Google Gemini AI Reviews

Google Gemini AI is my primary tool for office workflows at ADP, where I have evolved from using Gemini 1.5 Flash to Gemini 3 Pro. I started with personal prototyping during my 100-day journey of rapid prototyping of AI projects, beginning around day 40 or day 50, using Google Gemini AI API keys in applications I was building called YouTube Optimizer.

The YouTube Optimizer was designed to help creators find their niche, hashtags, and captions that align with their audience. I relied heavily on Google Gemini AI to narrow down the audience based on the preferences that creators selected. The research and answers regarding niche captions and hashtags were completely driven by Google Gemini AI, and we also provided content strategies for creators. This research was possible entirely through Google Gemini AI because every result is based on searches it makes in Google, providing real-time data that gave us an extra edge. This has been a beautiful application that we built using Google Gemini AI.

I have also used Google Gemini AI in many other projects and rely heavily on the Google Gemini AI API key for building applications. I have used it in multiple hackathons while building different tools called Guru Sahayam, which excels in handling multiple languages. The hackathon application was designed to teach children from different regions with different languages the same content, and Google Gemini AI handled it gracefully, resulting in a beautiful application that helped me win multiple hackathons.

In my workspace, I use Google Gemini AI 3 Pro on a daily basis for code assisting. Google Gemini AI Pro performs exceptionally well for all coding work, and in my personal prototyping, I use their API keys to build applications. Google Gemini AI handles everything gracefully because of its context awareness and real-time search capabilities, which give it an extra edge over other tools.

I use Google Gemini AI in many AI agents that I build, and I use N8N and make.com for creating new agents for clients in my freelancing web agency. I rely on Google Gemini AI free tier API keys to prototype and provide them with the MVP version. Google Gemini AI has beautiful integration with all workspace tools, including mail and Google Sheets. Code scaffolding for Python, fast APIs, and Google Workspace automation have been other major use cases for me.

Google Gemini AI 3 Pro provides PhD level reasoning, which makes it my best friend when I am searching for or researching something based on real-time search. The one million context for enterprise documents and logs is exceptionally useful for developers because we use it when building large projects. Having one million context helps us build, debug, and rapidly enhance features.

The agentic workflows with Jira and Slack integrations have been tremendous. I appreciate how Google Gemini AI handles multiple languages, is quick for prototypes, and excels in complex planning and supply chain analysis. The workspace native integration is excellent, the context awareness is strong, and the level of reasoning it provides is impressive.

Google Gemini AI provides free tier API keys that allow me to prototype ideas easily by wrapping an application on top of a Gemini API key, which is available to everybody. The free tier API key also has complete knowledge of different languages. When building applications with multiple language integration, it is easy to rapidly prototype and build production-grade applications using Google Gemini AI API keys rather than relying on many developers and trainers in different languages.

There is a steeper learning curve for advanced agentic features that could be improved, and hallucinations should be reduced. The answers provided are long, which is impressive but not efficient for users needing rapid, crisp responses. Providing concise answers would improve the user experience.

Google Gemini AI's UI code is too vague and the designs are not very appealing. Google Gemini AI can improve its UI code and address hallucination issues. The long answers provided can be tiresome to read, and the pricing is too high for individuals like me. These considerations led me to give a rating one point less than ten.

Native GitHub or Vercel export could be integrated, and the context could be increased to over two million tokens. A simplified agentic setup for the UI could also help non-technical experts handle it more effectively.

I have been using Google Gemini AI for about one and a half years, and it has been a great journey.

At times, I see Google Gemini AI hallucinate, and I feel that Gemini 3 Pro is too expensive for individuals like me, costing about thirty dollars per user per month. The context limits I had in 1.5 were addressed with one million token context in Gemini 3 Pro, which is good, but it earlier faced context limits and hallucinations. Google Gemini AI's responses tend to be long, requiring users to read considerably more than other models that provide crisper answers. This is something that could be improved.

