Google’s Gemini 3 Just Crushed Every AI Benchmark – The Coding Wars Just Got Real

Well, this is fascinating. Google just released Gemini 3 on November 18th, 2025, and honestly, the numbers are pretty stunning. We’re talking about a 37.4 score on the Humanity’s Last Exam benchmark – that’s not just an incremental improvement, that’s a massive leap over GPT-5 Pro’s previous record of 31.64. To put that in perspective, that’s an 18% improvement on what’s considered one of the most challenging AI reasoning tests available.

What really caught my attention though is the timing. This comes just seven months after Gemini 2.5, less than a week after OpenAI’s GPT 5.1 release, and only two months after Anthropic dropped Sonnet 4.5. The pace here is absolutely relentless – we’re seeing major model releases every few months now, and each one is trying to leapfrog the competition. It’s like watching a high-stakes game of technological leapfrog, except each jump costs hundreds of millions in compute and research.

According to Tulsee Doshi, Google’s head of product for the Gemini model, they’re seeing “this massive jump in reasoning” with responses showing “a level of depth and nuance that we haven’t seen before.” Now, product heads always say things like this, but the benchmark scores seem to back it up. Beyond the Humanity’s Last Exam record, Gemini 3 also topped LMArena’s leaderboard – and that’s a human-led benchmark measuring actual user satisfaction, not just algorithmic performance.

The scale Google is operating at here is worth noting. The article mentions that the Gemini app currently has more than 650 million monthly active users. That’s approaching the scale of major social media platforms. When you’re serving that many users and can deploy a significantly more capable model overnight, the network effects become enormous. Plus, they’ve got 13 million software developers already using the model in their workflows – that’s a substantial professional user base that can immediately benefit from the reasoning improvements.

## The Coding Wars Heat Up

But here’s where things get really interesting – Google also launched something called Antigravity, which is their Gemini-powered coding interface. This isn’t just another ChatGPT wrapper for code; it’s a multi-pane agentic coding environment that combines a ChatGPT-style prompt window with a command-line interface and a browser window. The agent can work across your editor, terminal, and browser simultaneously to help build applications.

This puts Google in direct competition with some of the hottest startups in the coding space. Cursor, which raised $60 million in Series A funding earlier this year and is valued at around $400 million, has been making waves with its AI-powered IDE. Warp, another agentic coding environment, has raised over $23 million and has been growing rapidly among developers. Google is essentially saying “we can do this too, and we have the scale and model capability to do it better.”

The competitive dynamics here are fascinating. Microsoft, based in Redmond, Washington, has GitHub Copilot integrated into VS Code, which dominates the developer editor market with over 15 million monthly active users. OpenAI, headquartered in San Francisco, powers Copilot and has been the go-to for AI coding assistance. But now Google, from Mountain View, California, is bringing a potentially superior model (based on these benchmarks) directly to developers through an integrated environment.

What’s particularly smart about the Antigravity approach is that it’s not trying to replace existing developer workflows entirely. Instead, it’s creating a complementary environment where the AI agent can work across multiple tools simultaneously. This addresses one of the biggest pain points with current AI coding tools – context switching between the AI interface, your code editor, terminal, and browser to test changes.

The timing is crucial too. As of November 2025, we’re seeing massive enterprise adoption of AI coding tools. GitHub reported that Copilot is now used by over 1.3 million paid subscribers and more than 50,000 businesses. The market for AI-powered development tools is projected to reach $24.3 billion by 2030, growing at a 25% CAGR. Google is positioning itself to capture a significant portion of this market with a potentially superior underlying model.

## Market Implications and Strategic Positioning

Let’s talk about what this means for the broader AI landscape. Google’s strategy here seems to be about vertical integration – they’re not just building better foundation models, they’re building complete application experiences on top of those models. The Gemini app with 650 million users gives them a massive distribution advantage that pure-play AI companies like Anthropic (San Francisco) or Perplexity (San Francisco) simply don’t have.

The introduction of Gemini 3 Deepthink, a more research-intensive version coming to Google AI Ultra subscribers, is also interesting from a business model perspective. This suggests Google is moving toward tiered pricing based on model capability, similar to OpenAI’s approach with GPT-4 versus GPT-3.5. Ultra subscribers likely pay significantly more – OpenAI’s GPT-4 API costs about 10-20x more than GPT-3.5 depending on usage patterns.

