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고급 다중 모달리티 능력 100만 토큰 장문 컨텍스트 Google DeepMind 개발

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소스 코드 비공개 라이선스 프리뷰 버전으로서의 불안정성 상세 성능 지표 부족

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대규모 문서 분석 및 요약 복잡한 다중 모달리티 처리 장문 컨텍스트 데이터 활용

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Google Deep Mind독점

Gemini 3.1 Pro Preview

Name: Gemini 3.1 Pro Preview
Price: 3.6 USD
Author: Google Deep Mind

Gemini 3.1 Pro Preview는 Google DeepMind가 개발한 다중 모달리티 기반 모델입니다. 100만 토큰의 광대한 컨텍스트 윈도우를 특징으로 합니다.

파라미터

Undisclosed

컨텍스트

라이선스

Proprietary

출시일

2026-02-20

API 가격

입력 가격 (1M 토큰당)

$3.6

출력 가격 (1M 토큰당)

$21.6

과금 모드: standard

강점

・고급 다중 모달리티 능력
・100만 토큰 장문 컨텍스트
・Google DeepMind 개발

약점

・소스 코드 비공개 라이선스
・프리뷰 버전으로서의 불안정성
・상세 성능 지표 부족

활용 사례

・대규모 문서 분석 및 요약
・복잡한 다중 모달리티 처리
・장문 컨텍스트 데이터 활용

심층 분석

Arena Elo

~1493

#3 overall (April 2026), trailing Claude Opus 4.6 (1504)

SWE-Bench Verified

80.6%

vs Claude Opus 4.6: 80.8% (0.2pp gap)

GPQA Diamond

94.3%

vs GPT-5.4: 92.0% (current leader)

ARC-AGI-2

77.1%

2.5x improvement over Gemini 3 Pro (31.1%)

Input Price

$2/1M

for ≤200k context, cheapest frontier model

Context Window

1M tokens

64K output, supports entire codebases

강점

・Leads reasoning and multimodal benchmarks at a fraction of competitor costs (64% cheaper than Claude Opus 4.6 max reasoning).
・Massive 1M token context window enables whole codebase and long-document analysis in single requests.
・Significant agentic capability improvements with BrowseComp +45% and dedicated customtools endpoint for tool-heavy workflows.

약점

・Still trails Claude Opus 4.6 in pure coding accuracy (SWE-Bench 80.6% vs 80.8%).
・Preview status means no production SLA guarantees and potential API behavior changes before GA.
・Higher latency than some competitors and more confident when wrong (calibration error 51 vs GPT-5.4's 38 on Humanity's Last Exam).

경쟁사 비교

Model	Arena	SWE	GPQA	Price
Claude Opus 4.6	~1504	80.8%	89.6%	$5/$25 per 1M tokens
GPT-5.4	~1484	~80%	92.0%	$2.50/$15 per 1M tokens
DeepSeek R2	~1441	62.1%	82.4%	$0.55/$2.19 per 1M tokens

개요

Gemini 3.1 Pro Preview is Google DeepMind's latest flagship multimodal reasoning model, launched in February 2026 as an iterative upgrade to the Gemini 3 series. It represents a significant step forward in core reasoning capabilities while maintaining aggressive pricing, positioning Google as the leader in cost-efficient frontier AI. The model features a massive 1 million token context window, native multimodal understanding across text, images, audio, video, and code, and is specifically optimized for complex agentic workflows, advanced coding, and long-context analysis. The model achieves state-of-the-art or near-state-of-the-art performance across multiple benchmarks, leading on GPQA Diamond (94.3%), ARC-AGI-2 (77.1%), and Humanity's Last Exam (44.4% without tools). It ties GPT-5.4 on the Artificial Analysis Intelligence Index with a score of 57 while costing less than half to run the full evaluation suite. Key improvements over its predecessor include a 2.5x improvement on abstract reasoning (ARC-AGI-2), a 45% boost in search capabilities (BrowseComp), and a 20% improvement in terminal coding (Terminal-Bench 2.0). Currently in preview status, Gemini 3.1 Pro is available across Google's ecosystem including Gemini API, Vertex AI, Google AI Studio, and consumer products like the Gemini app and NotebookLM. Its pricing structure makes it the most cost-effective frontier model available, with input costs at $2 per million tokens for prompts under 200k context—significantly cheaper than competitors like Claude Opus 4.6 ($5/$25) while delivering comparable or superior performance on most benchmarks. The model represents Google's strategy of delivering frontier capabilities at accessible price points, though the preview status means developers should validate performance before production deployment.

