Alibaba's Qwen3 Models Set New Benchmarks in Open-Source AI

It's Qwen's summer: new open source Qwen3-235B-A22B-Thinking-2507 tops OpenAI, Gemini reasoning models on key benchmarks

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now If the AI industry had an equivalent to the recording industry's "song of the summer" -- a hit that catches on in the warmer months here in the Northern Hemisphere and is heard playing everywhere -- the clear honoree for that title would go to Alibaba's Qwen Team. Over just the past week, the frontier model AI research division of the Chinese e-commerce behemoth has released not one, not two, not three, but four (!!) new open source generative AI models that offer record-setting benchmarks, besting even some leading proprietary options. Last night, Qwen Team capped it off with the release of Qwen3-235B-A22B-Thinking-2507, it's updated reasoning large language model (LLM), which takes longer to respond than a non-reasoning or "instruct" LLM, engaging in "chains-of-thought" or self-reflection and self-checking that hopefully result in more correct and comprehensive responses on more difficult tasks. Indeed, the new Qwen3-Thinking-2507, as we'll call it for short, now leads or closely trails top-performing models across several major benchmarks. As AI influencer and news aggregator Andrew Curran wrote on X: "Qwen's strongest reasoning model has arrived, and it is at the frontier." In the AIME25 benchmark -- designed to evaluate problem-solving ability in mathematical and logical contexts -- Qwen3-Thinking-2507 leads all reported models with a score of 92.3, narrowly surpassing both OpenAI's o4-mini (92.7) and Gemini-2.5 Pro (88.0). The model also shows a commanding performance on LiveCodeBench v6, scoring 74.1, ahead of Gemini-2.5 Pro (72.5), OpenAI o4-mini (71.8), and significantly outperforming its earlier version, which posted 55.7. In GPQA, a benchmark for graduate-level multiple-choice questions, the model achieves 81.1, nearly matching Deepseek-R1-0528 (81.0) and trailing Gemini-2.5 Pro's top mark of 86.4. On Arena-Hard v2, which evaluates alignment and subjective preference through win rates, Qwen3-Thinking-2507 scores 79.7, placing it ahead of all competitors. The results show that this model not only surpasses its predecessor in every major category but also sets a new standard for what open-source, reasoning-focused models can achieve. A shift away from 'hybrid reasoning' The release of Qwen3-Thinking-2507 reflects a broader strategic shift by Alibaba's Qwen team: moving away from hybrid reasoning models that required users to manually toggle between "thinking" and "non-thinking" modes. Instead, the team is now training separate models for reasoning and instruction tasks. This separation allows each model to be optimized for its intended purpose -- resulting in improved consistency, clarity, and benchmark performance. The new Qwen3-Thinking model fully embodies this design philosophy. Alongside it, Qwen launched Qwen3-Coder-480B-A35B-Instruct, a 480B-parameter model built for complex coding workflows. It supports 1 million token context windows and outperforms GPT-4.1 and Gemini 2.5 Pro on SWE-bench Verified. Also announced was Qwen3-MT, a multilingual translation model trained on trillions of tokens across 92+ languages. It supports domain adaptation, terminology control, and inference from just $0.50 per million tokens. Earlier in the week, the team released Qwen3-235B-A22B-Instruct-2507, a non-reasoning model that surpassed Claude Opus 4 on several benchmarks and introduced a lightweight FP8 variant for more efficient inference on constrained hardware. All models are licensed under Apache 2.0 and are available through Hugging Face, ModelScope, and the Qwen API. Licensing: Apache 2.0 and its enterprise advantage Qwen3-235B-A22B-Thinking-2507 is released under the Apache 2.0 license, a highly permissive and commercially friendly license that allows enterprises to download, modify, self-host, fine-tune, and integrate the model into proprietary systems without restriction. This stands in contrast to proprietary models or research-only open releases, which often require API access, impose usage limits, or prohibit commercial deployment. For compliance-conscious organizations and teams looking to control cost, latency, and data privacy, Apache 2.0 licensing enables full flexibility and ownership. Availability and pricing Qwen3-235B-A22B-Thinking-2507 is available now for free download on Hugging Face and ModelScope. For those enterprises who don't want to or don't have the resources and capability to host the model inference on their own hardware or virtual private cloud through Alibaba Cloud's API, vLLM, and SGLang. * Input price: $0.70 per million tokens * Output price: $8.40 per million tokens * Free tier: 1 million tokens, valid for 180 days Integration and tooling The model is compatible with agentic frameworks via Qwen-Agent, and supports advanced deployment via OpenAI-compatible APIs. It can also be run locally using transformer frameworks or integrated into dev stacks through Node.js, CLI tools, or structured prompting interfaces. Sampling settings for best performance include temperature=0.6, top_p=0.95, and max output length of 81,920 tokens