AI model

AppWizard
May 21, 2026
Google has updated its "Android Bench" rankings, introducing new AI models for Android app development, including open-weight models. The latest rankings, as of May 18, 2026, show GPT 5.5 at the top, surpassing GPT 5.4 and Gemini 3.1 Pro by nearly 2%. The update provides metrics such as average latency, total tokens used, and average cost per benchmark run. GPT 5.5 has a score of 74, with an average latency of 15.5, total tokens of 64.5, and an average cost of .9. In comparison, GPT 5.4 has a score of 72.4, with an average latency of 21.2, total tokens of 64.2, and an average cost of [openai_gpt model="gpt-4o-mini" prompt="Summarize the content and extract only the fact described in the text bellow. The summary shall NOT include a title, introduction and conclusion. Text: Google has refreshed its “Android Bench” rankings, unveiling a new lineup of AI models tailored for Android app development. This update introduces several “open-weight” models and provides deeper insights into the performance metrics, including token usage and associated costs. Large language models have increasingly demonstrated their prowess in coding, significantly enhancing the app development process. This trend has given rise to what is now known as “vibe coding.” Earlier this year, Google released a benchmark ranking that evaluated the top AI models for Android development, focusing on common tasks and adherence to best practices. Initially, the rankings were led by Gemini 3.1 Pro, with OpenAI’s GPT 5.4 later sharing the spotlight. However, as of the latest update on May 18, 2026, a new contender has emerged. GPT 5.5 has claimed the top position, surpassing GPT 5.4 and Gemini 3.1 Pro by nearly 2%. This update also enhances clarity by presenting average latency, total tokens utilized, and the average cost associated with each AI model. Google has provided documentation detailing the methodology behind these metrics. Average Latency: Time taken to complete 100 tasks across 10 runs Average Total Tokens: Token consumption for a complete benchmark run across 10 iterations Average Cost: Cost per benchmark run in US dollars at the time of testing While GPT 5.5 boasts superior performance, it comes at a cost—over twice that of Gemini 3.1 Pro for equivalent functions. Here’s a look at the top ten models based on Google’s latest data as of May 21, 2026: Model Score Avg Latency Avg Total Tokens Avg Cost New: GPT 5.5 74 15.5 64.5 3.9 GPT 5.4 72.4 21.2 64.2 .7 Gemini 3.1 Pro Preview 72.4 11.5 75.4 .0 New: Claude Opus 4.7 68.7 11.6 90.0 4.3 GPT 5.3 Codex 67.7 11.2 71.4 .6 Claude Opus 4.6 66.6 9.9 69.5 .4 GPT 5.2 Codex 62.5 24.3 124.4 1.9 Claude Opus 4.5 61.9 12.5 79.8 2.5 Gemini 3 Pro Preview 60.4 9.8 117.0 .7 New: GLM 5.1 59.7 33.4 80.2 .7 The rankings now feature a wider array of open-weight models, including Gemma, Qwen, DeepSeek, and MiMo, among others. GLM 5.1 has emerged as the highest scorer among these newcomers, closely followed by Kimi K2.6. Google is committed to updating the “Android Bench” on a monthly basis. With the anticipated release of Gemini 3.5 Pro and the already available 3.5 Flash, the competitive landscape will be intriguing to watch as Google seeks to reclaim its lead against OpenAI's advancements. More on Android: Follow Ben: Twitter/X, Threads, Bluesky, and Instagram FTC: We use income earning auto affiliate links. More." max_tokens="3500" temperature="0.3" top_p="1.0" best_of="1" presence_penalty="0.1" frequency_penalty="frequency_penalty"].7. Gemini 3.1 Pro has the same score as GPT 5.4 but with different latency and token metrics. The rankings also include other models like Claude Opus 4.7, GPT 5.3 Codex, and GLM 5.1, which has emerged as the highest scorer among newcomers. Google plans to update the rankings monthly.
AppWizard
May 20, 2026
Google has rolled out its AI model, Gemini 3.5 Flash, across various platforms, claiming it outperforms its predecessor, Gemini 3.1 Pro, in key benchmarks. Gemini 3.5 Flash generates responses four times faster than competing AI systems and is designed for complex workflows and coding tasks. Google plans to introduce Gemini 3.1 Pro next month, which excels in decision-making and coding tests. The model is particularly effective for "long-horizon" tasks, aiding app development and document preparation. Google Antigravity, an agentic development platform, integrates with Gemini 3.5 Flash to manage large workloads. The company also introduced Gemini Spark, a personal AI agent for managing digital tasks, with a beta rollout for select testers. Gemini 3.5 was developed under the Frontier Safety Framework, incorporating enhanced safety measures and interpretability tools.
