By skyforbes I spent a few days testing Gemini 3.0. It’s a noticeable step up from 2.5, but I'm not seeing better responses for most use cases than ChatGPT 5.1. I also realized how important ChatGPT’s long-term conversational memory has become. Using two LLMs splits that memory and switching fully would mean losing a huge amount of accumulated context. Given the mixed performance and the memory issue, I've come to the conclusion dropping ChatGPT now would be jumping the gun. Google isn't quite there yet.
I've especially found the cross-conversation persistent memory to being very valuable on ChatGPT although Gemini supposedly released a "Personal Context" on Aug 13 but seems some Pro users even in the USA still don't have it. Also Gemini is missing the ability to create projects/folders where you can group conversations inside of them. You have to basically rename the conversation to [project name] Conversation title to get any sort of conversation grouping feature which is odd a company as big as Google hasn't implemented this and it's been in ChatGPT for quite a while.
Another issue is Gemini appears to have manual routing: Fast (Gemini 2.5 Flash), or Thinking (Gemini 3.0 Pro). 2.5 Flash hallucinates too much to use it for anything but fairly basic prompts. So I have Thinking turned on all the time and it's slow but tolerable. Whereas GPT 5.1 is picking the model in the background based on the complexity and content of my prompt. I'm sure when Gemini 3.0 Flash comes out I'll use that as the default.
People constantly ask which model is “better,” but that’s extremely subjective. I watched every Gemini 3.0 vs ChatGPT 5.1 comparison video released over the last three days, and almost all were useless for my real-world use cases. Nearly every reviewer evaluates the models from one or more of four narrow perspectives:
- Benchmark scores
- Coding performance
- Business workflows (document/data analysis, meeting help, presentation generation)
- Multimedia generation (images, videos, etc.)
But reviewers mostly avoid broader, real-world comparisons for several reasons: (1) it's more time consuming, (2) they aren't experts in a wide variety of areas so hard to evaluate performance, (3) some of the use cases may be too personal, (3) the majority of the audience is tech savvy so they are biased towards coding tools, business applications, media generation.
As a result, almost none of the reviews address the broader, practical tasks that matter more to a lot of the general public. This list of everyday applications is just the tip of the iceberg. It would be far more useful to in addition to going over benchmarks, coding and multimedia capabilities they also chose 5–10 common real-world use cases and compare the models on those instead of recycling benchmark charts and coding challenges.
- Healthcare symptom triage – structured Q&A to narrow likely causes and suggest urgency level.
- Medication interaction checker – verify conflicts, timing rules, and common contraindications.
- Nutrition optimizer – meal planning based on macros, allergies, and dietary goals.
- Exercise form coach – describe correct biomechanics, cues, and progression strategies.
- Personal trainer periodization – build phased workout cycles and weekly templates.
- Sleep optimization advisor – evaluate routines, environment, and circadian alignment.
- Mental-health journaling guide – structured CBT-style prompting, reframing, and pattern detection.
- Detailed Travel Itinerary planning – build a detailed itinerary based on detailed user instructions and preferences including checking hotel and airline prices
- Habit-building accountability – daily check-ins, streak tracking, and micro-goal adjustment.
- Learning tutor – break down complex topics (math, languages, science) into spaced lessons.
- Language conversation partner – simulated dialogues, accent correction, and vocabulary drills.
- Reading assistant – summarize chapters, track characters, highlight themes.
- Recipe generator – produce meals from available ingredients with substitutions.
- Home maintenance advisor – diagnose minor appliance issues, filter schedules, seasonal tasks.
- Vehicle maintenance assistant – interpret dashboard alerts, service intervals, troubleshooting.
- Smart-home scenario builder – create routines, automate lighting/HVAC schedules.
- Navigation integration – interpret traffic, propose detours, summarize hazards hands-free.
- Budget coaching – categorize expenses, forecast cash flow, identify waste areas.
- Shopping comparison – evaluate specs, alternatives, warranties, long-term ownership costs.
- Price-drop monitoring logic – track patterns and recommend best purchase windows.
- Streaming recommender – match user tastes to hidden catalog titles across services.
- Hobby instructor – photography, gardening, woodworking; technique + material selection.
- Pet-care guidance – feeding schedules, breed-specific exercise needs, behavior tips.
- Home organization planner – declutter workflows, storage system design, weekly upkeep tasks.
- Event/holiday coordinator – itineraries, menus, checklists, and logistics.
- Sentiment + trend analyzer – interpret sports, fantasy, gambling signals using probabilistic framing.