이 모델의 강점은 무엇인가요?

Advanced multimodal processing Developed by Google DeepMind Latest foundation model design

이 모델의 약점은 무엇인가요?

Restrictions from non-open-source status Closed license No detailed design from external sources

어떤 용도에 가장 적합한가요?

Multimodal data analysis Advanced content generation Complex information integration

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Google Deep Mind독점

Gemini 3.5 Pro

Name: Gemini 3.5 Pro
Author: Google Deep Mind

Gemini 3.5 Pro is a multimodal foundation model developed by Google DeepMind. Featuring advanced multimodal processing capabilities, it is provided under a closed license.

파라미터

Undisclosed

컨텍스트

라이선스

Proprietary

출시일

2026-06-19

API 가격

이 모델의 API 가격 정보는 현재 공개되지 않았습니다

강점

・Advanced multimodal processing
・Developed by Google DeepMind
・Latest foundation model design

약점

・Restrictions from non-open-source status
・Closed license
・No detailed design from external sources

활용 사례

・Multimodal data analysis
・Advanced content generation
・Complex information integration

심층 분석

Arena Elo

TBD

Not yet released; expected to be higher than Gemini 3.5 Flash and compete with top flagships like Claude Opus 4.6 (1,504)

SWE-Bench Verified

TBD

Not yet released; expected to surpass Gemini 3.5 Flash (55.1% on SWE-Bench Pro) and potentially match/exceed Claude Opus 4.7 (87.6%)

Input Price

Expected $2.00-$5.00/1M

Based on Gemini 3.1 Pro pricing ($2.00/1M) and premium positioning

Output Price

Expected $12.00-$25.00/1M

Based on Gemini 3.1 Pro pricing ($12.00/1M) and premium positioning

Context Window

Expected 1M-2M tokens

Gemini 3.1 Pro had 2M; Gemini 3.5 Flash has 1M

Release Date

June 2026

Confirmed by Google; currently in internal testing

강점

・Expected to deliver frontier-level reasoning and pure problem-solving capabilities surpassing Gemini 3.5 Flash
・Likely to maintain advanced multimodal processing (text, image, audio, video) with potential improvements in understanding
・Anticipated to have a larger context window (up to 2M tokens) for handling extremely long documents and complex tasks

약점

・Not yet publicly available, with no official benchmarks, pricing, or detailed specifications released
・Expected to be more expensive than Gemini 3.5 Flash, potentially placing it in the premium flagship tier alongside Claude Opus and GPT-5.5
・Likely slower inference speed than Gemini 3.5 Flash, following the trade-off pattern between 'Pro' and 'Flash' model tiers

경쟁사 비교

Model	Arena	SWE	GPQA	Price
Gemini 3.5 Flash	Not yet ranked (1,504 for Claude Opus 4.6 as reference)	55.1% (SWE-Bench Pro)	~90.4%	$1.50/$9.00
Claude Opus 4.7	1,504 (Opus 4.6)	87.6%	91.3%	$5.00/$25.00
GPT-5.5	~1,769 Elo (GDPval-AA)	88.7%	~92.4%	$5.00/$30.00

개요

Gemini 3.5 Pro is Google DeepMind's upcoming flagship model in the Gemini 3.5 series, announced at Google I/O 2026 for release in June 2026. Currently in internal testing, it is positioned as the capability-optimized counterpart to the recently released Gemini 3.5 Flash. While Flash is designed for speed, cost-efficiency, and agentic tasks, 3.5 Pro is expected to push the frontier on pure reasoning, long-context understanding, and complex problem-solving—areas where Flash shows clear limitations compared to prior Pro models and competitor flagships. It represents Google's effort to close any remaining gaps with models like Claude Opus 4.7 and GPT-5.5 on the most challenging intellectual tasks, potentially combining the agentic strengths of Flash with deeper reasoning capabilities. As a multimodal foundation model, Gemini 3.5 Pro will likely feature a 1M-2M token context window, advanced function calling, and improved interpretability tools. Its development under the Frontier Safety Framework indicates strengthened safeguards against misuse. The release will complete Google's 3.5 model family, which began with Flash targeting the agent era, and may force competitive responses from Anthropic and OpenAI. For developers, 3.5 Pro could become the default for research, complex analysis, and tasks requiring absolute accuracy, while Flash remains optimal for high-volume, speed-sensitive agent workflows.

