Gemini 3.6 Flash
Capability Radar & Benchmark Scores
Multi-Axis Capability Radar
Comparing Gemini 3.6 Flash against Frontier Top 10 & Open-Source average
Where Gemini 3.6 Flash Leads
All Verified Benchmark Evaluations
Direct Pass@1 and resolution rates across standardized evaluations.
Run Benchmarks on Dedicated AI Clusters
Zero-cold-start inference endpoints with dedicated GPU instances.
Executive Analysis & Verdict
Gemini 3.6 Flash by Google is a high-efficiency proprietary model scoring 48.5 overall with 1538 Code Arena Elo and 91.8% on GDM-MRCR v2.
Key Strengths & Highlights
- •Strong long-context needle retrieval with 91.8% on GDM-MRCR v2 and 84.2% on LVBench
- •1538 Code Arena Elo with 78.0% on Terminal Bench 2.1
- •Generation throughput of 165c/s with cost-effective $0.75/$3.75 pricing
Limitations & Considerations
- •Subject to standard provider rate limits and API quota
Architectural Overview & Capability Profile
Gemini 3.6 Flash is developed by Google DeepMind as an advanced foundation model engineered for production enterprise applications, reasoning workflows, and autonomous problem-solving. On the comprehensive RankLLMs composite evaluation index, Gemini 3.6 Flash registers an overall benchmark rating of 48.5 / 100.
Operating under a Proprietary license structure, Gemini 3.6 Flash is served via high-throughput managed API endpoints designed for low-latency production queries.
Benchmark Performance & Real-World Utility
In standardized laboratory and real-world evaluation suites, Gemini 3.6 Flash demonstrates calibrated proficiency across key performance vectors:
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SWE-bench Verified Software Engineering (66.8%): Measures multi-file GitHub issue resolution. The model demonstrates practical capability in localizing source code bugs, synthesizing functional patches, and passing rigorous integration test matrices. For evaluation methodology, consult our SWE-bench Pro guide.
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GPQA Diamond Scientific Reasoning (52%): Assesses graduate-level domain problem solving across physics, chemistry, and biology, minimizing hallucinations in multi-step deductive arguments. See our Humanity’s Last Exam benchmark analysis.
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MATH-500 Competition Mathematics (52%): Evaluates formal mathematical problem solving and symbolic derivation, providing foundational logic for algorithmic computing.
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Terminal-Bench Execution (78%): Reflects proficiency in interactive command-line environments, shell tool use, and automated DevOps scripting.
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Code Arena Preference Rating (1538 Elo): Validates human developer satisfaction in blind side-by-side coding evaluations against contemporary alternatives.
Inference Latency & Cost Architecture
- Token Context Window: 1.0M tokens, allowing large documents, multi-file codebases, and long conversation memories to be processed in a single context window.
- Inference Speed: Reference throughput clocks at approximately 165 tokens/second, enabling responsive streaming in developer tools and interactive interfaces.
- Commercial Pricing: API consumption is billed at $0.75 per 1M input tokens and $3.75 per 1M output tokens, offering predictable scaling for high-volume enterprise traffic.
Strengths, Limitations & Ideal Workflows
Primary Strengths
- Strong long-context needle retrieval with 91.8% on GDM-MRCR v2 and 84.2% on LVBench
- 1538 Code Arena Elo with 78.0% on Terminal Bench 2.1
- Generation throughput of 165c/s with cost-effective $0.75/$3.75 pricing
Known Considerations
- Subject to standard provider rate limits and API quota
Recommended Use Cases
High-volume multimodal processing, long-video comprehension, and real-time interactive agents.
Related Comparisons & Leaderboard Context
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Frequently Asked Questions About Gemini 3.6 Flash
What is Gemini 3.6 Flash and who developed it?↓
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Related Research & Guides
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