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Gemini 3

Ai Tool

Gemini 3 is an advanced AI model developed by DeepMind.

article 11 storys calendar_today First seen: 2026-01-02 update Last seen: 2026-03-03 open_in_new Website menu_book Wikipedia

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Google’s Gemini 3.1 Flash-Lite targets high-volume, low-latency workloads

Google released Gemini 3.1 Flash-Lite, a faster, cheaper model aimed at high-volume developer workloads and signaling a broader shift to lighter LLMs for routine backend and data tasks. Google’s launch of [Gemini 3.1 Flash-Lite](https://thenewstack.io/google-gemini-3-1-flash-lite/) emphasizes low-latency responses for tasks where cost is critical, with preview access via the Gemini API in Google AI Studio and enterprise access in Vertex AI, alongside industry moves like OpenAI’s GPT-5.3 Instant toward lighter models ([context and availability](https://www.thedeepview.com/articles/openai-google-target-lighter-models)). Independent coverage pegs Flash-Lite at $0.25/million input tokens and $1.5/million output tokens—about one-eighth the price of Gemini 3.1 Pro—and notes support for four “thinking” levels to trade speed for reasoning when needed ([pricing and modes](https://simonwillison.net/2026/Mar/3/gemini-31-flash-lite/#atom-everything)). For backend/data teams, this sweet spot makes Flash-Lite a strong default for translation, content moderation, summarization, and structured generation (dashboards/simulations), reserving heavier models for only the hardest requests ([use cases](https://www.thedeepview.com/articles/openai-google-target-lighter-models)). If your pipelines push files, mind Gemini’s surface-specific limits across Apps (including NotebookLM notebooks), API, and enterprise tools—think up to 10 files per prompt, 100MB per file/ZIP with caveats, strict video caps, and code folder/GitHub repo constraints—so ingestion doesn’t silently truncate or fail ([file-handling constraints](https://www.datastudios.org/post/gemini-file-upload-support-explained-supported-formats-size-constraints-and-document-handling-acr)). Zooming out, the race to lighter models (OpenAI’s GPT-5.3 Instant and Alibaba’s Qwen Small Model Series) underscores a clear pattern: push routine throughput to cheaper, faster tiers and escalate to heavyweight reasoning only on ambiguity or failure ([trend snapshot](https://www.thedeepview.com/articles/openai-google-target-lighter-models)).

calendar_today 2026-03-03
google gemini-31-flash-lite gemini-api google-ai-studio vertex-ai

Windsurf ships new models, Linux ARM64, and enterprise hooks

Windsurf rolled out new frontier coding models, full Linux ARM64 support, and enterprise-grade Cascade Hooks while community feedback spotlights its transparent crediting versus rivals' opaque limits. Windsurf’s latest updates add Gemini 3.1 Pro, Claude Sonnet 4.6, GLM-5, Minimax M2.5, and GPT-5.3-Codex-Spark with time-limited credit multipliers, plus quality-of-life fixes and features like automatic Plan→Code switching, skills loading from .agents/skills, tracked rules in post_cascade_response, and diff zones auto-closing on commit; importantly, it now provides full Linux ARM64 deb/rpm packages and enterprise cloud config for Cascade Hooks with Devin service key auth, as detailed in the [Windsurf changelog](https://windsurf.com/changelog). A power user’s comparison underscores cost control and predictability: they favored Windsurf’s clear credit model over Cursor/Claude Code’s rate-limit surprises, keeping GitHub Copilot Pro+ for predictable premium requests while continuing to code primarily in Windsurf, per this [Reddit write-up](https://www.reddit.com/r/windsurf/comments/1r9b58e/i_almost_left_windsurf/).

calendar_today 2026-02-20
windsurf gemini-31-pro claude-sonnet-46 glm-5 minimax-m25

