Llama 3.1 8B vs Mistral 7B v0.3: Benchmark Comparison
Detailed comparison of Llama 3.1 8B and Mistral 7B v0.3 covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 3.1 8B | Mistral 7B v0.3 |
|---|---|---|
| Vendor | meta | mistral |
| Version | 3.1-8b | 7b-v0.3 |
| Release Date | 2024-07-23 | 2024-05-22 |
| Context Window | 128000 tokens | 32768 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 8B | Mistral 7B v0.3 | Winner |
|---|---|---|---|
| ARC | 87.4 | 88.9 | Mistral 7B v0.3 |
| BBH | 67.1 | 60.5 | Llama 3.1 8B |
| GPQA | 34.8 | 31.2 | Llama 3.1 8B |
| GSM8K | 58.8 | 69.4 | Mistral 7B v0.3 |
| HUMANEVAL | 63.4 | 68.8 | Mistral 7B v0.3 |
| IFEVAL | 62.1 | 60.9 | Llama 3.1 8B |
| MATH | 31.1 | 26.7 | Llama 3.1 8B |
| MMLU | 73 | 72.6 | Llama 3.1 8B |
| MUSR | 41.7 | 48.1 | Mistral 7B v0.3 |
| WINOGRANDE | 77.6 | 72.7 | Llama 3.1 8B |
Pricing Comparison
| Tier (per Mtok) | Llama 3.1 8B | Mistral 7B v0.3 |
|---|---|---|
| Input | $0.18 | $0.18 |
| Output | $0.18 | $0.18 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 3.1 8B contra Mistral 7B v0.3
Visão geral do modelo
Llama 3.1 8B and Mistral 7B v0.3 are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Especificações principais
| Fornecedor | Data de lançamento | Janela de contexto | Licença |
|---|---|---|---|
| Meta / Mistral | 2024-07-23 / 2024-05-22 | 128K / 32K | Llama 3 Community License / Apache 2.0 |
Desempenho em benchmarks
| Benchmark | Llama 3.1 8B | Mistral 7B v0.3 | Vencedor |
|---|---|---|---|
| ARC | 87.4 | 88.9 | B |
| BBH (BIG-Bench Hard) | 67.1 | 60.5 | A |
| GPQA | 34.8 | 31.2 | A |
| GSM8K (Grade School Math 8K) | 58.8 | 69.4 | B |
| HumanEval | 63.4 | 68.8 | B |
| IFEval | 62.1 | 60.9 | A |
| MATH | 31.1 | 26.7 | A |
| MMLU (Massive Multitask Language Understanding) | 73.0 | 72.6 | Tie |
| MUSR | 41.7 | 48.1 | B |
| WinoGrande | 77.6 | 72.7 | A |
Comparação de preços
| Entrada | Saída | Leitura de cache | Escrita de cache |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
por milhão de tokens — A / B
Pontos fortes & Pontos fracos
Llama 3.1 8B
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
Mistral 7B v0.3
- ✅ 可靠的通用模型。
- ⚠️ 闭源专有模型，不支持自托管。
Opinião do editor
Llama 3.1 8B and Mistral 7B v0.3 each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
Perguntas frequentes
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.
Referências
Editor's Take
See Editor's Take section.