Gemini 3 is the latest series of multi-modal AI models launched by Google. Gemini 3.1 Pro has achieved major improvements in long context understanding and multi-modal reasoning. LMArena 1501 Elo has reached the top, supporting multi-modal input of text, images, videos and deep thinking mode.

Gemini 3: A new generation of multi-modal understanding and reasoning AI model launched by Google

Core parameters and statistics

Project	Details
Product Name	Gemini 3
Product type	Basic large model / API infrastructure
Delivery Formats	Web (Google AI Studio, Gemini App), API (Gemini API), Enterprise (Vertex AI)
Developer	Google DeepMind
Place of Residence	United States (US)
Supported languages	Multi-language, including Chinese and English
Support Platform	Web, API

One sentence: Gemini 3 is not an independent chat application, but the general name of Google's multi-modal large model series, covering the complete capability gradient from lightweight Flash to deep reasoning Deep Think, and is delivered in two forms: API and end products.

The Gemini 3 series adopts a hierarchical model strategy - 3.5 Flash focuses on cutting-edge performance and low latency, 3.1 Pro is oriented towards complex reasoning tasks, 3.1 Deep Think specializes in scientific research and engineering problems, and 3.1 Flash-Lite focuses on high throughput and low-cost scenarios. This layered architecture allows developers to choose what they need in the "power-cost-speed" triangle instead of being forced to pay for capabilities they don't need.

User and market recognition

• LMArena Top: Gemini 3 Pro topped the LMArena Leaderboard with 1501 Elo points. It is currently one of the most comprehensive publicly testable models.
• Developer Ecosystem Coverage: Connected to Google AI Studio, Vertex AI, Gemini CLI and Google Antigravity platforms, and available in third-party tool chains (Cursor, GitHub, JetBrains, Manus, Replit).
• Enterprise-level adoption: Through Vertex AI services, enterprise customers have used Gemini 3 series models in production environments, and are used in code generation, customer service, data analysis and other scenarios.

Cost advantage

Hierarchy	Cost Model	Description
C-side free	Gemini App basic version free	Supports basic interactions such as text, images, and voice, including deep thinking mode (limited to Google AI Ultra subscribers)
API pay-as-you-go	Hierarchical Token pricing	Within 200k tokens: input $2.00/M, output $12.00/M; more than 200k tokens: input $4.00/M, output $18.00/M
Enterprise Solution	Vertex AI Pay-As-You-Go/Reservation	Supports private deployment, compliance authentication RBAC permission isolation

• C-side: Google AI Ultra subscription (approximately $19.99/month) unlocks deep thinking mode and a larger context window.
• Developer: The input cost of the API within 200k tokens is $2.00/M tokens, which is lower than the average level compared to the same level of inference models.
• Enterprise: Vertex AI provides reserved throughput, regional data residency and compliance support, suitable for scenarios that require data sovereignty.

Main functions

• Multi-modal understanding: supports multiple inputs such as text, images, videos, etc., reaching 81% in MMMU-Pro and 87.6% in Video-MMMU, and can parse complex charts and dynamic video streams.
• Deep Think Mode: Significantly improves accuracy on complex questions requiring multi-step reasoning, Humanity's Last Exam score 41.0% (Deep Think) vs. 37.5% (Pro).
• Agent capabilities: supports long-term task planning and tool invocation, topped Vending-Bench 2, and scored 54.2% in Terminal-Bench 2.0.
• Code Generation and Development: WebDev Arena reached the top with 1487 Elo, supports zero sample generation of complex Web UI, and SWE-bench Verified significantly surpasses the previous generation.
• SECURITY & RELIABILITY: Comprehensive security assessment to reduce sycophantic behavior and increase resistance to instant injections with 72.1% SimpleQA Verified.

[Expert View]: The real value of Gemini 3 does not lie in the leadership of a single function point, but in the coverage of "reasoning → coding → agent → search" - the model's reasoning score in LMArena can be directly converted into the coding ability of WebDev Arena, and the coding ability has verified the reliability of its Agent tool invocation through Terminal-Bench and SWE-bench, and the three form a positive flywheel. This means that developers can use the same model to complete the complete link of "requirements analysis → code generation → debugging and deployment" without having to switch between multiple models.

Model and version evolution

Version	Type	Release Date	Key Features
Gemini 3.1 Pro	All-round flagship	~2026-04	LMArena 1501 Elo, MMMU-Pro 81%, GPQA Diamond 91.9%, significant improvements in long context and multi-modal reasoning
Gemini 3.5 Flash	Frontier General	~2026-06	Optimized for Agent and programming, reaching 76.2% in Terminal-Bench 2.1 and 83.6% in MCP Atlas
Gemini 3.1 Deep Think	Deep Reasoning	~2026-04	ARC-AGI-2 reaches 45.1%, Humanity's Last Exam 41.0%, for scientific research and engineering
Gemini 3.1 Flash-Lite	High throughput and lightweight	~2026-04	For high-concurrency and low-latency scenarios, suitable for large-scale tasks that require a balance of efficiency and intelligence

The evolution path of the Gemini 3 series is clear: starting from the main Pro model, it will gradually differentiate into speed-oriented Flash, depth-oriented Deep Think and throughput-oriented Flash-Lite, forming a complete product matrix.

