Gemini 3.1 Pro Arrives on Vertex AI: 1M Context, 94.3% GPQA, and One-Third the Cost

What Google Just Released

Google has made Gemini 3.1 Pro available in preview on Vertex AI and Gemini Enterprise as of April 2026, marking the model's official entry into Google Cloud's managed AI development environment. For enterprise teams that have been evaluating the model since its February 2026 preview debut, this availability on Vertex AI represents the pathway to production deployment within Google Cloud's compliance, security, and SLA framework.

Gemini 3.1 Pro is not a minor iteration. According to Google's own benchmark data, it leads 13 of 16 major AI benchmarks — including a 94.3% score on GPQA Diamond, a test specifically designed to evaluate graduate-level scientific reasoning across physics, chemistry, and biology. It also achieves 77.1% on ARC-AGI-2, one of the most demanding general intelligence tests currently in use. These are not marginal gains over Gemini 3 Pro; they represent a meaningful capability jump that Google is positioning as enterprise-ready.

Key Features

1 Million Token Context Window

Gemini 3.1 Pro ships with a fully released (not beta) 1 million token context window on input, with 64,000 tokens of output capacity. This is large enough to process entire legal contracts, multi-year financial reports, full software repositories, or complete technical manuals within a single API call. Google has added document-level caching for this context — meaning repeatedly queried large documents (such as a company's entire codebase) can be cached server-side, dramatically reducing latency and cost for iterative analysis tasks.

Native Video Understanding at 1 FPS

Unlike text-centric competitors, Gemini 3.1 Pro includes native video understanding at 1 frame per second. This enables use cases such as automated video audit trails, content moderation, product quality inspection from recorded footage, and meeting summarization — all processed natively within the same model, without requiring separate computer vision pipelines.

Multimodal Input: Text, Image, Audio, and Video

The model accepts text, image, speech/audio, PDF, and video inputs and outputs text. This unified multimodal capability is particularly relevant for enterprise workflows that historically required multiple specialized models — document OCR, audio transcription, image analysis — to be stitched together. Gemini 3.1 Pro handles all of these natively, reducing integration complexity.

94.3% GPQA Diamond Score

GPQA Diamond is a benchmark of graduate-level expert questions across hard science disciplines, specifically designed to be resistant to web search and rote memorization. A 94.3% score indicates that Gemini 3.1 Pro can navigate complex multi-step scientific reasoning chains — relevant for pharmaceutical R&D, materials science, financial risk analysis, and similar enterprise verticals where domain-specific depth matters.

Competitive Pricing: Approximately One-Third of Comparable Flagships

Gemini 3.1 Pro Preview is priced at $2.00 per million input tokens and $12.00 per million output tokens for contexts up to 200,000 tokens. For contexts exceeding 200,000 tokens, pricing rises to $4.00/$18.00 per million tokens. This is substantially lower than Claude Opus 4.6 ($15/$75 per million tokens) and positions Gemini 3.1 Pro as the cost-efficient choice among frontier models for high-volume enterprise workloads.

Usability Analysis

For teams already operating within Google Cloud, Vertex AI integration is the primary draw. Vertex AI provides managed infrastructure, VPC-SC compliance, CMEK encryption support, audit logging, and the organizational controls required for regulated industries — financial services, healthcare, government. Running Gemini 3.1 Pro through Vertex AI means these enterprise-grade safeguards apply automatically, without custom security engineering.

For document-heavy workflows, the combination of 1M context plus server-side document caching is the standout usability feature. Legal teams reviewing contracts, compliance officers auditing internal policies, or developers doing cross-repo code analysis can ingest massive documents once and query them iteratively without re-uploading each time.

The video understanding capability, while compelling, is still best suited for pre-recorded content at 1 FPS — it is not optimized for real-time video streams, which limits its current applicability in live operations scenarios.

Pros and Cons

Strengths:

• 94.3% GPQA Diamond score — the highest in its class for graduate-level scientific reasoning
• Fully released 1M token context window with server-side document caching
• Pricing roughly one-third of comparable flagship models (Claude Opus 4.6)
• Native multimodal support: text, image, audio, video, PDF in a single model
• Deep Vertex AI integration with enterprise security controls (CMEK, VPC-SC, audit logs)
• Leads 13 of 16 major AI benchmarks as of April 2026

Limitations:

• Preview status on Vertex AI means full SLA and GA-level support are not yet guaranteed
• 1 FPS video understanding limits real-time video applications
• 64K output token limit is lower than Claude Opus 4.6's 128K maximum output
• Heavy dependence on Google Cloud ecosystem creates vendor lock-in risk for multi-cloud strategies

Outlook

Gemini 3.1 Pro's Vertex AI launch sets up a direct competitive confrontation with Claude Opus 4.6 in the enterprise market. The two models target overlapping use cases — complex reasoning, large-context document analysis, agentic coding — but from opposite pricing positions. Anthropic's model is more expensive but leads on coding benchmarks and LMSYS Arena rankings. Google's model is significantly cheaper and leads on scientific reasoning benchmarks and multimodal breadth.

For cost-sensitive enterprises processing large document volumes, Gemini 3.1 Pro is likely to be the default choice. For organizations whose primary use case is software engineering and agentic coding, Claude Opus 4.6 will remain the frontrunner. The general availability release from Google — expected after the current preview phase — will be the signal for procurement teams to finalize their model strategy.

Conclusion

Gemini 3.1 Pro on Vertex AI is a compelling enterprise offering that combines frontier-level benchmarks, native multimodal capability, and price efficiency that few competitors can match. Its 94.3% GPQA Diamond score and 1M context window with document caching make it particularly well-suited for knowledge-intensive enterprise workflows — legal, compliance, research, and data analysis. Organizations with existing Google Cloud infrastructure should treat this as a high-priority evaluation.