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 대 Mixtral 8x7B
모델 개요
Llama 3.1 70B and Mixtral 8x7B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
핵심 사양
| 공급업체 | 출시일 | 컨텍스트 창 | 라이선스 |
|---|---|---|---|
| Meta / Mistral | 2024-07-23 / 2023-12-11 | 128K / 32K | Llama 3 Community License / Apache 2.0 |
벤치마크 성능
| 벤치마크 | Llama 3.1 70B | Mixtral 8x7B | 승자 |
|---|---|---|---|
| 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 |
가격 비교
| 입력 | 출력 | 캐시 읽기 | 캐시 쓰기 |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
백만 토큰당 — A / B
강점 & 약점
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。
편집자 의견
Llama 3.1 70B and Mixtral 8x7B 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.
참고문헌
Editor's Take
See Editor's Take section.