Open-weight "AI engineer" models arrive: Qwen 3.5, GLM-5, MiniMax M2.5

A new wave of open-weight frontier models now rivals closed systems on coding and long-horizon agent tasks, making self-hosted AI engineer workflows practical for backend and data teams. Alibaba’s Qwen 3.5 ships as an open‑weights Mixture‑of‑Experts model (397B total, 17B active) with multimodal input and a 256K context, alongside a hosted Qwen3.5‑Plus variant offering 1M context and built‑in tools; details and early impressions are summarized by Simon Willison’s write‑up of the [Qwen 3.5 release](https://simonwillison.net/2026/Feb/17/qwen35/#atom-everything) and the official [Qwen blog](https://qwen.ai/blog?id=qwen3.5). Z.ai’s GLM‑5 launched open source with top open-model scores on SWE‑bench‑Verified (77.8) and Terminal Bench 2.0 (56.2), plus long‑context and RL‑driven agent training advances, with the announcement and code at [BusinessWire](https://www.businesswire.com/news/home/20260215030665/en/GLM-5-Launch-Signals-a-New-Era-in-AI-When-Models-Become-Engineers) and the [GitHub repo](https://github.com/zai-org/GLM-5). MiniMax M2.5 claims state‑of‑the‑art coding/agent performance (e.g., 80.2% SWE‑Bench Verified) and aggressive cost/speed on its [Hugging Face card](https://huggingface.co/unsloth/MiniMax-M2.5), while hands‑on videos compare real coding runs for GLM‑5 and M2.5; you can also quickly trial free models via [OpenRouter’s free router](https://openrouter.ai/openrouter/free).

calendar_today 2026-02-17
qwen35-397b-a17b qwen35-plus qwen-chat alibaba-cloud glm-5

Ship an AI RFP-scoring pipeline with n8n + Gemini, and mind the file limits (vs ChatGPT)

You can automate RFP scoring and spreadsheet analysis with Gemini today using n8n, while planning around concrete file-format and size limits across Gemini and ChatGPT. An end-to-end n8n workflow shows how to accept vendor PDFs via a form webhook, fetch the RFP from Drive, extract text, merge both streams, call the Gemini API with a structured prompt to return JSON scores, and append results to Sheets—plus Drive auth scopes and download details like alt=media are covered in this guide ([n8n + Gemini RFP evaluation](https://dev.to/hackceleration/building-ai-powered-rfp-evaluation-with-n8n-and-google-gemini-pf5)). For data handling at scale, Gemini supports XLS/XLSX/CSV/TSV and Google Sheets; Gemini chat allows up to 10 files per prompt at 100 MB each, while the Files API permits up to 2 GB per file and 20 GB per project for 48 hours—useful for batch or programmatic flows ([Gemini spreadsheet upload and limits](https://www.datastudios.org/post/google-gemini-spreadsheet-uploading-excel-and-csv-support-data-analysis-capabilities-formula-hand)). If you compare providers, ChatGPT accepts many document and data types but caps file size at 512 MB (with spreadsheet practical limits around ~50 MB) and also enforces token and image-specific ceilings, which can influence provider selection for large artifacts ([ChatGPT file upload limits](https://www.datastudios.org/post/chatgpt-file-uploading-capabilities-supported-file-types-upload-size-limits-rules-and-document-r)).

calendar_today 2026-02-17
google-gemini n8n google-drive google-sheets google-files-api

Agentic coding meets reality: benchmarks expose gaps, runtime tracing narrows them

New evidence shows LLMs still struggle with production-grade observability and cross-cutting tasks, but agentic workflows augmented with runtime facts significantly improve reliability and speed. An independent SRE benchmark, [OTelBench](https://www.freep.com/press-release/story/145971/quesma-releases-otelbench-independent-benchmark-reveals-frontier-llms-struggle-with-real-world-sre-tasks/), finds frontier models pass only 29% of OpenTelemetry instrumentation tasks across 11 languages, with context propagation as a key failure mode despite much higher scores on coding-only tests. In contrast, Syncause boosted SWE-bench Verified fixes to 83.4% by adding dynamic tracing “Runtime Facts” to the Live-SWE-agent with Gemini 3 Pro, detailing methods and open-sourcing trajectories and code in their [blog](https://syn-cause.com/blog/swe-bench-verified-83) and [repo](https://github.com/Syncause/syncause-swebench). Complementing this, new research on cross-domain workflow generation proposes a decompose–recompose–decide method that surpasses 20-iteration refinement baselines in a single pass, reducing latency and cost for agentic orchestration ([paper](https://arxiv.org/html/2602.11114v1)). For hands-on adoption, the open-source [DeepCode](https://github.com/HKUDS/DeepCode) project provides multi-agent “Text2Backend” capabilities to prototype structured, telemetry-aware coding agents.

calendar_today 2026-02-12
quesma otelbench opentelemetry google-gemini-3-pro syncause