벤치마크 및 성능

Gemini 3.1 Pro demonstrates strong performance across reasoning, coding, and multimodal benchmarks. Below are key benchmark comparisons from official and independent evaluations: | Benchmark | Gemini 3.1 Pro | Claude Opus 4.6 | GPT-5.4 | Notes | |-----------|----------------|-----------------|---------|-------| | ARC-AGI-2 (Abstract Reasoning) | 77.1% | 68.8% | Not published | Leads by 8.3pp over Claude | | GPQA Diamond (Science Reasoning) | 94.3% | 89.6% (Thinking) | 92.0% | Highest public score, 2.3pp ahead of GPT-5.4 | | SWE-Bench Verified (Coding) | 80.6% | 80.8% | ~80% | Virtual tie with Claude, slight leader | | Humanity's Last Exam (Reasoning) | 44.4% (no tools) | 34.44% (Thinking Max) | 44.32% (Pro) | Leads by 9.96pp over Claude | | MMMLU (Multilingual Knowledge) | 92.6% | 91.1% | 89.6% | Strong multilingual capabilities | | Terminal-Bench 2.0 (Agentic Coding) | 68.5% | 65.4% | 54.0% | Leads by 3.1pp over Claude | | LiveCodeBench Pro (Competitive Coding) | Elo 2887 | Not published | Elo 2393 | Significant competitive coding lead | | BrowseComp (Agentic Search) | 85.9% | 84.0% | 65.8% | Leads by 1.9pp over Claude | | MMMU-Pro (Multimodal Reasoning) | 80.5% | 73.9% | 79.5% | Leads by 1.0pp over GPT-5.4 | | Artificial Analysis Intelligence Index | 57 | 53 | 57 | Tied with GPT-5.4, 4 points ahead of Claude | | LMArena Elo (Human Preference) | ~1493 (April 2026) | ~1504 | ~1484 | #3 overall, trails Claude by 11 Elo | The model shows particular strength in reasoning tasks (ARC-AGI-2, HLE) and multimodal understanding (MMMU-Pro, VideoMME 87.2%). Its biggest improvement over predecessors is in abstract reasoning (ARC-AGI-2: 31.1% → 77.1%) and search capabilities (BrowseComp: 59.2% → 85.9%).

