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I guess it was too soon to call this 4.0 -- but don't let the 3.1 fool you. |
This was way more than just a minor upgrade. |
This was one of the biggest capability jumps we've seen in a while — especially if you care about reasoning, research, and actually shipping well-built, high-quality work. |
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Everyone has been talking about 1 particular unbelievable improvement with this new update. |
Imagine going from scoring 31.1% in a reasoning test... to 77.1% and being the absolute best in the same test just a few months later -- but this is what Gemini 3.1 Pro just shocked the world with. |
More than a 100% upgrade in capabilities. |
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And this is abstract reasoning we're talking about -- not memorization or "glorified autocomplete". It had to solve problems with completely new logic patterns, problems it had never seen before -- or something like before. |
This is huge. |
And this makes the 1 million context window it has even more lethal for coding and every other use case we can think of. |
It's vastly superior to its predecessor in every way. The graphics and SVG generation are so good -- which is also a huge win for web developers. |
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1. Web browsing got dramatically better: 59.2% →... |
This one is just as important. |
On BrowseComp — a benchmark that measures how well a model can use web tools and navigate information — Gemini 3.1 Pro jumped from 59.2% to 85.9% -- overtaking all Claude models, including the recently released Sonnet 4.6. |
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That's huge. |
The difference between those two numbers isn't cosmetic. It's the difference between: |
Surface-level summaries vs. actual synthesis Grabbing the first answer vs. cross-checking sources Losing context across tabs vs. maintaining a clear research thread
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If you use AI for research, competitive analysis, trend tracking, sourcing stats, or building content from multiple references, this upgrade matters a lot. |
Better browsing doesn't just mean "it can search." It means it's better at deciding what to search for, what to ignore, and how to combine findings into something coherent. |
That's a big shift. |
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2. This reasoning upgrade is not a joke |
And neither was the test that measured it. |
On ARC-AGI-2 — a standard benchmark designed to test abstract reasoning (not pattern regurgitation, but actual problem-solving) — Gemini jumped from 31.1% to 77.1%. |
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That's not incremental improvement. That's a different class of performance. |
What does that mean in real life? |
It means: |
Fewer moments where the model "almost" understands your problem but misses a key constraint. Better step-by-step thinking when tasks require multiple logical hops. Stronger performance on planning, debugging, and structured workflows. More reliable outputs when you're building agents or automation.
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If you've ever felt like an AI model lost the thread halfway through a complex task -- this is the kind of upgrade that directly addresses that frustration. |
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3. |
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Dictate prompts and tag files automatically |
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Stop typing reproductions and start vibing code. Wispr Flow captures your spoken debugging flow and turns it into structured bug reports, acceptance tests, and PR descriptions. Say a file name or variable out loud and Flow preserves it exactly, tags the correct file, and keeps inline code readable. Use voice to create Cursor and Warp prompts, call out a variable like user_id, and get copy you can paste straight into an issue or PR. The result is faster triage and fewer context gaps between engineers and QA. Learn how developers use voice-first workflows in our Vibe Coding article at wisprflow.ai. Try Wispr Flow for engineers. |
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