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 karşı DeepSeek V2
Model Genel Bakışı
Llama 3.1 70B and DeepSeek V2 are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Temel Özellikler
| Satıcı | Yayın Tarihi | Bağlam Penceresi | Lisans |
|---|---|---|---|
| Meta / Deepseek | 2024-07-23 / 2024-05-07 | 128K / 32K | Llama 3 Community License / DeepSeek License |
Benchmark Performansı
| Benchmark | Llama 3.1 70B | DeepSeek V2 | Kazanan |
|---|---|---|---|
| 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 |
Fiyat Karşılaştırması
| Giriş | Çıkış | Önbellek Okuma | Önbellek Yazma |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
milyon token başına — A / B
Güçlü Yönler & Zayıf Yönler
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。
Editör Görüşü
Llama 3.1 70B and DeepSeek V2 each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
SSS
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.
Referanslar
Editor's Take
See Editor's Take section.