Gemini 3.1 Pro: Major improvements in long context and multi-modal reasoning

Gemini 3.1 Pro is the main flagship model of the Gemini 3 series. Compared with the previous generation, it has achieved a qualitative leap in the following dimensions:

• Breakthrough in long context understanding: 3.1 Pro optimizes the information retrieval accuracy and instruction following stability in ultra-long context (more than 200K tokens) scenarios. In the MRCR long context benchmark test, the score of 3.1 Pro under the 1M tokens window is significantly improved compared to the previous generation. For users who need to process an entire code base, a novel, or a research paper in one go, this means that the model won't suffer from "forgetting the beginning" in the second half of the window.

• Multi-modal reasoning depth enhancement: 3.1 Pro achieves best-in-class performance on cross-modal reasoning tasks such as MMMU-Pro (81%) and Video-MMMU (87.6%). The key improvement lies in the closer integration of visual understanding and logical reasoning - the model can not only "see" the information in the chart, but also make causal connections with the text context, which is of outstanding value in scenarios such as business analysis of complex charts and interpretation of scientific research papers.

• Enhanced Deep Think mode: 3.1 Pro's Deep Think mode reached 41.0% in Humanity's Last Exam, and performed well on difficult scientific questions that require rigorous derivation. Deep Think mode is suitable for scenarios that require "slow thinking" such as mathematics competitions, complex code debugging, and scientific research hypothesis verification.

The product positioning of 3.1 Pro is an "all-round flagship" - it is not biased toward deep inference like Deep Think, nor is it biased toward speed like Flash. Instead, it finds a balance between capability, cost, and latency, and is suitable for the production scenarios of most professional users.

Technical advantages

• Inference efficiency under MoE architecture: The Gemini 3 series adopts a hybrid expert (MoE) architecture, which only activates some parameters for each inference to maintain high model capacity while controlling inference costs - this is the key to its API pricing being lower than that of closed-source models of the same level.
• Graded pricing matches tiered capabilities: A pricing mechanism based on context length (200k tokens is the demarcation limit), which encourages users to concentrate short context requests in low-cost segments, and pay on-demand for long context scenarios, avoiding excessive expenditures caused by "unified billing by token" in large context scenarios.
• Multi-modal native alignment: Gemini 3 is not a plug-in visual module for text models, but aligns the joint distribution of text, images, and videos from the pre-training stage. Therefore, its performance on cross-modal reasoning tasks such as Video-MMMU far exceeds that of later splicing solutions.
• Agent Link Optimization: Provides end-to-end software development automation capabilities through the Antigravity platform, integrating LLM inference, code generation, sandbox execution, manual confirmation, etc. into a pipeline.

How to use

Entrance	Applicable people	Description
Gemini App	Ordinary users	Visit gemini.google.com, use the basic version for free, subscribe to Ultra to use the deep thinking mode
Google AI Studio	Developer	Cloud IDE, supports prompt debugging, model parameter adjustment, and code generation
Gemini API	Developer	RESTful API, supports stream, json_mode, temperature and other parameters
Vertex AI	Enterprise users	GCP fully managed platform, supporting privatization and enterprise-level permission management
Gemini CLI	Developer	Command line tool, suitable for embedding CI/CD or automation scripts
Google Antigravity	Agent Developer	Visual agent development platform, supporting end-to-end application construction

Typical developer usage process (Python):

import google.generativeai as genai

genai.configure(api_key="<YOUR_API_KEY>")
model = genai.GenerativeModel("gemini-3.5-flash")

response = model.generate_content(
    "Explain the principles of quantum entanglement and illustrate it with an analogy",
    generation_config=genai.types.GenerationConfig(
        temperature=0.7,
        max_output_tokens=2048,
    )
)
print(response.text)

Curl call example:

curl -X POST "https://generativelanguage.googleapis.com/v1/models/gemini-3.5-flash:generateContent,key=<YOUR_API_KEY>" \
  -H "Content-Type: application/json" \
  -d '{
    "contents": [{"parts": [{"text": "Explain quantum entanglement"}]}],
    "generationConfig": {"temperature": 0.7, "maxOutputTokens": 2048}
  }'

Product Pricing

The Gemini 3 series adopts hierarchical Token pricing based on context length (based on Gemini 3.0 Pro):

Token range	Input price (per million tokens)	Output price (per million tokens)
≤ 200k tokens	$2.00	$12.00
> 200k tokens	$4.00	$18.00

• C-side subscription: Google AI Ultra is about $19.99/month, unlocking deep thinking mode, larger context window and search AI mode.
• API free quota: Google AI Studio provides free quota, suitable for prototype verification and low-traffic testing.
• Enterprise Pricing: Vertex AI provides pay-as-you-go and reserved throughput packages, and supports data residency and compliance certification (SOC2, GDPR). Please contact sales for specific prices.

