Llama 2 70B vs Llama 3 70B: Benchmark Comparison
Detailed comparison of Llama 2 70B and Llama 3 70B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 2 70B | Llama 3 70B |
|---|---|---|
| Vendor | meta | meta |
| Version | 2-70b | 3-70b |
| Release Date | 2023-07-18 | 2024-04-18 |
| Context Window | 4096 tokens | 8192 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | Llama 2 Community License | Llama 3 Community License |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | Llama 2 70B | Llama 3 70B | Winner |
|---|---|---|---|
| ARC | 80.3 | 93.4 | Llama 3 70B |
| BBH | 62.5 | 77.6 | Llama 3 70B |
| GPQA | 27.2 | 38.2 | Llama 3 70B |
| GSM8K | 51.8 | 76.2 | Llama 3 70B |
| HUMANEVAL | 50.6 | 73 | Llama 3 70B |
| IFEVAL | 57.1 | 72.2 | Llama 3 70B |
| MATH | 22.4 | 50.1 | Llama 3 70B |
| MMLU | 53 | 79.5 | Llama 3 70B |
| MUSR | 36.1 | 51.2 | Llama 3 70B |
| WINOGRANDE | 73.4 | 79.2 | Llama 3 70B |
Pricing Comparison
| Tier (per Mtok) | Llama 2 70B | Llama 3 70B |
|---|---|---|
| Input | $0.9 | $0.9 |
| Output | $0.9 | $0.9 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 2 70B ضد Llama 3 70B
نظرة عامة على النموذج
Llama 2 70B and Llama 3 70B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
المواصفات الرئيسية
| المزود | تاريخ الإصدار | نافذة السياق | الترخيص |
|---|---|---|---|
| Meta / Meta | 2023-07-18 / 2024-04-18 | 4K / 8K | Llama 2 Community License / Llama 3 Community License |
أداء المعايير
| المعيار | Llama 2 70B | Llama 3 70B | الفائز |
|---|---|---|---|
| ARC | 80.3 | 93.4 | B |
| BBH (BIG-Bench Hard) | 62.5 | 77.6 | B |
| GPQA | 27.2 | 38.2 | B |
| GSM8K (Grade School Math 8K) | 51.8 | 76.2 | B |
| HumanEval | 50.6 | 73.0 | B |
| IFEval | 57.1 | 72.2 | B |
| MATH | 22.4 | 50.1 | B |
| MMLU (Massive Multitask Language Understanding) | 53.0 | 79.5 | B |
| MUSR | 36.1 | 51.2 | B |
| WinoGrande | 73.4 | 79.2 | B |
مقارنة الأسعار
| الإدخال | الإخراج | قراءة الذاكرة المؤقتة | كتابة الذاكرة المؤقتة |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
لكل مليون رمز — A / B
نقاط القوة & نقاط الضعف
Llama 2 70B
- ✅ 可靠的通用模型。
- ⚠️ MMLU 仅 53.0，知识推理偏弱。
- ⚠️ 闭源专有模型，不支持自托管。
- ⚠️ 上下文窗口 4K 偏小。
Llama 3 70B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
- ⚠️ 上下文窗口 8K 偏小。
رأي المحرر
Llama 2 70B and Llama 3 70B each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
الأسئلة الشائعة
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.
المراجع
Editor's Take
See Editor's Take section.