for complex tasks. Enterprise applications and future outlook With its strong benchmark performance, long-context capability, and permissive licensing, Qwen3-Thinking-2507 is particularly well suited for use in enterprise AI systems involving reasoning, planning, and decision support. The broader Qwen3 ecosystem -- including coding, instruction, and translation models -- further extends the appeal to technical teams and business units looking to incorporate AI across verticals like engineering, localization, customer support, and research. The Qwen team's decision to release specialized models for distinct use cases, backed by technical transparency and community support, signals a deliberate shift toward building open, performant, and production-ready AI infrastructure. As more enterprises seek alternatives to API-gated, black-box models, Alibaba's Qwen series increasingly positions itself as a viable open-source foundation for intelligent systems -- offering both control and capability at scale.

Alibaba's new open source Qwen3-235B-A22B-2507 beats Kimi-2 and offers low compute version

Chinese e-commerce giant Alibaba has made waves globally in the tech and business communities with its own family of "Qwen" generative AI large language models, beginning with the launch of the original Tongyi Qianwen LLM chatbot in April 2023 through the release of Qwen 3 in April 2025. Why? Well, not only are its models powerful and score high on third-party benchmark tests at completing math, science, reasoning, and writing tasks, but for the most part, they've been released under permissive open source licensing terms, allowing organizations and enterprises to download them, customize them, run them, and generally use them for all variety of purposes, even commercial. Think of them as an alternative to DeepSeek. This week, Alibaba's "Qwen Team," as its AI division is known, released the latest updates to its Qwen family, and they're already attracting attention once more from AI power users in the West for their top performance, in one case, edging out even the new Kimi-2 model from rival Chinese AI startup Moonshot released in mid-July 2025. The new Qwen3-235B-A22B-2507-Instruct model -- released on AI code sharing community Hugging Face alongside a "floating point 8" or FP8 version, which we'll cover more in-depth below -- improves from the original Qwen 3 on reasoning tasks, factual accuracy, and multilingual understanding. It also outperforms Claude Opus 4's "non-thinking" version. The new Qwen3 model update also delivers better coding results, alignment with user preferences, and long-context handling, according to its creators. But that's not all... Read on for what else it offers enterprise users and technical decision-makers. FP8 version lets enterprises run Qwen 3 with far less memory and far less compute In addition to the new Qwen3-235B-A22B-2507 model, the Qwen Team released an "FP8" version, which stands for 8-bit floating point, a format that compresses the model's numerical operations to use less memory and processing power -- without noticeably affecting its performance. In practice, this means organizations can run a model with Qwen3's capabilities on smaller, less expensive hardware or more efficiently in the cloud. The result is faster response times, lower energy costs, and the ability to scale deployments without needing massive infrastructure. This makes the FP8 model especially attractive for production environments with tight latency or cost constraints. Teams can scale Qwen3's capabilities to single-node GPU instances or local development machines, avoiding the need for massive multi-GPU clusters. It also lowers the barrier to private fine-tuning and on-premises deployments, where infrastructure resources are finite and total cost of ownership matters. Even though Qwen team didn't release official calculations, comparisons to similar FP8 quantized deployments suggest the efficiency savings are substantial. Here's a practical illustration: Estimates based on industry norms for FP8 deployments. Actual results vary by batch size, prompt length, and inference framework (e.g., vLLM, Transformers, SGLang). No more 'hybrid reasoning'...instead Qwen will release separate reasoning and instruct models! Perhaps most interesting of all, Qwen Team announced it will no longer be pursuing a "hybrid" reasoning approach, which it introduced back with Qwen 3 in April and seemed to be inspired by an approach pioneered by sovereign AI collective Nous Research. This allowed users to toggle on a "reasoning" model, letting the AI model engage in its own self-checking and producing "chains-of-thought" before responding. In a way, it was designed to mimic the reasoning capabilities of powerful proprietary models such as OpenAI's "o" series (o1, o3, o4-mini, o4-mini-high), which also produce "chains-of-thought." However, unlike those rival models which always engage in such "reasoning" for every prompt, Qwen 3 could have the reasoning mode manually switched on or off by the user by clicking a "Thinking Mode" button on the Qwen website chatbot, or by typing "/think" before their prompt on a local or privately run model inference. The idea was to give users control to engage the slower and more