Customer support is generally very stable, and it is rare that I face issues with Google Gemini AI. On the rare occasions when I do encounter a problem, customer support is very responsive and provides instant help. Google Gemini AI has excellent customer support.

I was previously using ChatGPT-4, which was creative but too expensive. I also tried Claude 3.5, which was a reasoning king but not very reliable, and I used Groq, which was fast but inconsistent. I considered switching to Gemini due to its free tier access, which allowed me to integrate with Gemini API keys and develop rapid prototypes. The Google Ecosystem plus Vertex AI also played a role in my decision to switch to Gemini.

While evaluating other options, I looked at ChatGPT-4 and found it expensive. I also tried Claude 3.5 but found it lacking in real-time search grounding for its answers at that time. Groq was another option, but it lacked consistency. Ultimately, considering my choices, I found Gemini appealing due to its free tier access and one million token context.

There is a significant return on investment, with a reported four times productivity increase on research and coding tasks that I do daily. The one million context of Gemini 3 Pro helped handle full project specifications in one go. Personally, it saved over sixty hours across twenty-five projects during my one hundred-day journey. Workspace usage of Gemini 3 Pro for coding assistance significantly aids in building, prototyping, and preparing production-grade applications in a very short time. In the agency I am part of, dependency on graphic designers and UX designers for creative needs has shifted to utilizing Nano Banana, Gemini's image modeling tool. Prototyping has increased, and the output quality has been exceptional with Google Gemini AI.

Prototyping has increased by fifty times, so earlier we were taking approximately two and a half to three months to build a prototype, but now it is just a matter of wrapping up the application based on Google Gemini AI API key. The workflow has completely shifted to become more prompt-focused in my agency, where we pass certain things to Google Gemini AI API key and expect results from it. Prompting in the right manner gets the right result. The prototyping time has cut down from two months to just two or three hours. Productivity has increased four times on research and coding tasks. Gemini Pro's one million token context handles the full project specifications.

Personally, during my one hundred-day journey, it saved approximately sixty plus hours across twenty-five different projects that I built over time. Google Gemini AI has helped me tremendously with prototyping. The YouTube Optimizer project I built for clients in my freelancing journey had every data point grounded with Google search. Every time clients give us reviews, it leads to beautiful outcomes because they see real changes in the number of views and likes. Projects built using Google Gemini AI have been exceptionally successful.

The recent addition of Nano Banana and VO3 helped me integrate cool imagery on websites, making a significant difference in the UI. Clients have been happy about it. Earlier, a lot of time was spent on graphic design elements that clients requested. Now with Google Gemini AI, which has multi-models including image, video, and chat, I use the image model to generate cool images that clients are asking for and integrate them rapidly into the website. This has significantly reduced the prototyping time and reliance on graphic designers. The time saved for a web agency with fewer developers has been tremendous because we no longer need to depend on graphic designers and researchers to figure out niche audiences. Instead, we rely on Google Gemini AI to obtain niche audience mentality, build applications, create images, and create marketing content with VO3.

We have the Google Ecosystem plus Vertex AI for enterprise control. I would advise others to open Google AI Studio, start building applications, chat with the models, and try using multi-models including image, video, and chat models. In chat, there are different models like 1.5 Flash, 3 Pro, and 2.5. I recommend switching between these models to see how they respond to the same prompt. Everyone should utilize their free tier access and API keys to build applications. It is tremendously useful because it is available for free. Google Gemini AI also helps individuals who are non-experts easily create projects; it comes down to the type of problem presented and the conversations with Google Gemini AI. I gave this review a rating of nine out of ten.

Private Cloud

Google

My use cases for Google Gemini are largely towards market research where I generate reports and understand the market, and there is a clear differentiator with Google Gemini that wasn't there earlier with other LLMs, specifically that it does a better job with real-time searches of content because it has access to Google's search results.

I use it for creating market research reports, planning strategies, and analyzing spreadsheets since my role involves a lot of market strategy and planning how to enter a new market.

