Google Gemini Guide: Features, Tips & Comparisons

Claude's Max plan includes $100 in API credits a month, Ultra $200 and Team up to $500 in total, while Free and Pro get none. Gemini includes Google Cloud credits of $10 a month with Google AI Pro, $40 with Ultra 5x and $100 with Ultra 20x, and none with Plus. Since October 28, 2026, ChatGPT has included $100, $200 and $500 a month with Pro 100, 200 and 500, and per-seat credits with Business (up to $500 in total), while Go and Plus get none. Based on the three companies' official pricing pages, help articles and developer docs (checked October 27, 2026; ChatGPT checked October 30), this article compares, in the same order, where the credits can and can't be used (your Claude Code usage limits don't change), how to claim them (Gemini requires a $5 prepayment), when they expire (Claude and ChatGPT credits expire monthly, while Gemini credits are valid for 12 months and accumulate), what you can use for free without a plan (Claude's trial credit, Gemini's free tier and OpenAI's free tokens for data sharing), and whether the content you send is used for training, then sums up the deciding factors for choosing.

Google is replacing Gemini's Gems with skills. Gems end in November 2026 for personal Google accounts, March 2027 for work accounts, and June 2027 for school accounts, and remaining Gems are automatically rebuilt as skills, but Google Labs Gems, Opal, and GitHub files do not move. Based on Google's help pages and announcements (verified October 21, 2026), this article explains the differences between Gems and skills, three steps to move them yourself in advance, and how to create skills and use them with "/" (free to use, with up to 100 active at once). It also covers how to write names and descriptions that Gemini can use, what skills still cannot do, such as working with Canvas and Deep Research, and what to do before November.

Google's AI Gemini is free to use. Costs come from two separate paid options: app subscriptions (Google AI Plus, Pro, and Ultra), which raise usage limits and unlock higher-tier models, and the API, which is billed by usage. Based on Google's official pages (checked October 20, 2026), this guide covers plan prices and limits (reset every 5 hours, weekly caps), what is free and what isn't, Windows and Mac apps, API pricing and free tier (free-tier content is used to improve products), Antigravity for coding and the end of Gemini CLI for individuals, benchmarks separating Google's claims from third-party figures (Artificial Analysis), strengths and weaknesses, and which option to choose.

Ask an AI "What kind of person am I?" and all you get back is a plausible-sounding expansion of whatever you typed in. What works is the reverse: let the AI ask the questions to draw out real events, have it help sort out the common threads and put them into words, and make the final call yourself. This article gives you 8 prompts to use in order: an interview, strengths, values, weaknesses, the conditions of work that suits you, a personal pitch draft, mock interview questions, and a check from the opposing side. It also covers why you shouldn't take the answers at face value, including the tendency to feel that words which fit anyone are "spot on" (Forer's 1949 experiment) and AI's tendency to agree with you; what information is fine to enter and what to avoid; and how to use memory and projects in ChatGPT, Claude and Gemini, all checked against each company's official help pages.

Right after you send a message, Gemini stops with "Something went wrong(13)", and Google does not explain the cause of this error in its official help. However, Google's official incident report from May 2026 records this message appearing for about four days, with the root cause given as insufficient database resources plus application bugs, and the workaround as "none". Building on that official record, and on the fact that the number "13" matches gRPC's internal error code (INTERNAL), though Google has not said they mean the same thing, this article sorts out the four situations that come up most in the official community: it hits everyone at once, only a long chat, only when an image is attached, and only one particular account. It then lays out seven isolation steps to try in order of least effort, from checking the status page to handing off to a new chat, testing a different account and reporting it through feedback.

You don't have to ask for "lead with the conclusion" or "keep it in plain English" every time: write it once in the instruction field that automatically applies to every conversation. That field is "Custom Instructions" in ChatGPT, "Instructions for Claude" in Claude and "Instructions for Gemini" in Gemini, but the three differ in what it is called, where it lives and how much you can write. ChatGPT allows 1,500 characters on Free and Go and 5,000 on Plus and higher plans (raised in July 2026), while Claude and Gemini don't publish a limit. This article checks the location and limit of each against the companies' official help, then uses Anthropic's official prompting guide to show how to write instructions that work, with an example. It also turns the situations the official help pages say instructions don't take effect, such as inside projects, inside Gems, in temporary chats and on company accounts, into a checklist.

An in-depth comparison of GPT-5.6 Sol, OpenAI's flagship as of July 2026, and Google Gemini. Unlike the earlier battles against Claude, their strengths barely overlap: Sol dominates agentic and terminal coding (Terminal-Bench 2.1 88.8% vs 70.7%, SWE-bench Pro 64.6% vs 54.2%, both from OpenAI's evaluation table), while Gemini counters with native multimodality (voice and video) and a unit price about 40% of Sol's ($2/$12 up to 200K tokens vs $5/$30). There is also an important "timing trap": Google's true challenger, Gemini 3.5 Pro, was not yet released as of this writing (GA had been planned for mid-July 2026, and it was still unavailable on September 22), so the comparison target is Gemini 3.1 Pro (February 2026, still a preview in the API). This article lays out a spec cheat sheet, coding/reasoning/multimodal benchmarks, the multimodal gap that is Gemini's home turf, real cost (Gemini about 40% of Sol; on the GPT side Terra matches Gemini), a strengths-and-weaknesses map, and use-case-based selection, grounded in official announcements and independent benchmarks. For the successor, GPT-6 Astra (September 2026), see the note at the top.

Ask the AI a question, get an answer grounded in fresh Google Search — and it is continuous with Gmail, Docs, and YouTube. That is the world of Google Gemini. Gemini is a conversational AI built by Google (and the family of models behind it), broadly embedded across mobile apps, the web, Google Workspace, and Android, and multimodal across text, images, audio, and video. Models split into "the fast and cheap Flash family" and "the smart Pro family" — the specific versions change with each generation. Pricing runs Free / Plus $4.99 / Pro $19.99 / Ultra $99.99–$199.99 (US, September 2026; Ultra was once $249.99), and 2026 moved to compute-based usage limits. This article covers the model lineup, key features (Deep Research, Gems, Canvas, Live, Deep Think), three strengths (Google integration, long context, multimodal), pricing, and the difference from ChatGPT and Claude — all as of September 2026.

In 2026, top models reached the 80s on the MMMU-Pro multimodal benchmark (GPT-5.5 81.2% without tools, Gemini 3.1 Pro 80.5%, as reported by each vendor) — image understanding is nearing saturation. Architecture has been shifting from stitched (separate models chained together) to native (one model taking several inputs directly) — though only some models, such as Gemini and Qwen's Omni line, take all four of text, image, audio and video; OpenAI's flagship model and the Claude API stop at images. This article covers what multimodal AI is (LMM/VLM/Omnimodal), the architectural divide and why it matters, what technically determines strength in each modality (video, audio, documents/UI, open models) plus a May 2026 comparison snapshot, four benchmarks to watch (MMMU-Pro, Video-MMMU, DocVQA, AudioBench), five use cases and what to judge each on, and the three hard limits (low-quality image guesses, mid-video accuracy, dialect/jargon audio) — grounded in current research and practical use.

Ever asked a generative AI a question only to hear "that's beyond my knowledge"? Every generative AI has a training data deadline called a knowledge cutoff. Here's a side-by-side comparison of cutoff dates and web search support across 8 major tools.