token-intensive thinking mode for more difficult prompts and tasks, and use a non-thinking mode for simpler prompts. But again, this put the onus on the user to decide. While flexible, it also introduced design complexity and inconsistent behavior in some cases. Now As Qwen team wrote in its announcement post on X: "After talking with the community and thinking it through, we decided to stop using hybrid thinking mode. Instead, we'll train Instruct and Thinking models separately so we can get the best quality possible." With the 2507 update -- an instruct or NON-REASONING model only, for now -- Alibaba is no longer straddling both approaches in a single model. Instead, separate model variants will be trained for instruction and reasoning tasks respectively. The result is a model that adheres more closely to user instructions, generates more predictable responses, and, as benchmark data shows, improves significantly across multiple evaluation domains. Performance benchmarks and use cases Compared to its predecessor, the Qwen3-235B-A22B-Instruct-2507 model delivers measurable improvements: * MMLU-Pro scores rise from 75.2 to 83.0, a notable gain in general knowledge performance. * GPQA and SuperGPQA benchmarks improve by 15-20 percentage points, reflecting stronger factual accuracy. * Reasoning tasks such as AIME25 and ARC-AGI show more than double the previous performance. * Code generation improves, with LiveCodeBench scores increasing from 32.9 to 51.8. * Multilingual support expands, aided by improved coverage of long-tail languages and better alignment across dialects. The model maintains a mixture-of-experts (MoE) architecture, activating 8 out of 128 experts during inference, with a total of 235 billion parameters -- 22 billion of which are active at any time. As mentioned before, the FP8 version introduces fine-grained quantization for better inference speed and reduced memory usage. Enterprise-ready by design Unlike many open-source LLMs, which are often released under restrictive research-only licenses or require API access for commercial use, Qwen3 is squarely aimed at enterprise deployment. Boasting a permissive Apache 2.0 license, this means enterprises can use it freely for commercial applications. They may also: * Deploy models locally or through OpenAI-compatible APIs using vLLM and SGLang * Fine-tune models privately using LoRA or QLoRA without exposing proprietary data * Log and inspect all prompts and outputs on-premises for compliance and auditing * Scale from prototype to production using dense variants (from 0.6B to 32B) or MoE checkpoints Alibaba's team also introduced Qwen-Agent, a lightweight framework that abstracts tool invocation logic for users building agentic systems. Benchmarks like TAU-Retail and BFCL-v3 suggest the instruction model can competently execute multi-step decision tasks -- typically the domain of purpose-built agents. Community and industry reactions The release has already been well received by AI power users. Paul Couvert, AI educator and founder of private LLM chatbot host Blue Shell AI, posted a comparison chart on X showing Qwen3-235B-A22B-Instruct-2507 outperforming Claude Opus 4 and Kimi K2 on benchmarks like GPQA, AIME25, and Arena-Hard v2, calling it "even more powerful than Kimi K2... and even better than Claude Opus 4." AI influencer NIK (@ns123abc), commented on its rapid impact: "You're laughing. Qwen-3-235B made Kimi K2 irrelevant after only one week despite being one quarter the size and you're laughing." Meanwhile, Jeff Boudier, head of product at Hugging Face, highlighted the deployment benefits: "Qwen silently released a massive improvement to Qwen3... it tops best open (Kimi K2, a 4x larger model) and closed (Claude Opus 4) LLMs on benchmarks." He praised the availability of an FP8 checkpoint for faster inference, 1-click deployment on Azure ML, and support for local use via MLX on Mac or INT4 builds from Intel. The overall tone from developers has been enthusiastic, as the model's balance of performance, licensing, and deployability appeals to both hobbyists and professionals. What's next for Qwen team? Alibaba is already laying the groundwork for future updates. A separate reasoning-focused model is in the pipeline, and the Qwen roadmap points toward increasingly agentic systems capable of long-horizon task planning. Multimodal support, seen in Qwen2.5-Omni and Qwen-VL models, is also expected to expand further. And already, rumors and rumblings have started as Qwen team members tease yet another update to their model family incoming, with updates on their web properties revealing URL strings for a new Qwen3-Coder-480B-A35B-Instruct model, likely a 480-billion parameter mixture-of-experts (MoE) with a token context of 1 million. What Qwen3-235B-A22B-Instruct-2507 ultimately signals is not just another leap in benchmark performance, but a maturation of open models as viable alternatives to proprietary systems. The flexibility of deployment, strong general performance, and enterprise-friendly licensing give the model a unique edge in a crowded field. For teams looking to integrate advanced instruction-following models into their AI stack -- without the limitations of vendor lock-in or usage-based fees -- Qwen3 is a serious contender.

