Key Specifications

SpecificationGemini 2.0 FlashGemini 2.0 Flash Thinking
Vendorgooglegoogle
Version2.0-flash2.0-flash-thinking
Release Date2024-12-112024-12-19
Context Window1.048576e+06 tokens1.048576e+06 tokens
Input Modalitiestext, image, audio, videotext, image
Output Modalitiestexttext
LicenseProprietaryProprietary
SOC2
HIPAA
GDPR
ISO 27001

Benchmark Results

BenchmarkGemini 2.0 FlashGemini 2.0 Flash ThinkingWinner
ARC95.795Gemini 2.0 Flash
BBH83.487.9Gemini 2.0 Flash Thinking
GPQA5561Gemini 2.0 Flash Thinking
GSM8K92.791.3Gemini 2.0 Flash
HUMANEVAL90.787.2Gemini 2.0 Flash
IFEVAL85.982.8Gemini 2.0 Flash
MATH71.356.6Gemini 2.0 Flash
MMLU86.386.5Gemini 2.0 Flash Thinking
MUSR69.370.8Gemini 2.0 Flash Thinking
WINOGRANDE89.588.1Gemini 2.0 Flash

Pricing Comparison

Tier (per Mtok)Gemini 2.0 FlashGemini 2.0 Flash Thinking
Input$0.1$0.1
Output$0.4$0.4
Cache Read$0$0
Cache Write$0$0

Gemini 2.0 Flash contra Gemini 2.0 Flash Thinking

Visão geral do modelo

Gemini 2.0 Flash and Gemini 2.0 Flash Thinking are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.

Especificações principais

FornecedorData de lançamentoJanela de contextoLicença
Google / Google2024-12-11 / 2024-12-191048K / 1048KProprietary / Proprietary

Desempenho em benchmarks

BenchmarkGemini 2.0 FlashGemini 2.0 Flash ThinkingVencedor
ARC95.795.0A
BBH (BIG-Bench Hard)83.487.9B
GPQA55.061.0B
GSM8K (Grade School Math 8K)92.791.3A
HumanEval90.787.2A
IFEval85.982.8A
MATH71.356.6A
MMLU (Massive Multitask Language Understanding)86.386.5Tie
MUSR69.370.8B
WinoGrande89.588.1A

Comparação de preços

EntradaSaídaLeitura de cacheEscrita de cache
— / —— / —— / —— / —

por milhão de tokens — A / B

Pontos fortes & Pontos fracos

Gemini 2.0 Flash

  • ✅ MMLU score 86.3, strong knowledge reasoning.
  • ✅ HumanEval 90.7, excellent code generation.
  • ✅ GSM8K 92.7, robust math reasoning.
  • ✅ 支持文本、图像、音频多模态输入。
  • ⚠️ 闭源专有模型,不支持自托管。

Gemini 2.0 Flash Thinking

  • ✅ MMLU score 86.5, strong knowledge reasoning.
  • ✅ HumanEval 87.2, excellent code generation.
  • ✅ GSM8K 91.3, robust math reasoning.
  • ✅ 支持文本、图像、音频多模态输入。
  • ⚠️ 闭源专有模型,不支持自托管。

Opinião do editor

Gemini 2.0 Flash and Gemini 2.0 Flash Thinking each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.

Perguntas frequentes

Which model is better for coding tasks?

Refer to the HumanEval benchmark table; the model with a higher score is better suited for coding tasks.

Which model is cheaper?

Refer to the pricing comparison table above; the model with lower input/output prices is more cost-effective.

Which has a longer context window?

Refer to the key specifications table; the model with a larger context window is better for long documents.

Referências

Editor's Take

See Editor's Take section.