and pass@4 scores for our model. We view the pass@4 scores as evidence that the scaled ZAYA1-74B-Preview base model is already capable of producing successful reasoning and agentic trajectories with meaningful frequency. This suggests there is sufficient signal available for verifiable and agentic RL to build on in the corresponding domains. While the reasoning-base model unsurprisingly lags the fully post-trained competitor models, our pass@4 scores are highly competitive and provide sig

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deployment, continued training, and fine-tuning - no additional authorization required. Two models, both supporting a 1M-token context window : • MiMo-V2.5-Pro: built for complex agent and coding tasks, ranking No.1 among open-source models on GDPVal-AA and ClawEval • MiMo-V2.5: a native omni-modal model with strong agent capabilities A model's value isn't measured by rankings alone — it's measured by the problems it solves. Let's build with MiMo now! 🤗 Weights: https://t.co/w7wNtXj9Rm 📄 Blog:

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Introducing MiMo-V2.5 Voice — our full-stack voice lineup for the Agent era. 🚀 Voice AI is moving beyond just hearing and reading — towards precise understanding and flexible expression. That’s why today we’re launching the MiMo-V2.5-TTS Series and open-sourcing MiMo-V2.5-ASR. The TTS series includes three models: 🔹 MiMo-V2.5-TTS — premium built-in voices with fine-grained style control 🔹 MiMo-V2.5-TTS-VoiceDesign — generate brand-new voices from natural-language descriptions 🔹 MiMo-V2.5-TTS-Voi

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