Llama 3.1 405B vs Mixtral 8x22B: Benchmark Comparison
Detailed comparison of Llama 3.1 405B and Mixtral 8x22B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | Llama 3.1 405B | Mixtral 8x22B |
|---|---|---|
| Vendor | meta | mistral |
| Version | 3.1-405b | 8x22b |
| Release Date | 2024-07-23 | 2024-04-10 |
| Context Window | 128000 tokens | 64000 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 405B | Mixtral 8x22B | Winner |
|---|---|---|---|
| BBH | 82.9 | 74.5 | Llama 3.1 405B |
| GSM8K | 89.2 | 78.6 | Llama 3.1 405B |
| HUMANEVAL | 89 | 45.2 | Llama 3.1 405B |
| MATH | 73.8 | 46 | Llama 3.1 405B |
| MMLU | 88.6 | 77.8 | Llama 3.1 405B |
Pricing Comparison
| Tier (per Mtok) | Llama 3.1 405B | Mixtral 8x22B |
|---|---|---|
| Input | $5 | $1.2 |
| Output | $15 | $1.2 |
| Cache Read | $0 | $0 |
| Cache Write | $0 | $0 |
Llama 3.1 405B contro Mixtral 8x22B
Panoramica del modello
Llama 3.1 405B and Mixtral 8x22B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Specifiche chiave
| Fornitore | Data di rilascio | Finestra di contesto | Licenza |
|---|---|---|---|
| Meta / Mistral | 2024-07-23 / 2024-04-10 | 128K / 64K | Llama 3 Community License / Apache 2.0 |
Prestazioni benchmark
| Benchmark | Llama 3.1 405B | Mixtral 8x22B | Vincitore |
|---|---|---|---|
| BBH (BIG-Bench Hard) | 82.9 | 74.5 | A |
| GSM8K (Grade School Math 8K) | 89.2 | 78.6 | A |
| HumanEval | 89.0 | 45.2 | A |
| MATH | 73.8 | 46.0 | A |
| MMLU (Massive Multitask Language Understanding) | 88.6 | 77.8 | A |
Confronto prezzi
| Input | Output | Lettura cache | Scrittura cache |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
per milione di token — A / B
Punti di forza & Punti deboli
Llama 3.1 405B
- ✅ MMLU score 88.6, strong knowledge reasoning.
- ✅ HumanEval 89.0, excellent code generation.
- ✅ GSM8K 89.2, robust math reasoning.
- ⚠️ 闭源专有模型，不支持自托管。
Mixtral 8x22B
- ✅ 采用 MoE 混合专家架构。
- ⚠️ HumanEval 45.2，代码能力较弱。
- ⚠️ 闭源专有模型，不支持自托管。
Opinione dell’editore
Llama 3.1 405B and Mixtral 8x22B 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.
Riferimenti
Editor's Take
See Editor's Take section.