Gemini 2.0 Flash vs Gemini 2.0 Flash Thinking: Benchmark Comparison
Detailed comparison of Gemini 2.0 Flash and Gemini 2.0 Flash Thinking covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Gemini 2.0 Flash | Gemini 2.0 Flash Thinking |
|---|---|---|
| Vendor | ||
| Version | 2.0-flash | 2.0-flash-thinking |
| Release Date | 2024-12-11 | 2024-12-19 |
| Context Window | 1.048576e+06 tokens | 1.048576e+06 tokens |
| Input Modalities | text, image, audio, video | text, image |
| Output Modalities | text | text |
| License | Proprietary | Proprietary |
| SOC2 | ✓ | ✓ |
| HIPAA | ✗ | ✗ |
| GDPR | ✓ | ✓ |
| ISO 27001 | ✓ | ✓ |
Benchmark Results
| Benchmark | Gemini 2.0 Flash | Gemini 2.0 Flash Thinking | Winner |
|---|---|---|---|
| ARC | 95.7 | 95 | Gemini 2.0 Flash |
| BBH | 83.4 | 87.9 | Gemini 2.0 Flash Thinking |
| GPQA | 55 | 61 | Gemini 2.0 Flash Thinking |
| GSM8K | 92.7 | 91.3 | Gemini 2.0 Flash |
| HUMANEVAL | 90.7 | 87.2 | Gemini 2.0 Flash |
| IFEVAL | 85.9 | 82.8 | Gemini 2.0 Flash |
| MATH | 71.3 | 56.6 | Gemini 2.0 Flash |
| MMLU | 86.3 | 86.5 | Gemini 2.0 Flash Thinking |
| MUSR | 69.3 | 70.8 | Gemini 2.0 Flash Thinking |
| WINOGRANDE | 89.5 | 88.1 | Gemini 2.0 Flash |
Pricing Comparison
| Tier (per Mtok) | Gemini 2.0 Flash | Gemini 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 대 Gemini 2.0 Flash Thinking
모델 개요
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.
핵심 사양
| 공급업체 | 출시일 | 컨텍스트 창 | 라이선스 |
|---|---|---|---|
| Google / Google | 2024-12-11 / 2024-12-19 | 1048K / 1048K | Proprietary / Proprietary |
벤치마크 성능
| 벤치마크 | Gemini 2.0 Flash | Gemini 2.0 Flash Thinking | 승자 |
|---|---|---|---|
| ARC | 95.7 | 95.0 | A |
| BBH (BIG-Bench Hard) | 83.4 | 87.9 | B |
| GPQA | 55.0 | 61.0 | B |
| GSM8K (Grade School Math 8K) | 92.7 | 91.3 | A |
| HumanEval | 90.7 | 87.2 | A |
| IFEval | 85.9 | 82.8 | A |
| MATH | 71.3 | 56.6 | A |
| MMLU (Massive Multitask Language Understanding) | 86.3 | 86.5 | Tie |
| MUSR | 69.3 | 70.8 | B |
| WinoGrande | 89.5 | 88.1 | A |
가격 비교
| 입력 | 출력 | 캐시 읽기 | 캐시 쓰기 |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
백만 토큰당 — A / B
강점 & 약점
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。
편집자 의견
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.
FAQ
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.
참고문헌
Editor's Take
See Editor's Take section.