Gemini 3 Pro Preview

Gemini 3 Pro (Preview) is Google/DeepMind’s newest flagship multimodal reasoning model in the Gemini 3 family. It is positioned as their “most intelligent model yet,” designed for deep reasoning, agentic workflows, advanced coding, and long-context multimodal understanding (text, images, audio, video, code and tool integrations).

Key features

• Modalities: Text, image, video, audio, PDFs (and structured tool outputs).
• Agentic/tooling: Built-in function calling, search-as-tool, code execution, URL context, and support for orchestrating multi-step agents. Thought-signature mechanism preserves multi-step reasoning across calls.
• Coding & “vibe coding”: Optimized for front-end generation, interactive UI generation, and agentic coding (it tops relevant leaderboards reported by Google). It’s marketed as their strongest “vibe-coding” model yet.
• New developer controls: thinking_level (low|high) to trade off cost/latency vs reasoning depth, and media_resolution controls multimodal fidelity per image or video frame. These help balance performance, latency, and cost.

Benchmark performance

• The Gemini3Pro achieved first place in LMARE with a score of 1501, surpassing Grok-4.1-thinking’s 1484 points and also leading Claude Sonnet 4.5 and Opus 4.1.
• It also achieved first place in the WebDevArena programming arena with a score of 1487.
• In Humanity’s Last Exam academic reasoning, it achieved 37.5% (without tools); in GPQA Diamond science, 91.9%; and in the MathArena Apex math competition, 23.4%, setting a new record.
• In multimodal capabilities, the MMMU-Pro achieved 81%; and in Video-MMMU video comprehension, 87.6%.

Technical details & architecture

• “Thinking level” parameter: Gemini 3 exposes a thinking_level control that lets developers trade off depth of internal reasoning vs latency/cost. The model treats thinking_level as a relative allowance for internal multi-step reasoning rather than a strict token guarantee. Default is typically high for Pro. This is an explicit new control for developers to tune multi-step planning and chain-of-thought depth.
• Structured outputs & tools: The model supports structured JSON outputs and can be combined with built-in tools (Google Search grounding, URL context, code execution, etc.). Some structured-output+tools features are preview-only for gemini-3-pro-preview.
• Multimodal and agentic integrations: Gemini 3 Pro is explicitly built for agentic workflows (tooling + multiple agents over code/terminals/browser).

Limitations & known caveats

1. Not perfect factuality — hallucinations remain possible. Despite strong factuality improvements claimed by Google, grounded verification and human review are still necessary in high-stakes settings (legal, medical, financial).
2. Long-context performance varies by task. Support for a 1M input window is a hard capability, but empirical effectiveness can drop on some benchmarks at extreme lengths (observed pointwise declines at 1M on some long-context tests).
3. Cost & latency trade-offs. Large contexts and higher thinking_level settings increase compute, latency and cost; pricing tiers apply based on token volumes. Use thinking_level and chunking strategies to manage costs.
4. Safety & content filters. Google continues to apply safety policies and moderation layers; certain content and actions remain restricted or will trigger refusal modes.

How Gemini 3 Pro Preview compares to other top models

High level comparison (preview → qualitative):

Against Gemini 2.5 Pro: Step-change improvements in reasoning, agentic tool use, and multimodal integration; much larger context handling and better long-form understanding. DeepMind shows consistent gains across academic reasoning, coding, and multimodal tasks.

Against GPT-5.1 and Claude Sonnet 4.5 (as reported): On Google/DeepMind’s benchmark slate Gemini 3 Pro is presented as leading on several agentic, multimodal, and long-context metrics (see Terminal-Bench, MMMU-Pro, AIME). Comparative results vary by task.

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Typical and high-value use cases

• Large document / book summarization & Q&A: long context support makes it attractive for legal, research, and compliance teams.
• Code understanding & generation at repo scale: integration with coding toolchains and improved reasoning helps large codebase refactors and automated code review workflows.
• Multimodal product assistants: image + text + audio workflows (customer support that ingests screenshots, call snippets, and documents).
• Media generation & editing (photo → video): earlier Gemini family features now include Veo / Flow-style photo→video capabilities; preview suggests deeper multimedia generation for prototypes and media workflows.