Gemini 3.1 Pro - A smarter model for your most complex tasks

The AI race continues. OpenAI launched GPT-5.3-Codex 2 weeks ago. Anthropic, Sonnet 4.6 this week. And Google? They just announced @Gemini 3.1 Pro, "a smarter, more capable model for complex problem-solving."

Available in products like @Google AI Studio, @Kilo Code, and @Raycast.

Game on!

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.

@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!

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.

i like it

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?

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.

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?