Qwen3-Coder-480B-A35B-Instruct launches and it 'might be the best coding model yet'

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Chinese e-commerce giant Alibaba's "Qwen Team" has done it again. Mere days after releasing for free and with open source licensing what is now the top performing non-reasoning large language model (LLM) in the world -- full stop, even compared to proprietary AI models from well-funded U.S. labs such as Google and OpenAI -- in the form of the lengthily named Qwen3-235B-A22B-2507, this group of AI researchers has come out with yet another blockbuster model. That is Qwen3-Coder-480B-A35B-Instruct, a new open-source LLM focused on assisting with software development. It is designed to handle complex, multi-step coding workflows and can create full-fledged, functional applications in seconds or minutes. The model is positioned to compete with proprietary offerings like Claude Sonnet-4 in agentic coding tasks and sets new benchmark scores among open models. It is available on Hugging Face, GitHub, Qwen Chat, via Alibaba's Qwen API, and a growing list of third-party vibe coding and AI tool platforms. Open sourcing licensing means low cost and high optionality for enterprises But unlike Claude and other proprietary models, Qwen3-Coder, which we'll call it for short, is available now under an open source Apache 2.0 license, meaning it's free for any enterprise to take without charge, download, modify, deploy and use in their commercial applications for employees or end customers without paying Alibaba or anyone else a dime. It's also so highly performant on third-party benchmarks and anecdotal usage among AI power users for "vibe coding" -- coding using natural language and without formal development processes and steps -- that at least one, LLM researcher Sebastian Raschka, wrote on X that: "This might be the best coding model yet. General-purpose is cool, but if you want the best at coding, specialization wins. No free lunch." Developers and enterprises interested in downloading it can find the code on the AI code sharing repository Hugging Face. Enterprises who don't wish to, or don't have the capacity to host the model on their own or through various third-party cloud inference providers, can also use it directly through the Alibaba Cloud Qwen API, where the per-million token costs start at $1/$5 per million tokens (mTok) for input/output of up to 32,000 tokens, then $1.8/$9 for up to 128,000, $3/$15 for up to 256,000 and $6/$60 for the full million. Model architecture and capabilities According to the documentation released by Qwen Team online, Qwen3-Coder is a Mixture-of-Experts (MoE) model with 480 billion total parameters, 35 billion active per query, and 8 active experts out of 160. It supports 256K token context lengths natively, with extrapolation up to 1 million tokens using YaRN (Yet another RoPE extrapolatioN -- a technique used to extend a language model's context length beyond its original training limit by modifying the Rotary Positional Embeddings (RoPE) used during attention computation. This capacity enables the model to understand and manipulate entire repositories or lengthy documents in a single pass. Designed as a causal language model, it features 62 layers, 96 attention heads for queries, and 8 for key-value pairs. It is optimized for token-efficient, instruction-following tasks and omits support for <think> blocks by default, streamlining its outputs. High performance Qwen3-Coder has achieved leading performance among open models on several agentic evaluation suites: * SWE-bench Verified: 67.0% (standard), 69.6% (500-turn) * GPT-4.1: 54.6% * Gemini 2.5 Pro Preview: 49.0% * Claude Sonnet-4: 70.4% The model also scores competitively across tasks such as agentic browser use, multi-language programming, and tool use. Visual benchmarks show progressive improvement across training iterations in categories like code generation, SQL programming, code editing, and instruction following. Tooling and integration options Alongside the model, Qwen has open-sourced Qwen Code, a CLI tool forked from Gemini Code. This interface supports function calling and structured prompting, making it easier to integrate Qwen3-Coder into coding workflows. Qwen Code supports Node.js environments and can be installed via npm or from source. Qwen3-Coder also integrates with developer platforms such as: * Claude Code (via DashScope proxy or router customization) * Cline (as an OpenAI-compatible backend) * Ollama, LMStudio, MLX-LM, llama.cpp, and KTransformers Developers can run Qwen3-Coder locally