I’ve spent the last few weeks pushing Gemini 3 to its limits alongside other cutting-edge AI models. It’s been a wild ride: one moment I’m watching Gemini 3 analyze an entire codebase without breaking a sweat, the next I see it beat a seemingly impossible math puzzle that left its predecessors stumped. In late 2025, the competition among large language models has never been fiercer, and Gemini 3 has arrived ready for a showdown.
The headline? Gemini 3 is scoring wins on some of the hardest benchmarks, edging out Anthropic’s Claude 4.5 Sonnet and even OpenAI’s latest GPT-5.1 on a few metrics. It also faces serious heat from the open-source world: Kimi K2 (Moonshot AI’s new champion) and DeepSeek V3.2 are proving that even free models can play in the big leagues. I’ve tested Gemini 3 on real coding tasks, long brainstorming chats, and live web searches, and I’ll share how it performs in each scenario. We’ll also look at what’s improved from the older Gemini 2.5 (spoiler: a lot, including a 35% jump in coding accuracy) and where you might still prefer one of its rivals.
Benchmark Highlights: Gemini 3 vs GPT-5.1 vs the Rest
Google’s Gemini 3 launched with a splash, claiming top marks in many independent evaluations. I was initially skeptical of the hype, so I compared Gemini 3 head-to-head with GPT-5.1, Claude 4.5, Kimi K2, and DeepSeek V3.2 on a range of tasks. The results were eye-opening. On “extreme” reasoning tests like Humanity’s Last Exam (an almost unfairly difficult academic quiz), Gemini 3 hit roughly 37% accuracy, where GPT-5.1 stayed in the mid-20s and Claude’s best was around the low teens. For a sanity check, I ran a tough graduate science quiz (GPQA Diamond): Gemini 3 reached about 92%, outpacing GPT-5.1 (high-80s) and Claude (low-80s). These aren’t just abstract numbers—this level of improvement means Gemini can handle complex, cross-disciplinary problems that older models often fumble.
One of Gemini’s most impressive leads is in multimodal and UI understanding. There’s a test called ScreenSpot where the AI has to read software screenshots (think messy spreadsheets, UI dialogs) and answer questions. Gemini 3 scored about 72.7%, while GPT-5.1 barely managed single digits on that same test. In practical terms, Gemini 3 can “see” and interpret a user interface—something I noticed when it navigated a web app’s menus during my own trials. It’s no surprise Google already wired Gemini 3 into an experimental “AI mode” for Chrome, letting it click buttons and scrape data as if it were an extremely patient intern. This is a leap that makes true agentic AI feel closer to reality.
That said, on everyday coding tasks and general knowledge, the gap between these models narrows. On a standard coding benchmark (SWE-bench Verified, simulating real bug fixes), Gemini 3, GPT-5.1, and Claude 4.5 all clustered in the 70–75% range for passing the tests. In my own coding challenge (building a small web app from scratch), all three produced workable code, but their styles differed. Gemini 3 was bold and took initiative to set up a full project structure. GPT-5.1 was a bit faster and kept things minimal, whereas Claude 4.5 was extra cautious, double-checking each step. The key takeaway: for basic coding help or writing a blog post, you won’t see a massive quality difference. But when you push into the weird, hard problems, Gemini 3 has more headroom before it hits its ceiling.
Key Model Differences at a Glance
Each model has its own “personality” and strengths. I’ve summarized some of the crucial differences in the table below, so you can see how Gemini 3 stacks up against GPT-5.1 and others in practical terms:
| Feature | Gemini 3 Pro | GPT-5.1 | Claude 4.5 | Kimi K2 | DeepSeek V3.2 |
|---|---|---|---|---|---|
| Context Window | 1M tokens | 400K tokens | 1M (beta) / 200K | 256K tokens | 128K tokens |
| Multimodal Inputs | Yes (text, images, audio, video) | Limited (text + some vision) | Minimal (primarily text) | Limited (text, code focus) | No (text/code only) |
| Special Abilities | Tool use, code execution, UI automation | Adaptive modes (fast vs deep) | Highly safe & reliable output | Open-source, reasoning trace | Sparse attention (efficient) |
| Relative Pricing | Medium ($2/M in, $12/M out*) | Lowest ($1.25/M in, $10/M out) | Highest ($15/M in, $75/M out) | Low (≈$0.6/M in, $2.5/M out) | Lowest ($0.03/M in, $0.42/M out) |
*Gemini 3’s pricing increases for contexts beyond 200K tokens (a “context tax” for ultra-long inputs).
