Llama 3.1 70B vs Mixtral 8x7B: Benchmark Comparison
Detailed comparison of Llama 3.1 70B and Mixtral 8x7B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 3.1 70B | Mixtral 8x7B |
|---|---|---|
| Vendor | meta | mistral |
| Version | 3.1-70b | 8x7b |
| Release Date | 2024-07-23 | 2023-12-11 |
| Context Window | 128000 tokens | 32000 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | Llama 3 Community License | Apache 2.0 |
| SOC2 | ✗ | ✓ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✓ |
| ISO 27001 | ✗ | ✓ |
Benchmark Results
| Benchmark | Llama 3.1 70B | Mixtral 8x7B | Winner |
|---|---|---|---|
| ARC | 92.3 | 91.6 | Llama 3.1 70B |
| BBH | 70.2 | 78.4 | Mixtral 8x7B |
| GPQA | 40 | 43 | Mixtral 8x7B |
| GSM8K | 78.8 | 79.7 | Mixtral 8x7B |
| HUMANEVAL | 79.7 | 79 | Llama 3.1 70B |
| IFEVAL | 73.7 | 72.1 | Llama 3.1 70B |
| MATH | 38.5 | 38 | Llama 3.1 70B |
| MMLU | 75.6 | 77.2 | Mixtral 8x7B |
| MUSR | 48.1 | 53.5 | Mixtral 8x7B |
| WINOGRANDE | 81 | 82 | Mixtral 8x7B |
Pricing Comparison
| Tier (per Mtok) | Llama 3.1 70B | Mixtral 8x7B |
|---|---|---|
| Input | $0.9 | $0.7 |
| Output | $0.9 | $0.7 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 3.1 70B contra Mixtral 8x7B
Descripción del modelo
Llama 3.1 70B and Mixtral 8x7B 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 / Mistral | 2024-07-23 / 2023-12-11 | 128K / 32K | Llama 3 Community License / Apache 2.0 |
Rendimiento en benchmarks
| Benchmark | Llama 3.1 70B | Mixtral 8x7B | Ganador |
|---|---|---|---|
| ARC | 92.3 | 91.6 | A |
| BBH (BIG-Bench Hard) | 70.2 | 78.4 | B |
| GPQA | 40.0 | 43.0 | B |
| GSM8K (Grade School Math 8K) | 78.8 | 79.7 | B |
| HumanEval | 79.7 | 79.0 | A |
| IFEval | 73.7 | 72.1 | A |
| MATH | 38.5 | 38.0 | A |
| MMLU (Massive Multitask Language Understanding) | 75.6 | 77.2 | B |
| MUSR | 48.1 | 53.5 | B |
| WinoGrande | 81.0 | 82.0 | 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。
Mixtral 8x7B
- ✅ 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 Mixtral 8x7B 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.