Llama 3.1 70B vs DeepSeek V2: Benchmark Comparison
Detailed comparison of Llama 3.1 70B and DeepSeek V2 covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 3.1 70B | DeepSeek V2 |
|---|---|---|
| Vendor | meta | deepseek |
| Version | 3.1-70b | v2 |
| Release Date | 2024-07-23 | 2024-05-07 |
| Context Window | 128000 tokens | 32768 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | Llama 3 Community License | DeepSeek License |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | Llama 3.1 70B | DeepSeek V2 | Winner |
|---|---|---|---|
| ARC | 92.3 | 92.1 | Llama 3.1 70B |
| BBH | 70.2 | 70.5 | DeepSeek V2 |
| GPQA | 40 | 31.5 | Llama 3.1 70B |
| GSM8K | 78.8 | 78.8 | Tie |
| HUMANEVAL | 79.7 | 75.7 | Llama 3.1 70B |
| IFEVAL | 73.7 | 78.6 | DeepSeek V2 |
| MATH | 38.5 | 35.2 | Llama 3.1 70B |
| MMLU | 75.6 | 78.2 | DeepSeek V2 |
| MUSR | 48.1 | 53.4 | DeepSeek V2 |
| WINOGRANDE | 81 | 84.7 | DeepSeek V2 |
Pricing Comparison
| Tier (per Mtok) | Llama 3.1 70B | DeepSeek V2 |
|---|---|---|
| Input | $0.9 | $0.14 |
| Output | $0.9 | $0.28 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 3.1 70B contra DeepSeek V2
Descripción del modelo
Llama 3.1 70B and DeepSeek V2 are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Especificaciones clave
| Proveedor | Fecha de lanzamiento | Ventana de contexto | Licencia |
|---|---|---|---|
| Meta / Deepseek | 2024-07-23 / 2024-05-07 | 128K / 32K | Llama 3 Community License / DeepSeek License |
Rendimiento en benchmarks
| Benchmark | Llama 3.1 70B | DeepSeek V2 | Ganador |
|---|---|---|---|
| ARC | 92.3 | 92.1 | Tie |
| BBH (BIG-Bench Hard) | 70.2 | 70.5 | Tie |
| GPQA | 40.0 | 31.5 | A |
| GSM8K (Grade School Math 8K) | 78.8 | 78.8 | Tie |
| HumanEval | 79.7 | 75.7 | A |
| IFEval | 73.7 | 78.6 | B |
| MATH | 38.5 | 35.2 | A |
| MMLU (Massive Multitask Language Understanding) | 75.6 | 78.2 | B |
| MUSR | 48.1 | 53.4 | B |
| WinoGrande | 81.0 | 84.7 | B |
Comparación de precios
| Entrada | Salida | Lectura caché | Escritura caché |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
por millón de tokens — A / B
Fortalezas & Debilidades
Llama 3.1 70B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
DeepSeek V2
- ✅ 采用 MoE 混合专家架构。
- ⚠️ 闭源专有模型，不支持自托管。
Opinión del editor
Llama 3.1 70B and DeepSeek V2 each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
Preguntas frecuentes
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.
Referencias
Editor's Take
See Editor's Take section.