When it comes to integrating Google Gemini with other Google products, it integrates well, including on Google Sheets where you can ask questions. I think it's available there, but I'm not sure about Word. Using it on the spreadsheets integrates seamlessly because they have the Google Suite available.

Regarding the real-time data streaming analysis tools of Google Gemini, I find them effective, as these are foundational LLMs fed with data sets every day, and I tend to shuffle between three or four different LLMs, including Google Gemini. From my experience, Google Gemini stands out for real-time data analysis and factual information.

The best features for my use cases with Google Gemini are its dynamic LLM model that can perform dynamic searches, which is wonderful for factual searches to understand numbers and to generate real-time answers.

I find that Google Gemini does a really good job and that's when I use it to get factual real-time answers.

Google Gemini's biggest strength is also its drawback; it's excellent for generating reports and working with data in real-time, but it isn't the most creative LLM for tasks such as creating a digital storytelling campaign or crafting marketing messages.

Google Gemini's biggest strength is also its drawback; it's excellent for generating reports and working with data in real-time, but it isn't the most creative LLM for tasks such as creating a digital storytelling campaign or crafting marketing messages.

I have been using Google Gemini directly for almost three years now since it came out.

For my use case, stability is good; I don't notice any glitches, and it's quite stable overall.

In terms of scalability for Google Gemini, the paid tiers offer better access to more computing power, which is where my use case hits the limits, but this would be a better question for someone developing AI applications.

I have never contacted technical support from Google regarding Google Gemini because I've never had the need for it.

Positive

Since I consume Google Gemini on the cloud, there's no setup involved; you just go and start using it.

When comparing Google Gemini with Claude, Llama, and GPTs, each has its benefits based on use cases. Since I'm not a developer, I don't use them for creating code bases beyond some HTML or CSS scripting. I understand that different LLMs serve different purposes, and I feel Google Gemini excels for reporting and analysis.

I currently work at Oracle, so most of the tools we use are in-house and we don't use many third-party tools. However, one tool which comes into use quite a bit for us is Seismic, which I just started using.

Seismic is a sales enablement tool that integrates with Salesforce, and as I am on the marketing side of things, we use this tool for providing enablement collateral for sales enablement. Personally, I use Figma as an open-source tool. After AI has come in, I largely rely on AI for most things and we have Oracle's own copilot.

As an Oracle employee, I don't directly work with any Google solutions for AI or Microsoft, but we have Oracle's own AI interface that takes APIs from different LLM models, so we consume ChatGPT, Google Gemini, and Cohere through Oracle's interface built for us.

I have not used scalable ML models, as I don't directly deal with the engineering side of things. I have not used Google Gemini on-premises; everything I've used has been through existing Google servers. For the pricing of Google Gemini, you can find it online; I've not used the paid version, but I know ChatGPT's paid versions aren't very expensive and I find the pricing model satisfactory for users.

I don't use Google Gemini through AWS Marketplace as I'm an Oracle employee, so we use it through OCI, which is Oracle Cloud Infrastructure, a competitor of AWS.

My rating for Google Gemini is 8 out of 10.

Public Cloud

Google

Google Gemini AI is used for common, basic digital assistant queries such as asking about the weather and the time. Often there are conflicting responses from Google Home, Google Home Mini, and the phone, as if they are not synchronized or competing against each other.

Google Gemini AI eases rudimentary tasks, such as checking the calendar, reminders, and releases without having to pick up the phone. Before Google Gemini AI, Alexa from Amazon was preferred as it came across as more responsive. However, Google Gemini AI is better in terms of searching the web, considering that Google Gemini AI is a property of Google, and the search results when looking for answers from the web are superior compared to those given by Alexa.

Google Gemini AI is not used much because it does not appear to be as responsive or as effective as Alexa when responding to questions, queries, instructions, or commands. When in a room with Google Home Mini and a phone nearby, Google Gemini AI would interrupt or catch the command instead, resulting in an unexpected response. A strength that also becomes a weakness is that Google Gemini AI catches the command better than other Google digital assistant devices. Additionally, there is a slow response rate, especially when connecting a mobile phone to Android Auto.