Winsage
May 14, 2026
Microsoft has introduced MDASH (Multi-Model Agentic Scanning Harness), a security solution that uses over 100 specialized AI agents to identify software vulnerabilities. On May 12, 2026, MDASH identified 16 new vulnerabilities (CVEs) in the Windows networking and authentication stack, four of which were critical, including remote code execution vulnerabilities in tcpip.sys, ikeext.dll, netlogon.dll, and dnsapi.dll. Ten of these vulnerabilities can be accessed over the network without authentication. MDASH operates through a four-stage pipeline: analyzing source code, scrutinizing for suspicious elements, debating the exploitability of issues, and attempting to exploit vulnerabilities. The system is model-agnostic and allows integration of new models and domain-specific knowledge. MDASH scored 88.45 percent on the CyberGym benchmark, ranking first among competitors, although the comparison may not be entirely fair as it contrasts a comprehensive framework with individual models. The models used to achieve this score are not specified. MDASH is supported by Microsoft's Autonomous Code Security Team and is currently in a limited private preview for select customers.
Winsage
May 11, 2026
Microsoft has rolled out four Windows Insider builds, introduced new hidden features, and revamped the Windows Run feature. Despite these advancements, many Windows 10 users are hesitant to upgrade due to financial constraints. Xbox Mode has received criticism for its performance on dual monitor setups. Approximately 25% of Windows users on Steam are still using Windows 10. Windows 11 will enhance CPU frequency during high-priority tasks, and recent Insider builds have improved touchpad gestures, File Explorer descriptions, and voice-typing interface. Users can now prevent Chrome and Edge from automatically downloading local AI models. Feedback on new features like the Low Latency Profile has been predominantly negative, with users expressing concerns over CPU spikes.
AppWizard
May 9, 2026
Instagram has discontinued its end-to-end encryption (e2ee) feature for direct messages, which previously allowed users to communicate securely without interception. All direct messages will now be protected by standard encryption, allowing potential access by service or network providers. Meta, Instagram's parent company, cited low usage rates for this change, which was communicated in March. Privacy advocates have raised concerns about user communications being shared with law enforcement and for AI training purposes, although Meta clarified it does not use private messages for AI purposes. Users seeking privacy can switch to WhatsApp or the standalone Messenger app, which still support e2ee. Meta has also advised users who had e2ee enabled to download their chat histories and media before the feature is fully retired.
AppWizard
May 6, 2026
Google has installed a 4GB AI model called Gemini Nano on users' computers without their explicit consent. This model enhances user experience with features like "Help me write," AI-assisted browsing, and scam detection, but was integrated without prior notification to users. The AI scam detection features have been available on Android and desktop platforms for some time. The deployment of the Nano model has raised concerns about user autonomy and trust, as it was done without user consultation or approval.
Tech Optimizer
May 5, 2026
Researchers have unveiled a new AI model that enhances machine learning capabilities by streamlining data processing and improving predictive accuracy. The model incorporates advanced techniques for efficient training, leveraging deep learning algorithms and optimized data structures to analyze large datasets quickly and precisely. It offers enhanced data processing, scalability for growing business needs, and cost efficiency by reducing computational load. The model can learn from diverse data sources, making it adaptable for various applications and contributing to smarter decision-making and operational efficiencies.
AppWizard
May 4, 2026
Google's AICore app enhances on-device AI capabilities for Android users, offering features like text summarization and proofreading. The app's significant storage usage is by design, as it temporarily retains both old and new versions of AI models during updates for reliability, which can lead to storage consumption of up to 11GB. This approach aims to prevent disruptions in functionality during updates. Once the new update is stable, the extra storage will be released automatically. Users are concerned about storage limitations, particularly on devices with 128GB base storage, and are advocating for a standardization of 256GB base storage for AI-enabled Android phones.
AppWizard
May 4, 2026
AICore can temporarily use large storage (up to 11GB) during updates on Android devices. Google retains both old and new AI models for up to three days as a fail-safe during these updates. The storage used is automatically freed once the new AI model is confirmed stable.
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