벤치마크 및 성능

Detailed benchmark data for Gemini 3.5 Pro is not yet available, as the model is still in internal testing. Based on patterns from previous generations (e.g., Gemini 3.1 Pro outperforming Gemini 3 Flash on reasoning tasks) and official statements, we can expect significant improvements over Gemini 3.5 Flash in several key areas: **Expected Improvements Over Gemini 3.5 Flash:** | Benchmark | Gemini 3.5 Flash (Current) | Expected Gemini 3.5 Pro | Rationale | |-----------|----------------------------|-------------------------|----------| | ARC-AGI-2 | 72.1% | >77.1% (likely higher) | Flash showed a 5-point regression from Gemini 3.1 Pro; Pro should recover and push further | | Humanity's Last Exam | 40.2% | >44.4% (likely higher) | Flash trailed Gemini 3.1 Pro by 4.2 points; Pro should lead | | MRCR v2 (128k) | 77.3% | >84.9% (likely higher) | Flash showed a 7.6-point regression from Gemini 3.1 Pro; Pro should restore and advance | | Long-context retrieval (1M) | 26.6% | Potentially higher | Pro may improve retrieval across the full 1M-2M window | | SWE-Bench Verified | 55.1% (SWE-Bench Pro) | Expected to approach/exceed 87.6% | To compete with Claude Opus 4.7 and GPT-5.5 | **Areas Where Flash Already Leads (Pro may match or slightly improve):** | Benchmark | Gemini 3.5 Flash | Notes for Pro | |-----------|------------------|-------------| | MCP Atlas (tool use) | 83.6% | Pro may match or slightly exceed; agentic strength likely preserved | | Terminal-Bench 2.1 (coding) | 76.2% | Pro could match or improve on complex coding tasks | | MMMU-Pro (multimodal) | 84.0% | Pro may maintain or advance multimodal understanding | **Comparisons with Competitor Flagships (Hypothetical):** - **vs Claude Opus 4.7:** Pro should aim to match or exceed SWE-Bench (87.6%) and GPQA Diamond (91.3%) while potentially offering faster inference or better pricing. - **vs GPT-5.5:** Pro should target ARC-AGI-2 (85.0%) and SWE-Bench (88.7%) to compete with OpenAI's reasoning flagship. - **vs Gemini 3.1 Pro:** Pro should surpass its predecessor across all benchmarks, continuing the generational improvement cycle. *Note: All performance projections are based on historical patterns and official communications. Actual benchmarks will be released by Google upon Gemini 3.5 Pro's launch.*

상세 비교

**Gemini 3.5 Pro vs. Gemini 3.5 Flash:** - **Positioning:** Pro is capability-optimized (reasoning, accuracy); Flash is speed/cost-optimized (agentic tasks, high volume). - **Expected Performance:** Pro should significantly outperform Flash on ARC-AGI-2, Humanity's Last Exam, long-context retrieval, and SWE-Bench; Flash leads on speed and cost-efficiency for tool use. - **Pricing:** Pro will likely be 2-3× more expensive on input/output tokens than Flash ($1.50/$9.00). - **Context Window:** Both may offer 1M tokens, but Pro could provide 2M. - **Use Cases:** Flash for agents, RAG, and high-throughput workflows; Pro for research, complex analysis, and tasks where accuracy is critical. **Gemini 3.5 Pro vs. Claude Opus 4.7:** - **Pricing:** Expected similar to Opus ($5/$25) or lower; Google historically prices competitively. - **Context Window:** Both around 1M-2M tokens. - **Strengths:** Opus excels in software engineering (SWE-Bench 87.6%) and creative writing; Pro expected to lead on multimodal understanding and potentially agentic benchmarks. - **Speed:** Pro likely faster than Opus (~25 tok/s) but slower than Flash (~289 tok/s). **Gemini 3.5 Pro vs. GPT-5.5:** - **Pricing:** Expected similar to GPT-5.5 ($5/$30) or lower. - **Strengths:** GPT-5.5 leads on ARC-AGI-2 (85.0%) and SWE-Bench (88.7%); Pro should compete closely while offering superior multimodal capabilities and potentially better agentic performance. - **Ecosystem:** Both integrated into broad ecosystems (Google Workspace vs. OpenAI ChatGPT/Enterprise).

