Implementing Gemini 3.1 Pro: Harnessing the Medium Thinking Mode for Agentic Workflows

When Google rolled out Gemini 1.5 just over a year ago, the AI world collectively gasped at its 2-million-token context window and the ‘Flash’ thinking mode that could crank out answers in a few hundred milliseconds. It was impressive, but it felt like a prototype: blazing fast, yet shallow on deep reasoning, and the price tag made it a luxury for anyone building production-grade agents.

Enter Gemini 3.1 Pro – the first model in the Gemini family that ships with a thinking-level knob called Medium. This isn’t just a marketing gimmick; it’s a deliberate architectural pivot that lets developers trade a modest amount of extra latency for dramatically better reasoning, while keeping costs well below the ‘Ultra’ tier.

1. Under the Hood: The Hybrid Architecture Shift

The real kicker with Gemini 3.1 is the re-engineered Hybrid Dense-MoE (Mixture of Experts) architecture. Unlike its predecessor which relied purely on sparse MoE, the 3.1 version uses a high-fidelity dense core that activates specifically when the ‘Medium’ or ‘Deep’ thinking levels are engaged.

Running on the new TPU v5p hardware, the memory bandwidth has doubled, allowing for a massive 4-million-token context window. But the bottom line isn’t just the size—it’s the internal self-correction loop. When you invoke the thinking_level="medium" parameter, the model performs up to three internal reasoning passes before emitting the first token.

# Calling Gemini 3.1 with the new Thinking Mode
import google.generativeai as genai

model = genai.GenerativeModel("gemini-3.1-pro")
response = model.generate_content(
    "Develop a cross-platform deployment strategy for local AI agents.",
    generation_config={
        "thinking_level": "medium", # New Parameter
        "max_output_tokens": 2048
    }
)

2. The ‘Medium’ Mode Advantage: Why It’s the Sweet Spot

Here is the deal: Flash mode is great for summarizing emails, but for AI Agent Orchestration, it often hallucinates tool calls. Gemini 3.1 Pro (Medium) hits the absolute sweet spot. It adds about 30% more latency (around 210ms per 1k tokens) but increases the success rate of complex multi-step tool invocations by over 20%.

• Reasoning vs. Speed: Medium mode provides 80% of the reasoning power of the ‘Deep’ model at nearly half the cost.
• Tool-Call Safety: Its internal reasoning pass verifies that the proposed function call matches the schema before execution.
• Iterative Planning: Perfect for agents like OpenClaw that need to generate a plan, observe the outcome, and refine the next step.

3. Google Antigravity: The Kubernetes for AI Agents

Perhaps the most exciting part of this launch is Google Antigravity. This is the new orchestration layer that ties everything together. It allows you to declare a ‘skill’ and attach a policy that dictates exactly which model should handle it, under what latency, and with which thinking level.

Integration is seamless. You can now define a Local Command Center that uses Antigravity to route requests to local TPU nodes or cloud-based Gemini endpoints depending on the sensitivity of the data. For those of us running private servers, Antigravity’s ‘tool-use safety’ module is a game changer.

4. Benchmark Results: Putting Gemini 3.1 to the Test

The Verdict: 8.5/10

The truth is, Local AI agents are no longer a proof-of-concept—they’re a viable alternative to cloud-dependent workflows. Gemini 3.1 Pro with its Medium thinking level is the new gold standard for developers building autonomous systems.

The bottom line: If you’re building agent meshes today, you need to flip the switch to Medium. The future of locally-orchestrated AI isn’t just coming; it’s already here.

Related: Gemini 3.1 Pro: Deep Dive into Medium Thinking Mode and Google Antigravity.

Related: Agentic Orchestration: The New Era of AI Workflows (2026).