From a competitive standpoint, this puts serious pressure on OpenAI. The San Francisco-based company has been the benchmark leader, but Google just leapfrogged them on key metrics while also matching their product breadth. OpenAI’s recent GPT 5.1 release was supposed to maintain their edge, but if Google’s benchmarks hold up in real-world usage, that advantage has evaporated quickly.

For enterprise customers, this creates an interesting decision point. Many companies have been hesitant to fully commit to any single AI provider given how rapidly the landscape is changing. Google’s latest release validates that strategy – the best model today might not be the best model next month. Companies like Microsoft (Redmond, Washington), which has invested $13 billion in OpenAI, now have to consider whether their partnership gives them access to the best available AI capabilities.

The developer market is particularly crucial because it represents both high-value users and influential early adopters. If Google can capture significant developer mindshare with Antigravity, it could create a flywheel effect where more developers use Google’s tools, provide feedback that improves the models, which attracts more developers. This is similar to how AWS built its dominance – by serving developers first, then expanding to enterprise.

## Technical Capabilities and Real-World Impact

The benchmark improvements Google is claiming aren’t just academic exercises. The Humanity’s Last Exam benchmark is designed to test general reasoning and expertise across diverse domains – essentially measuring whether an AI can think through complex problems the way a human expert would. A 37.4 score suggests the model can handle sophisticated reasoning tasks that were previously beyond AI capabilities.

For software development specifically, this could mean AI agents that can understand complex codebases, debug intricate issues, and architect solutions rather than just generating boilerplate code. Current AI coding tools are excellent for autocomplete and simple function generation, but they struggle with system-level thinking and complex debugging. If Gemini 3’s reasoning improvements translate to coding tasks, we could see a significant leap in AI programming capabilities.

The multi-pane interface in Antigravity addresses a real workflow problem. Currently, when using AI for coding, developers typically copy code from the AI chat interface, paste it into their editor, run it in the terminal, check results in the browser, then go back to the AI with feedback. Antigravity’s integrated approach could eliminate much of this friction, making AI-assisted development significantly more efficient.

However, there are some important caveats to consider. Benchmark scores don’t always translate directly to real-world performance, and Google has a history of impressive demos that don’t quite live up to expectations in practice. Remember Google’s LaMDA demo that seemed incredibly human-like, but the production version was much more limited. The real test will be how Gemini 3 performs in actual developer workflows over the coming months.

There’s also the question of safety and reliability. Google mentions that Gemini 3 Deepthink needs to pass “further rounds of safety testing” before release. This suggests they’re being cautious about the most capable version, which is probably wise given the potential for more powerful models to exhibit unexpected behaviors. The AI safety community has been increasingly concerned about rapid capability improvements outpacing safety measures.

## Looking Forward: The Acceleration Continues

What’s most striking about this release is how it exemplifies the current pace of AI development. Seven months from Gemini 2.5 to Gemini 3 with this level of improvement suggests we’re still in the steep part of the capability curve. If this pace continues, we could see even more dramatic improvements by mid-2026.

For investors and businesses trying to navigate this landscape, Google’s latest release reinforces several key trends. First, the foundation model race is far from over – no single company has established a durable moat. Second, vertical integration (model + application) is becoming increasingly important for capturing value. Third, developer tools represent a crucial battleground where AI capabilities can translate directly into business value.

The competitive response will be fascinating to watch. OpenAI will likely accelerate their own release timeline, Anthropic will need to demonstrate that their constitutional AI approach can keep pace with raw capability improvements, and Microsoft will have to decide how much to lean into their OpenAI partnership versus developing their own capabilities. Meanwhile, startups in the AI coding space will need to figure out how to compete with tech giants offering superior models at potentially lower costs.

Ultimately, this release feels like another inflection point in the AI wars. Google has reasserted itself as a serious contender after a period where OpenAI seemed to be pulling ahead. The next few months will show whether these benchmark improvements translate to real-world advantages and whether Google can leverage its distribution scale to gain market share. But one thing is clear – the pace of innovation in AI isn’t slowing down, and the stakes keep getting higher.

This post was written after reading Google launches Gemini 3 with new coding app and record benchmark scores. I’ve added my own analysis and perspective.

Disclaimer: This blog is not a news outlet. The content represents the author’s personal views. Investment decisions are the sole responsibility of the investor, and we assume no liability for any losses incurred based on this content.