커뮤니티 평가

The developer and research community is eagerly anticipating Gemini 3.5 Pro, with reactions shaped by the recent Gemini 3.5 Flash release: **Anticipation and Speculation:** - Developers are closely watching whether Pro will close the reasoning gaps Flash showed against competitor flagships, particularly on ARC-AGI-2 and SWE-Bench. - The delay from May to June 2026 has led to speculation that Google is optimizing for benchmark leadership, especially after Flash's strong showing on agentic tasks. - On forums like Hacker News and Twitter/X, discussions focus on the potential for Pro to combine Flash's agentic strengths with frontier reasoning, possibly becoming the "first truly unified" model. **Adoption Patterns:** - Many production teams have already deployed Gemini 3.5 Flash for agent workloads and are waiting for Pro to handle edge cases requiring deeper reasoning. - The AI community notes that if Pro matches or exceeds Claude Opus 4.7 and GPT-5.5 on key benchmarks while offering competitive pricing, it could shift significant developer mindshare toward Google's ecosystem. - Enterprise interest is high, especially from industries like finance and healthcare where both reasoning accuracy and multimodal capabilities are critical. **Concerns and Considerations:** - Some developers express caution about migrating from established providers (Anthropic, OpenAI) until Pro's benchmarks are independently verified. - There's debate on whether Pro will maintain Flash's speed advantages or revert to slower, more expensive inference patterns typical of flagship models. - The community awaits details on Pro's safety and interpretability features, given Google's emphasis on the Frontier Safety Framework. Overall, sentiment is cautiously optimistic, with expectations that Gemini 3.5 Pro will establish Google as a leader across the full spectrum of AI capabilities.

활용 사례

**1. Complex Research and Analysis:** *Example:* A research team analyzing thousands of scientific papers to identify novel drug interactions. *Why Pro:* Requires deep reasoning across long documents, synthesis of contradictory findings, and high accuracy—areas where Pro is expected to excel over Flash's agentic focus. **2. High-Stakes Software Engineering:** *Example:* Refactoring a mission-critical legacy codebase with subtle interdependencies. *Why Pro:* Needs strong SWE-Bench performance (expected >87.6%) and long-context understanding to grasp full codebase implications, surpassing Flash's 55.1% on SWE-Bench Pro. **3. Multimodal Reasoning in Specialized Domains:** *Example:* Interpreting medical scans alongside clinical notes for diagnostic support. *Why Pro:* Requires both frontier multimodal understanding (MMMU-Pro leadership) and deep domain reasoning, combining Flash's vision strengths with Pro's reasoning depth. **4. Long-Document Compliance and Legal Review:** *Example:* Analyzing 500-page regulatory filings for risk disclosures and compliance gaps. *Why Pro:* Demands exceptional long-context retrieval (expected to overcome Flash's MRCR v2 regression) and precise reasoning over dense text. **When to Choose Pro Over Alternatives:** - When accuracy is non-negotiable and errors carry high cost (legal, medical, financial analysis). - When tasks require both deep reasoning and advanced multimodal processing. - When working with extremely long documents (>500k tokens) that require holistic understanding. - When willing to trade speed and cost for maximum capability on the hardest intellectual tasks.