상세 비교

## Head-to-Head with Main Competitors ### Claude Opus 4.6 - **Pricing**: Gemini 3.1 Pro ($2/$12 per 1M tokens) vs Claude Opus 4.6 ($5/$25) - Gemini is 2.5× cheaper on input and 2.08× cheaper on output. - **Context Window**: Both support 1M tokens, but Gemini maintains full capability at scale while Claude's performance degrades beyond 200k context in some tasks. - **Strengths**: Gemini leads on reasoning benchmarks (HLE, GPQA, ARC-AGI-2) and costs significantly less. Claude maintains slight edge in pure coding accuracy (SWE-Bench 80.8% vs 80.6%) and higher human preference scores (LMArena Elo 1504 vs 1493). - **Weaknesses**: Gemini's preview status means no production SLA guarantees. Claude's pricing limits cost-sensitive applications. ### GPT-5.4 - **Pricing**: Gemini ($2/$12) vs GPT-5.4 ($2.50/$15) - Gemini is 20% cheaper on both input and output. - **Context Window**: Both support 1M tokens, but Gemini shows better long-context retrieval accuracy (RULER 93.4% vs unspecified for GPT-5.4). - **Strengths**: Gemini leads on science reasoning (GPQA 94.3% vs 92.0%) and multimodal tasks. GPT-5.4 has more mature production deployment options and potentially better calibration. - **Weaknesses**: Gemini has higher calibration error (more confident when wrong). GPT-5.4 may have broader ecosystem integrations. ### DeepSeek R2 - **Pricing**: Gemini ($2/$12) vs DeepSeek R2 ($0.55/$2.19) - DeepSeek is 73% cheaper on input and 82% cheaper on output. - **Context Window**: Gemini 1M vs DeepSeek 128K - Gemini has 8× larger context window. - **Strengths**: DeepSeek leads on pure math (AIME 2025: 93.8% vs 91.2%) and is significantly cheaper. Gemini has superior multimodal capabilities (supports video/images/audio) and much larger context. - **Weaknesses**: DeepSeek is text-only and lacks multimodal understanding. Gemini is more expensive for pure math applications. ## Pricing Comparison Table | Model | Input Price (≤200k) | Output Price (≤200k) | Context Window | Cost at 10M tokens/day | |-------|-------------------|---------------------|----------------|------------------------| | Gemini 3.1 Pro | $2.00/1M | $12.00/1M | 1M tokens | ~$56/day | | Claude Opus 4.6 | $5.00/1M | $25.00/1M | 1M tokens | ~$450/day | | GPT-5.4 | $2.50/1M | $15.00/1M | 1M tokens | ~$200/day | | DeepSeek R2 | $0.55/1M | $2.19/1M | 128K tokens | ~$14/day |

커뮤니티 평가

Developers and researchers have responded positively to Gemini 3.1 Pro's performance-to-cost ratio, with many highlighting it as a game-changer for cost-sensitive applications. The Artificial Analysis community notes that "Gemini 3.1 Pro Preview is the strongest general-purpose model available as of February 2026" and "undercuts every competitor on price" while maintaining frontier capabilities. On developer forums and social media, the main discussion points include: - **Cost efficiency**: The 64% cost reduction compared to Claude Opus 4.6 at max reasoning is frequently cited as the most compelling feature, with developers calculating significant savings at scale. - **Preview concerns**: Many enterprise developers express caution about adopting preview models in production, noting the lack of SLAs and potential API changes. - **Benchmark skepticism**: Some researchers question whether benchmark leadership translates to real-world performance, particularly given the model's higher calibration error on Humanity's Last Exam. - **Migration ease**: Former Gemini 3 Pro users report smooth transitions, with the main change being simply updating the model identifier from `gemini-3-pro-preview` to `gemini-3-1-pro-preview`. The JetBrains AI Director's feedback about 15% output efficiency gains has been particularly influential in the developer community, with many teams reporting similar improvements in token usage. Overall sentiment is that Gemini 3.1 Pro represents a meaningful step forward in making frontier AI capabilities more accessible, though production adoption will likely wait for the general availability release.