Google Gemini AI has been in use since it was installed or updated on the mobile phone approximately six months ago.

There are no bugs or issues found with the system. Google Gemini AI has proven to be reliable so far.

Support was not requested.

Negative

Alexa and Google Gemini AI are used, and Siri is also used sometimes for an iOS phone. All three are used simultaneously.

The setup was seamless. As soon as the phone received its update and restarted, the digital assistant changed seamlessly to Google Gemini AI without any issues.

The pricing of Google Gemini AI is not well understood, so no feedback can be provided on the cost. It was thought to have come together with the device subscription.

The overall review rating for this product is 6 out of 10.

I am aware of Google Gemini translation tools, but I haven't used them. They don't impact my work as all my work is done in English, so I don't need any kind of translation.

I've been using the API model of Google Gemini to do integrations. I use the tool to make decisions on the next step through the API to Google Gemini, which helps make my software smarter. I can make decisions based on what kind of information I need to extract and parse. Based on Google Gemini, I make my software easier to integrate and easier to make decisions, because I don't have to think about all the details. I just focus on what is important for me.

There is another benefit in that Google Gemini is highly competitive from a cost perspective. The cost per token is very competitive. It's not the best - the Chinese models such as DeepSeek are better, but I can't use them. From a cost perspective, between European and United States options, Google Gemini is very competitive.

It's not only its cost, it's also very performing. The time it takes to process the prompts and context information is very fast. It's one of the fastest models in the market. That's another advantage. There are other models which are also very good in quality but not as fast, such as Anthropic Claude. Google Gemini currently has a very good balance.

The AI capabilities of Google Gemini are a multi-modal LLM which allows me to pass documents, images, and texts in the same prompt. Being multi-modal makes my life easier. I don't have to translate information before I can work with it. Google provides free tools to test scenarios, which speeds up development and reduces costs. It's a very fast and accurate model with quality responses.

There's a new release every two months and it's very fast. I would say that they should keep increasing the context size of information that we can provide. Going from 1 million to 10 million would be fantastic. The context size and improving creativity without hallucinating is very difficult to do, but I expect them to achieve that. I expect them to be more creative but still control the amount of hallucination.

The tool just works as expected.

The tool just works as expected.

The tool just works as expected.

The biggest blocker for scaling is the context size. If you want to grow the amount of information that you want to insert into the model before you provide an answer, you have to use different techniques. The context window is always a limitation. There are techniques which help you to address those limitations, retrieve augmented generation, RAG, and Kada. That's where the problems are too complex, and you have to find ways to handle the scale of the problem. There are scalability issues, and they are all related to what you can do within that context window of a million tokens.

Regarding ease of scaling, I would rate it a six.

So far, it has been perfect, deserving a rating of ten.

Positive

I have used OpenAI, specifically the GPT-3 Mini and other models from OpenAI, including ChatGPT. I have also used the Anthropic model called Sonnet.

It is very easy to set up as it's a well-thought tool for developers.

You create your own application and your application can use Google Gemini by calling directly the Google Cloud. You don't have to worry about development, and concerning the Gemini tool, you don't have to worry about having multiple environments for development, quality and production. You just use a single one.

There is a free layer which you can use as a developer, using the Google AI Studio tool. For some of the models it's actually free. It doesn't cost anything, but once you get to production scenarios in which you have to use the API, you have to pay. If you're using the APIs to make calls for automatic scenarios of data analysis, then you have to pay. It's very competitive. In a scale of one to ten in the market, I would rate it probably a seven or eight.

If ten is expensive and one is free, I would rate it a three or four.

Google Gemini has the best combination of scalability, costs, performance, and accuracy. These things evolve very fast, but currently, on these four vectors, I believe it's very competitive and the combination of these four is better than any other that I'm aware of.