Compared with similar inference models, the input price of Gemini 3 Pro within 200k tokens ($2.00/M) is lower than OpenAI o3 ($5.00/M) and Claude Opus ($15.00/M), making it competitive in terms of cost performance.

Application scenarios

• Learning and Education: The model integrates multi-modal information and can interpret handwritten recipes, generate interactive learning tools, analyze video content and generate training plans.
• Development and Programming: As Google's strongest programming model, WebDev Arena topped the list with 1487 Elo. Supporting zero-sample generation of complex web UIs and applications, SWE-bench Verified significantly surpasses the previous generation.
• Task Planning and Management: Agent capability supports long-term task planning, Vending-Bench 2 reaches the top, and is suitable for project management, schedule coordination and other scenarios that require coherent decision-making.
• Content Creation: Can generate creative content such as poems, stories, game codes, etc. The deep thinking mode is outstanding in the generation of long texts that require logical coherence.
• Search and Knowledge Management: Integrated into Google search AI model, providing generative UI and multi-step reasoning search experience.

Applicable people

• AI application developers: Access through Gemini API or Google AI Studio, suitable for scenarios such as Chatbot, Agent, RAG, etc. that require cutting-edge model capabilities. A layered model system allows for smooth migration from prototype to production.
• Enterprise AI Team: Vertex AI provides compliance, authority management and privatized deployment, suitable for highly regulated industries such as finance, medical, and legal. Note that the data will not be used for model training.
• Research and Engineering Staff: The performance of Deep Think mode on ARC-AGI-2 (45.1%) and Humanity's Last Exam (41.0%) is suitable for research scenarios that require deep reasoning.
• Normal Users: Use basic capabilities for free through Gemini App, no technical background required. Heavy users can subscribe to Ultra to get the deep thinking mode.

Not fitting boundaries:

• For tasks that require coherent understanding of ultra-long contexts (>1M tokens), Gemini 3’s MRCR long context score (1M 26.6%) still has significant room for improvement.
• For real-time interactions that are extremely sensitive to generation latency (such as voice dialogue), the latency of the Flash series can meet most scenarios, but the first word latency of Deep Think mode increases significantly.
• For idle environments that require fully localized deployment, Gemini 3 currently only provides cloud access through Vertex AI, with no local running option.

Summary and Outlook

Gemini 3 is Google’s culmination of multi-modal and agent capabilities. Its core advantages are: the hierarchical model system allows corresponding models to be found for different cost-capability requirements; the inference efficiency brought by the MoE architecture makes it superior to most similar closed-source models in terms of performance-cost trade-off; the complete coverage of the Agent link (inference → coding → tool call → search) enables developers to complete end-to-end development within a single model ecosystem.

The main limitations are: the stability of ultra-long context scenarios still needs to be verified; the first word delay of deep thinking mode is not friendly enough for real-time interaction scenarios; localized deployment options are missing, and enterprises with sensitive data sovereignty can only alleviate it through the compliance capabilities of Vertex AI.

Procurement/Adoption Risk Assessment: For teams that rely on the Google ecosystem (GCP, Android, Chrome), Gemini 3 is a natural extension of AI capabilities with minimal integration costs. For teams with multi-cloud or non-Google technology stacks, it is necessary to evaluate the cost of API migration and the risk of model lock-in - it is recommended to use a unified inference gateway (such as LiteLLM) as the model abstraction layer to hedge risks. In terms of pricing, the cost-effectiveness is outstanding at $2.00/M for 200k tokens, but the price doubles ($4.00/M) after 200k tokens are exceeded, and a budget needs to be reserved for long context scenarios.

Related tools: DeepSeek, ChatGPT

Version Info

• Gemini 3.1 Pro ：The main model of the Gemini 3 series has achieved significant improvements in long-context understanding and multi-modal reasoning. LMArena 1501 Elo tops the list, supporting multi-modal input and deep thinking mode. （~2026-04）
• Gemini 3.5 Flash ：Cutting-edge performance, optimized for agents and programming scenarios, achieving leading results in multiple benchmark tests. There is no official precise date yet. （~2026-06）
• Gemini 3.1 Deep Think ：Deep thinking mode for science, research and engineering, reaching 41.0% in Humanity's Last Exam, greatly enhancing reasoning ability. There is no official precise date yet. （~2026-04）