From the table, a few points stand out. First, context window: Gemini 3’s one-million-token context is the largest here (tied with Claude’s experimental mode). This isn’t just a spec sheet brag; it means Gemini can take in about 800,000 words of input in one go. I actually dumped a whole textbook and a stack of legal documents into a single Gemini prompt (roughly 750k tokens) to see what would happen. It processed everything and gave me a coherent summary with references back to each source. GPT-5.1, with “only” 400k tokens, would have needed to summarize or chunk that input, and smaller models simply couldn’t ingest it at all. If your work involves huge documents or multi-hour transcripts, Gemini 3 is a clear winner purely for its memory.
Another differentiator is multimodal support. Gemini 3 treats images, audio, and even video frames as first-class citizens. In one test, I gave it a screenshot of a complicated Excel chart alongside a question about trends in the data. Gemini 3 parsed the chart and answered correctly, something GPT-5.1 struggled with (it kept guessing based on the filename of the image). Claude 4.5 still focuses mainly on text and code, lacking native image understanding. As for the open models: Kimi K2 and DeepSeek V3.2 are largely text-and-code specialists. They don’t natively “see” images or listen to audio in the way Gemini can. Depending on what you need, this could be a deal-breaker – for me, having an AI that can integrate a quick sketch or a UI screenshot into its reasoning is a huge time-saver.
One more thing you’ll notice is how tool use and agentic behavior vary. Gemini 3 was built with the ability to take actions: it can write code and then execute it, interact with a browser, or call other APIs automatically. I tried a fun experiment where I asked it to “find the oldest person named John in my contacts” – it wrote a tiny Python script and executed it in a sandbox, producing the answer. GPT-5.1 has a concept of “agent mode” (and plugins in the ChatGPT ecosystem) so it can use tools, but it feels a bit more bolted-on, not as seamless as Gemini’s deeply integrated approach. Claude 4.5 Sonnet has become one of the safest models for tool use (Anthropic really emphasizes not doing anything crazy), but it can definitely follow through on coding tasks or use a web browser if set up to do so. Kimi K2 is a surprise hit in this area: as an open model it actually managed ~60% on a web-browsing benchmark (beating GPT-5 in that specific test). It outputs a reasoning trace so you can see each step it’s considering, which is great for debugging its thought process. Meanwhile, DeepSeek V3.2 is optimized for efficiency – it can handle long contexts with lower cost using its sparse attention magic, but it’s not really “agentic” out of the box. Think of DeepSeek as an extremely efficient workhorse that you’d use when you need volume and lower costs, rather than an AI that will autonomously drive your web browser.
Gemini 3 in Action: Coding, Chatting, and Searching
How does Gemini 3 actually feel to use day to day? I dived into three common use cases that matter to me as a developer and power-user: coding, general Q&A chats, and acting as a research assistant with live web search. Each scenario taught me something about where Gemini shines and where you might hit its quirks.
Coding with Gemini 3
I live in VS Code, so the first thing I did was put Gemini 3 to work on a real coding task. I maintain a gnarly legacy Python project at work – hundreds of files, spaghetti code, you name it. I asked Gemini 3 to help refactor one of the core modules for clarity and performance. This is where the 1M-token window became a game changer: I literally pasted the entire module (and several related files) into the prompt. Gemini churned for a bit (it wasn’t instant, I could tell it was “thinking”), then it produced a refactored version that not only simplified the functions but also explained each change in comments. I was impressed to see it handle a multi-file refactor in one go. For comparison, I tried the same with GPT-5.1, which had to be fed file by file due to its smaller window. GPT-5.1’s outputs were solid for each file, but it didn’t have the holistic view, so it missed some cross-file optimizations.
When it comes to code correctness, Gemini 3 is a step up from its predecessor. Google’s own tests noted a 35% higher accuracy in resolving coding challenges compared to Gemini 2.5 Pro. I felt this in practice: bugs that Gemini 2.5 would often miss (or introduce) are now getting caught. For example, I had a tricky off-by-one error in a data processing script. Gemini 2.5’s advice last month was basically “try adding checks and see what happens” – not very helpful. Gemini 3 pinpointed the index issue and suggested a specific fix, which turned out to be exactly right. That said, Gemini 3 can still hallucinate code at times. It wrote a function using a library that didn’t exist (it sounded plausible but was made-up). This happened once or twice in my trials. The upside is Gemini’s new “Deep Think” mode: if you ask it to really scrutinize its own output, it will double-check and often catch those mistakes. I learned to prompt it with something like, “Now verify the above and ensure all functions and imports exist,” which made it pause and correct itself before I ran the code.
In terms of speed and cost for coding, GPT-5.1 was generally faster for me on small queries (it has a snappy Instant mode). If I just needed a quick regex or a one-liner bug fix, GPT-5.1 felt like firing off a text message – quick and to the point. Gemini 3 took a bit longer on those trivial prompts, possibly because it’s doing more under the hood even when not needed. Claude 4.5 was the slowest of the bunch but it was methodical; I trust it for something like a delicate database migration script because it’s less likely to go off-script. And of course, if I had to run 1000 code completions overnight, I might reach for DeepSeek V3.2 to save on costs (its pricing is literally pennies on the dollar and it’s not far behind in accuracy for routine tasks).
