DeepSeek V2 vs DeepSeek V3: Benchmark Comparison
Detailed comparison of DeepSeek V2 and DeepSeek V3 covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | DeepSeek V2 | DeepSeek V3 |
|---|---|---|
| Vendor | deepseek | deepseek |
| Version | v2 | v3 |
| Release Date | 2024-05-07 | 2024-12-26 |
| Context Window | 32768 tokens | 64000 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | DeepSeek License | DeepSeek License |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | DeepSeek V2 | DeepSeek V3 | Winner |
|---|---|---|---|
| ARC | 92.1 | — | DeepSeek V2 |
| BBH | 70.5 | 84.9 | DeepSeek V3 |
| GPQA | 31.5 | — | DeepSeek V2 |
| GSM8K | 78.8 | 89.3 | DeepSeek V3 |
| HUMANEVAL | 75.7 | 82.6 | DeepSeek V3 |
| IFEVAL | 78.6 | — | DeepSeek V2 |
| MATH | 35.2 | 61.6 | DeepSeek V3 |
| MMLU | 78.2 | 88.5 | DeepSeek V3 |
| MUSR | 53.4 | — | DeepSeek V2 |
| WINOGRANDE | 84.7 | — | DeepSeek V2 |
Pricing Comparison
| Tier (per Mtok) | DeepSeek V2 | DeepSeek V3 |
|---|---|---|
| Input | $0.14 | $0.27 |
| Output | $0.28 | $1.1 |
| Cache Read | $0 | $0.07 |
| Cache Write | $0 | $0.27 |
DeepSeek V2 대 DeepSeek V3
모델 개요
DeepSeek V2 and DeepSeek V3 are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
핵심 사양
| 공급업체 | 출시일 | 컨텍스트 창 | 라이선스 |
|---|---|---|---|
| Deepseek / Deepseek | 2024-05-07 / 2024-12-26 | 32K / 64K | DeepSeek License / DeepSeek License |
벤치마크 성능
| 벤치마크 | DeepSeek V2 | DeepSeek V3 | 승자 |
|---|---|---|---|
| ARC | 92.1 | — | A |
| BBH (BIG-Bench Hard) | 70.5 | 84.9 | B |
| GPQA | 31.5 | — | A |
| GSM8K (Grade School Math 8K) | 78.8 | 89.3 | B |
| HumanEval | 75.7 | 82.6 | B |
| IFEval | 78.6 | — | A |
| MATH | 35.2 | 61.6 | B |
| MMLU (Massive Multitask Language Understanding) | 78.2 | 88.5 | B |
| MUSR | 53.4 | — | A |
| WinoGrande | 84.7 | — | A |
가격 비교
| 입력 | 출력 | 캐시 읽기 | 캐시 쓰기 |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
백만 토큰당 — A / B
강점 & 약점
DeepSeek V2
- ✅ 采用 MoE 混合专家架构。
- ⚠️ 闭源专有模型，不支持自托管。
DeepSeek V3
- ✅ MMLU score 88.5, strong knowledge reasoning.
- ✅ HumanEval 82.6, excellent code generation.
- ✅ GSM8K 89.3, robust math reasoning.
- ✅ 采用 MoE 混合专家架构。
- ⚠️ 闭源专有模型，不支持自托管。
편집자 의견
DeepSeek V2 and DeepSeek V3 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.