활용 사례

## 1. Agentic Coding Workflows **Example**: Automated code review and PR generation. Gemini 3.1 Pro's combination of SWE-Bench Verified performance (80.6%) and the dedicated `customtools` endpoint makes it ideal for autonomous coding agents that need to navigate repositories, read files, and generate patches. **Why choose over alternatives**: At $2/$12 per million tokens, it's 2.5× cheaper than Claude Opus 4.6 while only trailing by 0.2pp on coding accuracy. For budget-constrained teams running high-volume coding agents, this cost difference is decisive. ## 2. Long-Context Document Analysis **Example**: Legal contract analysis across 800k-token document sets. The 1M token context window eliminates the need for chunking and retrieval engineering required with Claude's 200k limit. **Why choose over alternatives**: While Claude might have slightly better accuracy per chunk, Gemini's native long-context capability reduces system complexity and avoids information loss from document splitting. ## 3. Multimodal Research Synthesis **Example**: Analyzing video tutorials, research papers, and code repositories simultaneously. Gemini's native video understanding (VideoMME 87.2%) and 1M context window allow processing entire research workflows in a single session. **Why choose over alternatives**: GPT-5.4 and Claude lack equivalent video understanding capabilities. DeepSeek R2 is text-only. Gemini's multimodal integration is uniquely comprehensive at this price point. ## 4. Cost-Sensitive Enterprise Deployment **Example**: Customer support automation handling millions of queries monthly. The $2/$12 pricing enables running frontier AI capabilities at 64% lower cost than Claude Opus 4.6. **Why choose over alternatives**: For applications where reasoning quality is 'good enough' across the board, Gemini's price-performance ratio is unmatched. The savings can fund significant additional infrastructure or features.

최신 뉴스

## February 2026 Launch - **February 19, 2026**: Gemini 3.1 Pro Preview officially launched with immediate availability via Gemini API, Vertex AI, Google AI Studio, and consumer products (Gemini app, NotebookLM). - **Preview Status**: Google explicitly positions this as a preview release to "validate updates and refine agentic workflows" before general availability. Production SLAs are not yet committed. - **Pricing Structure**: Maintains same pricing as Gemini 3 Pro ($2/$12 per 1M tokens for ≤200k context, doubling above 200k). Batch pricing available at $1/$6. ## Key Technical Updates - **New Capabilities**: Added Google Maps grounding support, YouTube URL direct pass-through, and dedicated `gemini-3-1-pro-preview-customtools` endpoint for tool-heavy agents. - **Performance Improvements**: Significant gains in reasoning (ARC-AGI-2: 31.1% → 77.1%), search (BrowseComp: 59.2% → 85.9%), and coding (Terminal-Bench 2.0: 56.9% → 68.5%). - **Safety Profile**: Maintains below critical capability thresholds in Frontier Safety Framework across CBRN, cyber, harmful manipulation, ML R&D, and misalignment domains. ## Ecosystem Integration - **Deprecation of Gemini 3 Pro**: Google has deprecated `gemini-3-pro-preview` (shut down March 9, 2026) and recommends migration to 3.1 Pro. - **Consumer Rollout**: Rolling out with higher limits for Google AI Pro and Ultra subscription tiers in the Gemini app and NotebookLM. - **Developer Tools**: Available through Gemini CLI, Google Antigravity (agentic development platform), and Android Studio. ## Upcoming Developments - General availability expected within 6-12 weeks based on Google's historical preview timelines. - Potential pricing adjustments at GA release, as Google has adjusted pricing between major model generations in the past. - Further improvements to structured output reliability and calibration accuracy anticipated before production release.

The model achieves state-of-the-art or near-state-of-the-art performance across multiple benchmarks, leading on GPQA Diamond (94.3%), ARC-AGI-2 (77.1%), and Humanity's Last Exam (44.4% without tools). It ties GPT-5.4 on the Artificial Analysis Intelligence Index with a score of 57 while costing less than half to run the full evaluation suite. Key improvements over its predecessor include a 2.5x improvement on abstract reasoning (ARC-AGI-2), a 45% boost in search capabilities (BrowseComp), and a 20% improvement in terminal coding (Terminal-Bench 2.0).

Currently in preview status, Gemini 3.1 Pro is available across Google's ecosystem including Gemini API, Vertex AI, Google AI Studio, and consumer products like the Gemini app and NotebookLM. Its pricing structure makes it the most cost-effective frontier model available, with input costs at $2 per million tokens for prompts under 200k context—significantly cheaper than competitors like Claude Opus 4.6 ($5/$25) while delivering comparable or superior performance on most benchmarks. The model represents Google's strategy of delivering frontier capabilities at accessible price points, though the preview status means developers should validate performance before production deployment.

출처

분석 생성일: 2026-05-23