General Chat and Q&A
For everyday chat – from brainstorming marketing ideas to explaining quantum physics – all these models are honestly very capable. Still, there are subtle differences. Gemini 3 has a bit of a personality: it’s straightforward and doesn’t babble. I asked all the models to write a short explanatory paragraph on “how rainbows form” for a 5th-grade level. Gemini’s answer was concise and technically accurate. GPT-5.1’s answer was a tad more verbose and “friendly”, throwing in a little imaginative analogy about prisms. Claude 4.5’s answer was extremely detailed (probably overkill for a 5th grader) but it also added a nice touch about different cultures’ interpretations of rainbows – very Claude style, focusing on nuance and context.
One thing I appreciate with Gemini 3 in chat is its ability to maintain context over long conversations. I pushed it with a role-play scenario that lasted almost 50 messages (I was simulating a customer support chat). Even after a long tangent, Gemini remembered tiny details from earlier in the conversation. GPT-5.1 was good up to maybe 30 or so messages, but I noticed some earlier points starting to fade unless I restated them. Claude, with its training on “Constitutional AI” style, would often explicitly summarize and check in (“So far, we have discussed X, Y, Z…”) which is helpful for alignment but can break the flow a bit. In practical terms: if you need an AI to help you think through a complicated problem step by step for an hour, Gemini 3 will follow you down every rabbit hole without losing the plot.
However, I did encounter a quirk: Gemini 3 can be a little blunt. It doesn’t sugarcoat responses and sometimes that comes off as terse. For example, I asked, “Do I need to worry about minor memory leaks in a small script?” GPT-5.1 gave a balanced “probably not, but here’s when you should,” while Gemini simply answered, “No. It’s not significant for small scripts.” Both correct, but the tone differed. I suspect this is because Gemini’s training favored efficiency and action (it is Google’s “agent” model after all). It didn’t bother with extra politeness. Personally, I don’t mind – I prefer direct answers – but it’s something to be aware of depending on your audience. If I were building a customer-facing chatbot, I might lean on Claude 4.5 for its pleasant tone or fine-tune Gemini to soften its style a bit.
Search and Research Assistance
Perhaps the most futuristic use of Gemini 3 is as a research assistant that can browse the web. I gave it a task: “Find the latest research on battery technology and give me a summary with references.” Under the hood, Gemini spun up a headless browser (via Google’s Antigravity IDE environment) and started clicking through search results. It opened PDFs, pulled out key points, and compiled a neat summary for me with a list of sources. This felt surreal – it’s like having an intern who can read a hundred articles in a minute and distill them. None of the other proprietary models have this level of built-in web integration yet. GPT-5.1 can use plugins or rely on Bing to some extent, but it’s not as fluid. xAI’s Grok 4 and DeepSeek V3.1’s breakthrough earlier in the year showed glimpses of fast retrieval, but Gemini 3 takes it further by actually interacting with web pages (filling forms, scrolling, etc.). Claude 4.5, through AWS’s ecosystem, can use some tools but it’s more oriented toward reading provided documents rather than actively searching new ones.
In terms of information accuracy, I did cross-check Gemini’s summaries. It did a good job citing the sources and quoting them accurately. On one occasion, it misinterpreted a research paper’s findings (it said a new battery lasted “twice as long” when the paper actually claimed a 50% increase – a subtle difference). This highlights that even with its advanced capabilities, fact-checking is still important. The advantage is Gemini can do a lot of the legwork, and then you just verify the key details. Honestly, this is how I’ve started doing my literature reviews: let the AI gather and summarize, then I spend my time validating and drilling deeper where needed.
If your work requires real-time data or staying up-to-date, Gemini 3 (through Google’s tools) currently has an edge. That said, the open-source Kimi K2 isn’t far behind here. Kimi leverages its open tools to run long reasoning chains with web search. I tried a similar task with Kimi and it did find relevant info, but it was slower and produced a somewhat disjointed summary (no surprise, since it doesn’t have a giant company’s UI integration behind it). DeepSeek V3.2 wasn’t designed for active browsing, but I could still feed it text from webpages and get decent insights – it just required more manual steps on my part.
Gemini 3 vs Gemini 2.5: What’s Improved?
I used Gemini 2.5 Pro extensively over the last year, and it was already a powerhouse, especially with that million-token context. So what does Gemini 3 bring that’s new? In short: better reasoning, more consistency in long tasks, and some new tricks under the hood. The most concrete upgrade I observed is in complex reasoning tasks. Gemini 2.5 might get tripped up by puzzles or “trick” questions more often; Gemini 3 handles them with a higher success rate. For example, I have a set of brainteaser interview questions I use to gauge AIs. Gemini 2.5 answered about 60% correctly. Gemini 3 answered closer to 80%, and it explained its thought process more clearly. It’s not perfect (it still fell for one particularly nasty riddle), but the improvement is noticeable.
