Gemini 3.1 Pro

A smarter model for your most complex tasks

Software Engineering

Artificial Intelligence

Gemini has advantages at the moment, I enjoy working with different AIs to see how they develop.

Impressive direction, pushing the baseline forward for deeper reasoning is what actually unlocks more serious use cases. Complex problem solving needs more than fast answers, it needs structured thinking.

Curious to see how 3.1 Pro performs in longer multi step workflows.

I’m building Ahsk.app , a macOS AI assistant focused on practical, in flow AI use. Would love to connect and exchange thoughts.

Congrats on launching Gemini 3.1 Pro, it sounds like a solid upgrade for complex problem-solving. To enhance user engagement, consider highlighting specific use cases where it outperforms competitors. What is your strategy for ensuring users see the value in this advanced reasoning capability quickly?

Nice benchmark numbers. My concern is always the gap between benchmarks and the actual developer experience. I use Claude primarily for coding because, from my personal experience, it follows instructions pretty closely (though there's always room for improvement). For me, Gemini has historically been frustrating for me, inserting comments and refactoring code I didn't ask it to do. Would love to hear from anyone who's tested 3.1 Pro on real coding workflows, not benchmarks, and whether that's actually improved.

Hey there, congrats on this launch!!

For SaaS use cases involving long-context multimodal inputs (e.g., analyzing full user-uploaded PDFs + screenshots + code snippets to generate UI code, migration scripts, or automated test plans), what's the practical sweet spot you've seen for token efficiency and accuracy at the 200k–1M range?

Multi-step reasoning is where I actually see model improvements matter - not on benchmarks but when you're chaining tool calls and the model needs to track state across a longer context. How does 3.1 compare to 2.0 Pro on that kind of work? I've been testing various models on agentic workflows lately and the gap between 'can reason' and 'reasons reliably without losing context' is pretty big in practice.

Does google read these?

I'll give it a shot in gemini CLI and see what's up

I can't keep using Antigrativy, there is no update available; and I can't use the previous model.

@peter_albert nailed it. I'm running Gemini models in production for Aitinery (AI travel planner) and this is exactly the gap.

Benchmarks say Gemini is world-class. My production logs say it sometimes hallucinates restaurant names that don't exist and occasionally generates itineraries with 16-hour driving days. Benchmarks don't test "can this model reliably plan a family trip to Puglia without suggesting a 3am dinner reservation?"

That said — 3.1 Pro feels like Google is finally closing the gap between benchmark performance and real-world agentic reliability. The reasoning improvements matter more for agent builders than the raw intelligence bump.

The uncomfortable truth about the AI model race: for 95% of real applications, the difference between GPT-5.3, Sonnet 4.6, and Gemini 3.1 Pro is negligible. What matters is reliability, cost, and speed — not who wins on ARC-AGI-2.

Curious to see how 3.1 Pro handles multi-step planning tasks. That's where Gemini has historically struggled compared to Claude for agentic workflows.

If you're building with Gemini 3.1 Pro and want to keep API costs under control as complexity scales, check out TokenCut by agentready.cloud — it helps reduce token usage without sacrificing output quality. Perfect companion for a reasoning-heavy model like this one!

Gemini is alwasy good at benchmarks, but usually not great at agentic behaviour. The models have very weird behaviour. Almost like the Gemini team is not really testing them themselves.