Gemini 2.0 Flash (Dec 2024)
by Google DeepMind · Released Jan 2024
Tested on 20 benchmarks · BenchGecko score 44.1. Top scores: HELM — IFEval (84.1%), MATH level 5 (82.2%), HELM — WildBench (80.0%).
Multi-language code editing from Aider. Tests editing ability across Python, JavaScript, TypeScript, Java, C++, Go, Rust, and more.
Computer-aided design evaluation. Tests understanding of CAD concepts, 3D modeling, and engineering design principles.
Unusual and adversarial machine learning challenges. Tests robustness of reasoning about edge cases in ML systems.
Stanford HELM WildBench evaluation. Tests reasoning on challenging real-world tasks.
Deceptively simple questions that humans find easy but AI models often get wrong. Tests common sense and reasoning gaps.
ARC-AGI 2, harder sequel to ARC. More complex abstract reasoning patterns that test generalization ability beyond training data.
Competition-level math from AMC, AIME, and olympiad problems. Level 5 is the hardest tier, requiring creative problem-solving.
Stanford HELM evaluation of mathematical reasoning across diverse problem types.
Mock AIME (American Invitational Mathematics Exam) problems from OTIS. Tests mathematical competition performance.
- Typetext
- ContextN/A
- ReleasedJan 2024
- LicenseProprietary
- Statusbenchmark-only
Frequently Asked Questions
Key facts · as of 2026-06-22
- Gemini 2.0 Flash (Dec 2024) by Google DeepMind. BenchGecko score 44.1, rank 178 of 312 scored models (normalized average of public benchmark scores).
- List price n/a input · n/a output per 1M tokens (as of 2026-06-22).
How to cite · data as of 2026-06-22
Gemini 2.0 Flash (Dec 2024) · benchmarks, pricing and providers. BenchGecko, data as of 2026-06-22. https://benchgecko.ai/model/gemini-2-0-flash-dec-2024
Credit "Source: BenchGecko" with a link. Prices per provider and Gecko Tests are BenchGecko data (CC BY 4.0); benchmark scores keep their original source, listed in the JSON. JSON · llms.txt · MCP