To hallucinate is when the model lies to you. Sometimes the responses are lies because it starts to imagine things which are not real. That's a hallucination. It's similar to being on drugs. I would rate Google Gemini as an eight.

Public Cloud

Google

I use and actively participate in Google Gemini research, specifically the Gemini 2.5 pro-research model. I conduct strategic and tactical research in various areas.

Google Gemini is a substantial time saver and provides reasonably accurate results, even though hallucinations are possible. I'm impressed with its orchestration capabilities, which structure the workflow for research automatically and meaningfully, making it a helpful tool. It helps in efficiently navigating a large volume of information and saving time.

There are ways to better enable and provide greater flexibility for experts to be more actively involved. Currently, it operates mostly autonomously, and while it provides structured activities, making the research configuration more accessible and flexible would be beneficial.

I have been using Google Gemini since it was released.

I am impressed with Google Gemini's scalability. It can conduct research quickly, taking only five to seven minutes to produce a ten-page research document with a reasonable executive summary.

I do not leverage Google support. They rely on a self-service approach, providing a lot of information online through blogs and documents. I am accustomed to this method, though I understand others may not rate it highly.

Positive

The setup is similar to any AI tool. The more I understand how AI works, the more effective Google Gemini is. I consider myself experienced, but many may find it complex, especially if not familiar with AI.

The pricing for Google Gemini is in line with other providers, harmonized. The feature of Gemini 2.5 research is highly discounted, but I understand costs may increase as features are released under open source licenses.

I use multiple frontier models for different purposes, including ChatGPT for document articulation and Anthropic Claude for developing architecture and solutions. These models complement each other due to their specialized training.

I recommend using multiple frontier models for different tasks. Larger LLM providers offer specialized models; for instance, I use Google Gemini 2.5 research for research purposes. I rate the overall solution an 8 to 9.

I have used that recently to create test cases for my scenarios. I have prompt engineering skills, so it is easy for me to get the right response which I use in my project.

I have used it for creating my test cases. I gave the prompt about how I could have the steps as I was new to the tool at that time and was not knowing how much time it would take for me. I got the response indicating the time required to learn and apply the skills. When I used the prompt to create test cases and added them to the project, I received good feedback indicating I had covered all positive and negative scenarios.

Most of the inputs are acceptable. It will give the correct response, but we need to have appropriate skills. For example, if we are not sure about our question or what desired output we need, if it is not a valid question, we will not receive the correct solution. The process requires natural language processing skills and knowledge for all prompts.

I use ChatGPT on a daily basis. It is available on my phone and I use it for basic daily chores such as food recipes and other tasks, apart from technical work. I have also used ChatGPT to create test cases. I have compared responses from Gemini and ChatGPT and received similar results but presented differently. Every tool has its uniqueness. It is good, and I am enjoying using both tools, but most often I use ChatGPT because I haven't used Gemini recently.

In initial stages, when using without prior experience, we must have basic understanding and prompt engineering skills. It will work for others even if they do not have skills from an IT professional perspective. Normal people can use it to improve their English or grammar. They can give prompts and expect sentences without grammatical mistakes. This way it is very helpful for normal people as well.

I have completed certification on Copilot, ChatGPT, and prompt engineering. I understand how we can use AI tools for daily work and testing progress. I have understanding of these GenAI products.

The main advantages are the test cases and scenarios covered from a user perspective. It also provides a scheduled timeline for solutions. If we work according to that, it takes approximately that time. We need to have a good understanding of the prompts we are using to get valid results. I would try and explore more options in Gemini in the future.

According to my understanding, if we provide a valid and proper prompt and seek a response, it will definitely give valid and helpful responses 95% of the time if we use it correctly.

I was thinking that now it is creating advanced images. In the same way, we could get video versions easily so it would be very helpful for people interested in video editing. If we give the scenario, it should try to create a story in video edition. I have not used that if it is already available.

I used it six months ago.