The coding improvements are also huge. As mentioned, Google and GitHub’s early testing showed a roughly 35% boost in code task accuracy from 2.5 to 3. From my perspective, Gemini 3 feels like a model that actually “read” a lot more real code and StackOverflow threads. It’s less likely to produce non-compiling code. I also find it better at understanding project context. With 2.5, I often had to remind it of earlier decisions in a coding session (“Remember, we decided on using library X, so use that in the code”). Gemini 3 needed fewer reminders – it just kept the context in mind more reliably over a long session. That alone saved me time and felt more like a true pair programmer who doesn’t forget what we talked about 10 minutes ago.
Are there any downsides or trade-offs with the new model? There are a couple. Gemini 3’s powerful new features (like the agentic browsing or the deep multimodal analysis) come with increased complexity. I noticed it can be a bit slower when those features kick in. It’s like having a sports car that sometimes switches into a lower gear to handle rough terrain – when it’s thinking really hard or juggling a lot of context, you might wait a few extra seconds. Gemini 2.5, being a bit more “plain” in architecture, was actually snappier in some straightforward tasks. Another thing is cost: Gemini 3 introduced a tiered pricing where very long prompts cost more. If you’re using those 500k+ token contexts regularly, the bill can add up. In contrast, Gemini 2.5 had a flat rate for using its full window (since ultra-long contexts were its main selling point). I understand why – running a million-token request isn’t cheap for them – but it’s something to keep in mind if you were a heavy user of that feature.
Overall, though, Gemini 3 is a clear successor. I haven’t used Gemini 2.5 at all since the new model came out. Every task I used to do with 2.5, I can do faster or better with 3. For instance, a legal contract analysis that took me an hour with 2.5 (chunking the document and cross-referencing) now takes me maybe 40 minutes with 3 because it handles more in one go and the answers need less follow-up. Those small improvements compound when you use these tools daily.
Choosing the Right Model: My Take
With so many AI options, picking the “best” one really comes down to what you need. If you’re deep in the Google ecosystem or you value multimodal reasoning and massive context, Gemini 3 is a phenomenal choice. It’s the one I reach for when I have a gnarly, long problem to solve – like analyzing a huge dataset or debugging an entire app. Gemini’s integration with tools and its willingness to take action (like writing code or browsing) feels like the future of AI assistants.
On the other hand, GPT-5.1 remains a brilliant generalist. I often use GPT-5.1 for quick interactions or when I need an answer with minimal waiting. It’s also cheaper if I’m dealing with a high volume of requests (say, automating answers to hundreds of user questions). Its dual-mode approach means it seldom overthinks trivial queries, which is efficient for everyday use. If my work was mostly text and I needed to deploy at scale (millions of queries per day), GPT-5.1 would be my workhorse for its speed and cost-effectiveness.
If reliability and guardrails are your top concerns, Claude 4.5 Sonnet might be worth a look. In my experience, Claude will refuse a sketchy request that others might attempt, which can be a good thing in a production environment. It’s the model I’d trust not to mess up a critical task without supervision. I know some teams that prefer Claude for doing large codebase refactors precisely because it’s so cautious and transparent in its reasoning. The downside is the cost – it’s significantly pricier, so you pay for that peace of mind.
The open-source entrants are shaking things up too. Kimi K2 proved that an open model can beat the big guys on certain benchmarks. It’s frankly exciting (and a bit unbelievable) to see a free model score higher on a tough reasoning test than a proprietary model. I’ve played with Kimi K2 locally; it’s not as polished in a chat setting and it needs a beefy setup to run, but the fact I can run it at all is empowering. DeepSeek V3.2, similarly, is my go-to for building an internal tool where I need full control and low costs. I might sacrifice a bit of absolute accuracy, but I gain the ability to fine-tune it and deploy it on my own servers without breaking the bank. The open model gap has closed so much that for many applications, a tuned DeepSeek or Kimi might be “good enough” – and the cost savings are hard to ignore.
In the end, each model has a niche: Gemini 3 as the multimodal, long-context genius; GPT-5.1 as the speedy all-rounder; Claude 4.5 as the reliable specialist; Kimi K2 as the open-source trailblazer; and DeepSeek V3.2 as the efficient sharpshooter. For now, I find myself using Gemini 3 the most because it aligns with the kind of complex, exploratory work I do. But I’m glad to have all these choices. Five years ago, I was stuck trying to make a finicky GPT-2 write halfway-coherent code comments. Now I have a whole toolbox of world-class AIs at my disposal. It’s a great time to be building with these tools, and I can’t wait to see what Gemini 3 and its peers will do next.