Google AI Releases Gemini 3.1 Professional with 1 Million Token Context and 77.1 % ARC-AGI-2 Reasoning for AI Brokers

Google has formally shifted the Gemini period into excessive gear with the discharge of Gemini 3.1 Professional, the primary model replace within the Gemini 3 sequence. This launch isn’t just a minor patch; it’s a focused strike on the ‘agentic’ AI market, specializing in reasoning stability, software program engineering, and tool-use reliability.

For devs, this replace alerts a transition. We’re transferring from fashions that merely ‘chat’ to fashions that ‘work.’ Gemini 3.1 Professional is designed to be the core engine for autonomous brokers that may navigate file methods, execute code, and cause by way of scientific issues with successful price that now rivals—and in some instances exceeds—the trade’s most elite frontier fashions.

Large Context, Exact Output

One of the crucial speedy technical upgrades is the dealing with of scale. Gemini 3.1 Professional Preview maintains an enormous 1M token enter context window. To place this in perspective for software program engineers: now you can feed the mannequin a whole medium-sized code repository, and it’ll have sufficient ‘reminiscence’ to grasp the cross-file dependencies with out shedding the plot.

Nevertheless, the actual information is the 65k token output restrict. This 65k window is a major soar for builders constructing long-form mills. Whether or not you’re producing a 100-page technical guide or a fancy, multi-module Python utility, the mannequin can now end the job in a single flip with out hitting an abrupt ‘max token’ wall.

Doubling Down on Reasoning

If Gemini 3.0 was about introducing ‘Deep Pondering,’ Gemini 3.1 is about making that considering environment friendly. The efficiency jumps on rigorous benchmarks are notable:

The 77.1% on ARC-AGI-2 is the headline determine right here. Google crew claims this represents greater than double the reasoning efficiency of the unique Gemini 3 Professional. This implies the mannequin is way much less more likely to depend on sample matching from its coaching information and is extra able to ‘figuring it out’ when confronted with a novel edge case in a dataset.

The Agentic Toolkit: Customized Instruments and ‘Antigravity‘

Google crew is making a transparent play for the developer’s terminal. Together with the primary mannequin, they launched a specialised endpoint: gemini-3.1-pro-preview-customtools.

This endpoint is optimized for builders who combine bash instructions with customized capabilities. In earlier variations, fashions usually struggled to prioritize which instrument to make use of, typically hallucinating a search when an area file learn would have sufficed. The customtools variant is particularly tuned to prioritize instruments like view_file or search_code, making it a extra dependable spine for autonomous coding brokers.

This launch additionally integrates deeply with Google Antigravity, the corporate’s new agentic improvement platform. Builders can now make the most of a brand new ‘medium’ considering degree. This lets you toggle the ‘reasoning funds’—utilizing high-depth considering for complicated debugging whereas dropping to medium or low for normal API calls to avoid wasting on latency and value.

API Breaking Adjustments and New File Strategies

For these already constructing on the Gemini API, there’s a small however crucial breaking change. Within the Interactions API v1beta, the sector total_reasoning_tokens has been renamed to total_thought_tokens. This alteration aligns with the ‘thought signatures’ launched within the Gemini 3 household—encrypted representations of the mannequin’s inner reasoning that should be handed again to the mannequin to take care of context in multi-turn agentic workflows.

The mannequin’s urge for food for information has additionally grown. Key updates to file dealing with embrace:

• 100MB File Restrict: The earlier 20MB cap for API uploads has been quintupled to 100MB.
• Direct YouTube Assist: Now you can move a YouTube URL immediately as a media supply. The mannequin ‘watches’ the video through the URL somewhat than requiring a guide add.
• Cloud Integration: Assist for Cloud Storage buckets and personal database pre-signed URLs as direct information sources.

The Economics of Intelligence

Pricing for Gemini 3.1 Professional Preview stays aggressive. For prompts underneath 200k tokens, enter prices are $2 per 1 million tokens, and output is $12 per 1 million. For contexts exceeding 200k, the worth scales to $4 enter and $18 output.

When in comparison with rivals like Claude Opus 4.6 or GPT-5.2, Google crew is positioning Gemini 3.1 Professional because the ‘effectivity chief.’ In keeping with information from Synthetic Evaluation, Gemini 3.1 Professional now holds the highest spot on their Intelligence Index whereas costing roughly half as a lot to run as its nearest frontier friends.

Key Takeaways

• Large 1M/65K Context Window: The mannequin maintains a 1M token enter window for large-scale information and repositories, whereas considerably upgrading the output restrict to 65k tokens for long-form code and doc era.
• A Leap in Logic and Reasoning: Efficiency on the ARC-AGI-2 benchmark reached 77.1%, representing greater than double the reasoning functionality of earlier variations. It additionally achieved a 94.1% on GPQA Diamond for graduate-level science duties.
• Devoted Agentic Endpoints: Google crew launched a specialised gemini-3.1-pro-preview-customtools endpoint. It’s particularly optimized to prioritize bash instructions and system instruments (like view_file and search_code) for extra dependable autonomous brokers.
• API Breaking Change: Builders should replace their codebases as the sector total_reasoning_tokens has been renamed to total_thought_tokens within the v1beta Interactions API to higher align with the mannequin’s inner “thought” processing.
• Enhanced File and Media Dealing with: The API file dimension restrict has elevated from 20MB to 100MB. Moreover, builders can now move YouTube URLs immediately into the immediate, permitting the mannequin to research video content material while not having to obtain or re-upload recordsdata.

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