I didn't face any issues, but if we have not signed in to Gemini and other AI apps, it will not record our history and will give random data. When we sign up and use it daily, it collects our history and gives perfect responses suitable for our point of view. If we have the sign-in option, it will definitely give more correct answers instead of using it in online mode without signing in.

It is scalable.

I have used different solutions. Currently, I am using Tosca. Previously, I worked on LEAPWORK for more than two years. For the past three months, I have been using Tosca automation tool.

I have not tried that yet.

I haven't used it to that extent.

I have not explored that yet.

I have not used the advanced version yet. I have explored what was available for me in the free versions.

I use ChatGPT more often, but in some situations, I have used Gemini and gotten good results for my use cases. I have also used the suggestions which I received from it and it has given me positive responses.

I have basic understanding of this.

It is a good tool. Even beginners can use it. For IT professionals, if we get dedicated code, there should be a version specifically for IT professionals who can give input and get desired output for coding parts.

Users can definitely try the free version which is available for everyone. If they want more privacy and advanced features, they can pay and try that as well to get more detailed answers. The free version is good, but the main consideration is using our prompts. We cannot use any coding in online mode due to privacy concerns. I prefer using the advanced paid version which has more privacy for all IT-related work.

I do not have much knowledge about machine learning algorithms as I have not recently worked with them.

I worked for more than six months on that in my previous project.

Review Rating: 8/10

We have implemented real-time data streaming and analysis tools in Gemini. We have used them to develop complicated systems such as a tender valuation system and a vision tool to classify images on real-time data. The performance is good.

The best features in Gemini are quite good. It is at the top of the chart as far as the models are concerned. It is good through API and they offer their services. The response of the model is good both for vision as well as for the text, which is the large language model.

The integration of Gemini with other Google services is quite good. We develop the application using open source platforms such as LangGraph or LangChain, where the integration for Gemini is quite good. While we don't use other Google services such as cloud, the integration appears to be good. The only drawback for Gemini services or Google Vertex AI platform is that their platform understanding and getting started with the platform is a difficult process.

Gemini's deployment flexibility, both cloud and on-premises, primarily benefits my organization by being in the cloud. It works through AWS cloud and the integration is easy.

Gemini can be improved in several ways. The ChatGPT platform is much more simple for getting started, whereas Gemini's initial setup is more difficult, particularly when using other services such as Document AI service. For Document AI services, there is a long drawn process involving installing the Google command-line interface, downloading the credentials, and configuring application access to those credentials. Microsoft Azure works through API calls, which means you download an API token, store it in your program, and start using it. With Gemini, it's a more tedious process. Though the facilities offered in Google Cloud Platform are extensive, one has to make a rigorous initial effort to get all of those things started.

I have experience with Gemini since it was launched when version 2.5 was released approximately three to four months ago. It is a multi-modal model that can be used for both image and text processing.

Gemini is a stable solution. Any AI solution using a large language model is probabilistic. Whatever it generates is basically probabilistic text. One has to iterate and work out a solution to ensure stability. It is stable, but the developer has to make it stable.

Gemini is a scalable solution.

The technical support from Gemini receives a rating of seven. We didn't utilize much technical support as we managed independently. They do have good technical support, though for these types of technologies, most fixes or solutions are available online.

I work with other solutions including AWS and Google GenAI services. From Microsoft Azure, we use their AI Foundry. We are using the services regarding the Large Language Models and the Document Intelligence Service from both providers. These services are quite well known within the AI community.

My relationship with Gemini is as a user. They have another partner through whom they bill, and we utilize it to develop AI applications.

Gemini is not purchased through the AWS marketplace. It is obtained directly through a Google partner. For a corporate account, they have partners through whom you can acquire it.

Based on my experience, I would rate Gemini overall eight out of ten.

Public Cloud

Amazon Web Services (AWS)

I use Google Gemini primarily with my internal documents for accessing, revising, rewriting, and creating summaries.

