Google Gemini Guide: Features, Tips & Comparisons

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 OpenAI's flagship GPT-5.6 Sol 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 68.5%, SWE-bench Pro 64.6% estimated vs 54.2%), while Gemini counters with native multimodality (voice and video), roughly half the price ($2.50/$15 vs $5/$30), and a lead on MMLU 92.6%, ARC-AGI-2 77.1%, and WebDev Arena. There is also an important "timing trap": Google's true challenger, Gemini 3.5 Pro, is not yet released as of this writing (GA planned for mid-July 2026 after a full architecture overhaul), so the fair comparison target today is the current flagship Gemini 3.1 Pro (February 2026). 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 2x cheaper than Sol, but Terra now undercuts Gemini), a strengths-and-weaknesses map, and use-case-based selection, grounded in official announcements and independent benchmarks.

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" — latest are Gemini 3.8 Flash and 3.1 Pro. Pricing runs Free / Plus $7.99 / Pro $19.99 / Ultra $99.99 (Ultra cut from $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 with May 2026 info.

In April 2026, the MMMU-Pro multimodal benchmark hit 81–83% across GPT-5.5, Claude Opus 4.7, Gemini 3.1 Pro, and Qwen 3.5 Omni — image understanding has effectively saturated. Architecture has migrated from stitched (separate encoders + adapter) to native omnimodal (all modalities as a shared token stream). 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.