or connect via OpenAI-compatible APIs using endpoints hosted on Alibaba Cloud. Post-training techniques: code RL and long-horizon planning In addition to pretraining on 7.5 trillion tokens (70% code), Qwen3-Coder benefits from advanced post-training techniques: * Code RL (Reinforcement Learning): Emphasizes high-quality, execution-driven learning on diverse, verifiable code tasks * Long-Horizon Agent RL: Trains the model to plan, use tools, and adapt over multi-turn interactions This phase simulates real-world software engineering challenges. To enable it, Qwen built a 20,000-environment system on Alibaba Cloud, offering the scale necessary for evaluating and training models on complex workflows like those found in SWE-bench. Enterprise implications: AI for engineering and DevOps workflows For enterprises, Qwen3-Coder offers an open, highly capable alternative to closed-source proprietary models. With strong results in coding execution and long-context reasoning, it is especially relevant for: * Codebase-level understanding: Ideal for AI systems that must comprehend large repositories, technical documentation, or architectural patterns * Automated pull request workflows: Its ability to plan and adapt across turns makes it suitable for auto-generating or reviewing pull requests * Tool integration and orchestration: Through its native tool-calling APIs and function interface, the model can be embedded in internal tooling and CI/CD systems. This makes it especially viable for agentic workflows and products, i.e., those where the user triggers one or multiple tasks that it wants the AI model to go off and do autonomously, on its own, checking in only when finished or when questions arise. * Data residency and cost control: As an open model, enterprises can deploy Qwen3-Coder on their own infrastructure -- whether cloud-native or on-prem -- avoiding vendor lock-in and managing compute usage more directly Support for long contexts and modular deployment options across various dev environments makes Qwen3-Coder a candidate for production-grade AI pipelines in both large tech companies and smaller engineering teams. Developer access and best practices To use Qwen3-Coder optimally, Qwen recommends: * Sampling settings: temperature=0.7, top_p=0.8, top_k=20, repetition_penalty=1.05 * Output length: Up to 65,536 tokens * Transformers version: 4.51.0 or later (older versions may throw errors due to qwen3_moe incompatibility) APIs and SDK examples are provided using OpenAI-compatible Python clients. Developers can define custom tools and let Qwen3-Coder dynamically invoke them during conversation or code generation tasks. Warm early reception from AI power users Initial responses to Qwen3-Coder-480B-A35B-Instruct have been notably positive among AI researchers, engineers, and developers who have tested the model in real-world coding workflows. In addition to Raschka's lofty praise above, Wolfram Ravenwolf, an AI engineer and evaluator at EllamindAI, shared his experience integrating the model with Claude Code on X, stating, "This is surely the best one currently." After testing several integration proxies, Ravenwolf said he ultimately built his own using LiteLLM to ensure optimal performance, demonstrating the model's appeal to hands-on practitioners focused on toolchain customization. Educator and AI tinkerer Kevin Nelson also weighed in on X after using the model for simulation tasks. "Qwen 3 Coder is on another level," he posted, noting that the model not only executed on provided scaffolds but even embedded a message within the output of the simulation -- an unexpected but welcome sign of the model's awareness of task context. Even Twitter co-founder and Square (now called "Block") founder Jack Dorsey posted an X message in praise of the model, writing: "Goose + qwen3-coder = wow," in reference to his Block's open source AI agent framework Goose, which VentureBeat covered back in January 2025. These responses suggest Qwen3-Coder is resonating with a technically savvy user base seeking performance, adaptability, and deeper integration with existing development stacks. Looking ahead: more sizes, more use cases While this release focuses on the most powerful variant, Qwen3-Coder-480B-A35B-Instruct, the Qwen team indicates that additional model sizes are in development. These will aim to offer similar capabilities with lower deployment costs, broadening accessibility. Future work also includes exploring self-improvement, as the team investigates whether agentic models can iteratively refine their own performance through real-world use.

