Google•Rank #23

Gemini 3.7 Flash

Proprietarygeneralreasoningcodingagentfast
Leaderboard Score
54.2
Scale: 0 - 100
Throughput Speed
169 tps
Context Window
1M
API Pricing / 1M
$0.75
Parameter Scale
Proprietary
Verified AI Radar & Benchmarks•8 Evaluated Metrics

Capability Radar & Benchmark Scores

Multi-Axis Capability Radar

Comparing Gemini 3.7 Flash against Frontier Top 10 & Open-Source average

0 - 100 Scale
Gemini 3.7 Flash
Frontier Top 10 Avg
Open-Source Avg
Performance Vectors

Where Gemini 3.7 Flash Leads

Coding & SWETop 10: 79.1% • OS: 47.6%
78.6%-0.5% vs Top 10
Reasoning & MathTop 10: 74.6% • OS: 46.6%
61.5%-13.1% vs Top 10
Agent & Tool AutomationTop 10: 71.2% • OS: 31.9%
55.0%-16.2% vs Top 10
Terminal & CLI ExecutionTop 10: 80.4% • OS: 47.6%
85.8%+5.4% vs Top 10
MMLU & Knowledge QATop 10: 50% • OS: 86.7%
67.8%+17.8% vs Top 10
Cyber & Security ExploitsTop 10: 87.8% • OS: 80.6%
51.5%-36.3% vs Top 10

All Verified Benchmark Evaluations

Direct Pass@1 and resolution rates across standardized evaluations.

OverallCodingReasoningAgenticTerminal
Featured Compute Platform

Run Benchmarks on Dedicated AI Clusters

Zero-cold-start inference endpoints with dedicated GPU instances.

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Executive Analysis & Verdict

Gemini 3.7 Flash by Google is a high-speed frontier model scoring 54.2 overall with #1 rankings on FrontierCode (43.6%), Code Arena (1588 Elo), and 97.0% on GDM-MRCR.

Key Strengths & Highlights

  • •Top rank on Code Arena (1588 Elo) and FrontierCode 1.1 Main (43.6%)
  • •Top Multimodal & Long Context with 97.0% on GDM-MRCR v2 and 85.4% on LVBench
  • •Top rank on Harvey LAB-AA (90.7%), HLE-Verified (53.6%), and AutomationBench (30.4%)

Limitations & Considerations

  • •Subject to standard provider rate limits and API quota
Recommended Workflows
Interactive developer workflows, low-latency streaming, complex multimodal reasoning, and legal document analysis

Architectural Overview & Capability Profile

Gemini 3.7 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 3.7 Flash registers an overall benchmark rating of 54.2 / 100.

Operating under a Proprietary license structure, Gemini 3.7 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 3.7 Flash demonstrates calibrated proficiency across key performance vectors:

  • SWE-bench Verified Software Engineering (78.6%): 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.

  • GPQA Diamond Scientific Reasoning (61.5%): 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.

  • MATH-500 Competition Mathematics (56%): Evaluates formal mathematical problem solving and symbolic derivation, providing foundational logic for algorithmic computing.

  • Terminal-Bench Execution (85.8%): Reflects proficiency in interactive command-line environments, shell tool use, and automated DevOps scripting.

  • Code Arena Preference Rating (1588 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 169 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

  • Top rank on Code Arena (1588 Elo) and FrontierCode 1.1 Main (43.6%)
  • Top Multimodal & Long Context with 97.0% on GDM-MRCR v2 and 85.4% on LVBench
  • Top rank on Harvey LAB-AA (90.7%), HLE-Verified (53.6%), and AutomationBench (30.4%)

Known Considerations

  • Subject to standard provider rate limits and API quota

Interactive developer workflows, low-latency streaming, complex multimodal reasoning, and legal document analysis.

Frequently Asked Questions About Gemini 3.7 Flash

What is Gemini 3.7 Flash and who developed it?↓
Gemini 3.7 Flash is a proprietary large language model developed by Google. It ranks #23 on the RankLLMs composite leaderboard, ahead of 75% of the 85 models we track, with an overall score of 54.2 / 100.
Can Gemini 3.7 Flash handle very long documents and codebases?↓
Gemini 3.7 Flash supports a 1.0M-token input context window and generates about 169 tps tokens per second in our reference measurements and which is enough to hold entire repositories, long transcripts, or multi-document research sets in a single session..
How much does Gemini 3.7 Flash cost per million tokens?↓
API pricing for Gemini 3.7 Flash is $0.75 per 1M tokens - cheaper than 55% of priced models we track. Confirm the current rate on the provider's official pricing page before budgeting.
What is Gemini 3.7 Flash best at?↓
Gemini 3.7 Flash is strongest in Top rank on Code Arena (1588 Elo) and FrontierCode 1.1 Main (43.6%); Top Multimodal & Long Context with 97.0% on GDM-MRCR v2 and 85.4% on LVBench; Top rank on Harvey LAB-AA (90.7%), HLE-Verified (53.6%), and AutomationBench (30.4%). Full pillar-by-pillar numbers are in the benchmark tables above.
What should you watch out for with Gemini 3.7 Flash?↓
Known trade-offs include: Subject to standard provider rate limits and API quota. Weigh these against the strengths above for your specific workload.