Llama 3.3 70B vs Llama 3.1 70B: Benchmark Comparison
Detailed comparison of Llama 3.3 70B and Llama 3.1 70B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 3.3 70B | Llama 3.1 70B |
|---|---|---|
| Vendor | meta | meta |
| Version | 3.3-70b | 3.1-70b |
| Release Date | 2024-12-06 | 2024-07-23 |
| Context Window | 128000 tokens | 128000 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | Llama 3.3 Community License | Llama 3 Community License |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | Llama 3.3 70B | Llama 3.1 70B | Winner |
|---|---|---|---|
| ARC | 93.9 | 92.3 | Llama 3.3 70B |
| BBH | 83.9 | 70.2 | Llama 3.3 70B |
| GPQA | 52.8 | 40 | Llama 3.3 70B |
| GSM8K | 86.9 | 78.8 | Llama 3.3 70B |
| HUMANEVAL | 87 | 79.7 | Llama 3.3 70B |
| IFEVAL | 79.3 | 73.7 | Llama 3.3 70B |
| MATH | 73.8 | 38.5 | Llama 3.3 70B |
| MMLU | 83.4 | 75.6 | Llama 3.3 70B |
| MUSR | 62.3 | 48.1 | Llama 3.3 70B |
| WINOGRANDE | 86.8 | 81 | Llama 3.3 70B |
Pricing Comparison
| Tier (per Mtok) | Llama 3.3 70B | Llama 3.1 70B |
|---|---|---|
| Input | $0.9 | $0.9 |
| Output | $0.9 | $0.9 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 3.3 70B против Llama 3.1 70B
Обзор модели
Llama 3.3 70B and Llama 3.1 70B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Ключевые характеристики
| Поставщик | Дата выпуска | Окно контекста | Лицензия |
|---|---|---|---|
| Meta / Meta | 2024-12-06 / 2024-07-23 | 128K / 128K | Llama 3.3 Community License / Llama 3 Community License |
Производительность
| Бенчмарк | Llama 3.3 70B | Llama 3.1 70B | Победитель |
|---|---|---|---|
| ARC | 93.9 | 92.3 | A |
| BBH (BIG-Bench Hard) | 83.9 | 70.2 | A |
| GPQA | 52.8 | 40.0 | A |
| GSM8K (Grade School Math 8K) | 86.9 | 78.8 | A |
| HumanEval | 87.0 | 79.7 | A |
| IFEval | 79.3 | 73.7 | A |
| MATH | 73.8 | 38.5 | A |
| MMLU (Massive Multitask Language Understanding) | 83.4 | 75.6 | A |
| MUSR | 62.3 | 48.1 | A |
| WinoGrande | 86.8 | 81.0 | A |
Сравнение цен
| Вход | Выход | Чтение кэша | Запись кэша |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
за миллион токенов — A / B
Сильные стороны & Слабые стороны
Llama 3.3 70B
- ✅ MMLU score 83.4, strong knowledge reasoning.
- ✅ HumanEval 87.0, excellent code generation.
- ✅ GSM8K 86.9, robust math reasoning.
- ⚠️ 闭源专有模型，不支持自托管。
Llama 3.1 70B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
Мнение редактора
Llama 3.3 70B and Llama 3.1 70B each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
ЧЗВ
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.
Ссылки
Editor's Take
See Editor's Take section.