The only advantage that I see is that Google Gemini is integrated into the Google ecosystem when we are on the Workspace plan. This makes it easier to access internal files without having to feed information from outside. The AI can work on those internally. It saves a lot of time in summarization, mail creation, and working with documents.

I would say the context length could be improved. When working on a 20-page document, Google Gemini sometimes loses context about earlier parts. It would be beneficial if the context limit, such as 128K tokens, were increased.

I have been working with Google Gemini for approximately two years.

I do not need any additional installation except activating the subscription license. There is no need for maintenance.

Google Gemini is quite stable but, like other AIs, it sometimes hallucinates.

Specifically for Gemini, I have not used the support. However, in general, I have given a rating of five because it's really difficult to reach them.

Neutral

I use ChatGPT apart from Google Gemini and have also used other AIs like Anthropic Claude, Deepseek, and others for different tasks.

There was no need for installation, only the subscription needs to be activated.

The per license cost is on par with others, but with the number of licenses, it becomes expensive.

I have used ChatGPT, Claude, and open-source ones like Deepseek for my own use cases.

Sometimes Google Gemini could hallucinate and provide incorrect information, which needs explanation for correction. I would rate my overall experience with Google Gemini as a nine out of ten.

Public Cloud

Google

I use Google Gemini for summarizing content and troubleshooting; it has helped script and build apps. That's really what I've used it for.

The most beneficial aspect of Google Gemini for me is that it's able to do searches much better. The best way to describe it is that it's much more intensive and much more specific.When I do a Google search and look for something, I have to weave through responses, and specifically on certain areas where I've done several searches, it's difficult to update queries to make them better.

Areas that could be improved with Google Gemini include more up-to-date content, more flexibility, and quicker responses. This is probably everyone's complaint. If you start using some AI tools quite a bit, they can get a little slower.

I have dealt with Google Gemini for two years.

When I have reached out to Google Support, it's been very limiting. I would rate Google at about a five.All the factors play a role; it's tier one, someone getting a hold of you and then maybe the right person as well. Microsoft has done better, though they're not great at it, but they seem to be more responsive.

Positive

The main differences between Google Gemini and some of the other similar products lie in the model and the responses. My favorite so far is ChatGPT; I seem to have the most success there and I prefer the answers. But there are certain limitations.I sometimes go back and forth; when I'm specifically dealing with Microsoft stuff, I go straight to Copilot and ask a question there. I have noticed that even there I'll get some outdated answers versus ChatGPT. When it comes to scripting, I have used others besides Google Gemini, and my experience so far is that I would prefer using ChatGPT over the three options. Among ChatGPT, Google Gemini, and Copilot, I'm probably most of the time leaning on ChatGPT.

I would recommend Google Gemini to others. I would rate Google Gemini 8 out of 10 overall.

I use Google Gemini as an intelligent search engine to conduct literature searches. It answers questions, even those I know the answers to, and verifies whether they are correct. I also use it to assist with functionality in KNIME and to resolve issues with applications like YouTube.

The most valuable feature of Google Gemini is its ability to function as an intelligent assistant, providing accurate answers to natural language queries and performing translations. It has a feature to place a slider on the screen that reports on world matters on a map. Additionally, Google Gemini offers advice on using tools like KNIME, enhancing its utility in professional settings.

The binning process could be more intuitive, especially when grouping data into categories like age groups. In particular, while the Auto-binner works well, the more general binning tool could be improved.

I have been working with Google Gemini for about six months to a year.

Google Gemini is very stable. Everything I've tried so far works without instability, bugs, or hallucinations. While chatbots can produce hallucinations, I have not encountered any with Google Gemini.

I previously used a tool called Leonardo, which was a powerful rule-based system. I also have experience with many other tools no longer available.

It took less than a minute to set up Google Gemini.

Google Gemini is free.

I have considered using other tools but settled on Google Gemini. Other tools used in the past include a solution called Leonardo.

I do not currently have any recommendations for improvement. The binning could be more intuitive, but this is a minor issue. I rate Google Gemini nine out of ten.

On-premises