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 対 Gemini 2.0 Flash Thinking
モデル概要
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.
主要仕様
| ベンダー | リリース日 | コンテキストウィンドウ | ライセンス |
|---|---|---|---|
| Google / Google | 2024-12-11 / 2024-12-19 | 1048K / 1048K | Proprietary / Proprietary |
ベンチマークパフォーマンス
| ベンチマーク | Gemini 2.0 Flash | Gemini 2.0 Flash Thinking | 勝者 |
|---|---|---|---|
| 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 |
料金比較
| 入力 | 出力 | キャッシュ読み取り | キャッシュ書き込み |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
100万トークンあたり — A / B
強み & 弱み
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。
編集者コメント
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.
参考文献
Editor's Take
See Editor's Take section.