Gemini Deep Think: research gains, CLI workflows, and model-extraction risks

Google’s Gemini Deep Think is graduating from contests to real research and developer workflows, but its growing capability is also attracting copycat extraction and criminal abuse that teams must plan around. Google DeepMind details how Gemini Deep Think, guided by experts, is tackling professional math and science problems using an agent (Aletheia) that iteratively generates, verifies, revises, and even browses to avoid spurious citations, with results improving as inference-time compute scales and outperforming prior Olympiad-level benchmarks ([Google DeepMind](https://deepmind.google/blog/accelerating-mathematical-and-scientific-discovery-with-gemini-deep-think/?_bhlid=c06248275cf06add0c919aabac361f98ed7c1e95)). A broader industry pulse notes the release’s framing and early user anecdotes around “Gemini 3 Deep Think” appearing in the wild ([Simon Willison’s Weblog](https://simonwillison.net/2026/Feb/12/gemini-3-deep-think/#atom-everything)). For context on user expectations, this differs from Google Search’s ranking-first paradigm—Gemini aims for single-response reasoning rather than surfacing diverse sources ([DataStudios](https://www.datastudios.org/post/why-does-gemini-give-different-answers-than-google-search-reasoning-versus-ranking-logic)). For day-to-day engineering, a terminal-native Gemini CLI is emerging to integrate AI directly into developer workflows—writing files, chaining commands, and automating tasks without browser context switching, which can accelerate prototyping, code generation, and research summarization in-place ([Gemini CLI guide](https://atalupadhyay.wordpress.com/2026/02/12/gemini-cli-from-first-steps-to-advanced-workflows/)). Security posture must catch up: Google reports adversaries tried to clone Gemini via high-volume prompting (>100,000 prompts in one session) to distill its behavior, and separate threat intel highlights rising criminal use of Gemini for phishing, malware assistance, and reconnaissance—underscoring the need for rate limits, monitoring, and policy controls around model access and outputs ([Ars Technica](https://arstechnica.com/ai/2026/02/attackers-prompted-gemini-over-100000-times-while-trying-to-clone-it-google-says/), [WebProNews](https://www.webpronews.com/from-experimentation-to-exploitation-how-cybercriminals-are-weaponizing-googles-own-ai-tools-against-the-digital-world/)).

calendar_today 2026-02-12
google-deepmind google gemini-deep-think gemini-cli google-search

Gemini 3.0 Pro GA early tests look strong—treat as directional

An early YouTube test claims Gemini 3.0 Pro GA shows significant gains, but findings are unofficial and should be validated on your workloads. An independent reviewer shares preliminary benchmarks and demos: [Gemini 3.0 Pro GA WILL BE Google's Greatest Model Ever! (Early Test)](https://www.youtube.com/watch?v=tPTMHT4O4HQ&pp=ygUXbmV3IEFJIG1vZGVsIGZvciBjb2Rpbmc%3D)[^1]. Treat these claims as directional until official enterprise docs and pricing/performance data are available. [^1]: Adds: early, unofficial tests and benchmark impressions of Gemini 3.0 Pro GA.

calendar_today 2026-02-09
google gemini-30-pro youtube llm code-generation

Early tests hint Gemini 3.0 Pro GA gains for coding workloads

An early test video claims Google's Gemini 3.0 Pro GA shows strong gains on coding and reasoning, warranting evaluation against current LLMs for backend and data tasks. One early-test breakdown reports top-line improvements with benchmark snippets and demos in this video [Early Test: Gemini 3.0 Pro GA](https://www.youtube.com/watch?v=tPTMHT4O4HQ&pp=ygUXbmV3IEFJIG1vZGVsIGZvciBjb2Rpbmc%3D)[^1]. [^1]: Early, third-party video with anecdotal benchmarks and demos; unofficial and subject to change.

calendar_today 2026-02-09
google gemini-30-pro gemini llm code-generation

Baytech review: Google Antigravity agentic IDE—greenfield boost, Microsoft friction

Baytech Consulting reports that Google released an AI-native IDE, Antigravity, in late 2025 that uses the Gemini 3 model to orchestrate agentic, multi-file development (a "mission controller" vs autocomplete). Their analysis says it accelerates prototyping and greenfield work but introduces a walled-garden feel and integration friction for teams anchored in VS Code and Azure DevOps. This is a single consultancy review; official details from Google are limited.

calendar_today 2026-01-02
google-antigravity gemini-3 agentic-development azure-devops visual-studio-code