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 vs Mixtral 8x22B
Model Overview
Llama 3.1 405B and Mixtral 8x22B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Key Specifications
| Vendor | Release Date | Context Window | License |
|---|---|---|---|
| Meta / Mistral | 2024-07-23 / 2024-04-10 | 128K / 64K | Llama 3 Community License / Apache 2.0 |
Benchmark Performance
| Benchmark | Llama 3.1 405B | Mixtral 8x22B | Winner |
|---|---|---|---|
| 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 |
Pricing Comparison
| Input | Output | Cache Read | Cache Write |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
per million tokens — A / B
Strengths & Weaknesses
Llama 3.1 405B
- ✅ MMLU score 88.6, strong knowledge reasoning.
- ✅ HumanEval 89.0, excellent code generation.
- ✅ GSM8K 89.2, robust math reasoning.
- ⚠️ Proprietary, not self-hostable.
Mixtral 8x22B
- ✅ Mixture-of-Experts architecture.
- ⚠️ HumanEval 45.2, coding weak.
- ⚠️ Proprietary, not self-hostable.
Editor’s Take
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.
References
Editor's Take
See Editor's Take section.