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 contre DeepSeek V2
Aperçu du modèle
Llama 3.1 70B and DeepSeek V2 are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Spécifications clés
| Fournisseur | Date de sortie | Fenêtre de contexte | Licence |
|---|---|---|---|
| Meta / Deepseek | 2024-07-23 / 2024-05-07 | 128K / 32K | Llama 3 Community License / DeepSeek License |
Performance aux benchmarks
| Benchmark | Llama 3.1 70B | DeepSeek V2 | Gagnant |
|---|---|---|---|
| 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 |
Comparaison des prix
| Entrée | Sortie | Lecture cache | Écriture cache |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
par million de jetons — A / B
Forces & Faiblesses
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。
Avis de la rédaction
Llama 3.1 70B and DeepSeek V2 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.
Références
Editor's Take
See Editor's Take section.