Qwen 3 : The Open Source AI That Outperforms the Competition Beats GPT-4.1

What if the future of open source AI wasn't just about keeping up but about redefining the game entirely? Enter Qwen 3 2507, Alibaba's latest innovation that doesn't just challenge industry heavyweights like GPT-4.1, Opus 4, and Kimi K2 -- it decisively outperforms them. With a bold dual-architecture design and an astonishing ability to process up to 256,000 tokens in a single context, Qwen 3 isn't merely another AI model; it's a statement. Imagine an AI that can seamlessly switch between crafting precise customer service responses and solving intricate logic puzzles, all while maintaining unparalleled efficiency. That's the promise of Qwen 3, and it's already turning heads in the developer and research communities. In this thorough assessment by World of AI, we'll explore how Qwen 3's new features -- like its dual-architecture system and multilingual mastery -- are reshaping the open source landscape. You'll discover why its unmatched performance in coding, agentic tasks, and long-context understanding has set a new standard for AI excellence. But is it truly the ultimate tool for professionals and innovators, or does it come with limitations? By the end, you'll have a clear perspective on whether Qwen 3 is the future-proof solution you've been waiting for -- or if the competition still has a fighting chance. Central to Qwen 3's success is its dual-architecture system, which divides the model into two specialized components: This separation of functions allows Qwen 3 to deliver precise, task-specific outputs. With a total of 235 billion parameters and 22 billion active parameters, the model achieves remarkable efficiency and accuracy. For example, the "Instruct" model is particularly effective in creating responsive customer service chatbots, while the "Thinking" model excels in solving advanced logic puzzles or conducting detailed financial analyses. This dual-architecture approach ensures that users can rely on Qwen 3 for both straightforward and complex tasks. Qwen 3 consistently outperforms its competitors in critical areas such as coding, mathematics, and agentic tasks. Its ability to process up to 256,000 tokens in a single context sets it apart, making it highly effective for handling extensive documents, legal texts, and academic research papers. Key performance highlights include: These capabilities position Qwen 3 as a versatile and reliable tool for professionals seeking advanced AI solutions. Whether you are a developer building applications or a researcher analyzing complex datasets, Qwen 3 delivers consistent and high-quality results. Advance your skills in open source language models by reading more of our detailed content. Qwen 3's multilingual proficiency is another standout feature, supporting a wide array of languages. This capability makes it an ideal choice for global applications, allowing seamless communication and content generation across linguistic and cultural boundaries. Businesses operating in international markets can use this feature to enhance their operations and reach diverse audiences effectively. In addition to its language capabilities, Qwen 3 integrates seamlessly with external tools, further enhancing its utility. The model can: This combination of multilingual support and tool integration makes Qwen 3 a valuable asset for organizations aiming to optimize their workflows and expand their reach. One of Qwen 3's most appealing features is its accessibility. The model is available for free via OpenRouter, allowing users to explore its capabilities without financial barriers. For those who prefer local deployment, platforms like Olama and LM Studio offer straightforward installation options, making sure that users can choose the setup that best suits their needs. Additionally, Qwen 3 supports API integration, allowing seamless incorporation into existing systems. This flexibility allows businesses and developers to enhance their operations with advanced AI capabilities without significant infrastructure changes. Whether you are a small startup or a large enterprise, Qwen 3 provides a scalable and cost-effective solution. Qwen 3 2507 represents a significant advancement in open source AI development. Its dual-architecture design not only enhances performance but also sets a new benchmark for future models. Alibaba's ongoing commitment to refining this series suggests that users can expect continuous updates and improvements, particularly in specialized areas such as reasoning, instruction-following, and multilingual capabilities. For users, this means access to a innovative tool that evolves alongside their needs. Whether you are solving complex problems, developing innovative applications, or exploring creative projects, Qwen 3 offers a reliable and forward-thinking solution. Its versatility and adaptability ensure that it remains a valuable resource for professionals and organizations navigating the rapidly evolving AI landscape.