DeepSeek V3 vs Mixtral 8x22B: Benchmark Comparison
Detailed comparison of DeepSeek V3 and Mixtral 8x22B covering benchmarks, pricing, context window, and compliance.
Key Specifications
| Specification | DeepSeek V3 | Mixtral 8x22B |
|---|---|---|
| Vendor | deepseek | mistral |
| Version | v3 | 8x22b |
| Release Date | 2024-12-26 | 2024-04-10 |
| Context Window | 64000 tokens | 64000 tokens |
| Input Modalities | text | text |
| Output Modalities | text | text |
| License | DeepSeek License | Apache 2.0 |
| SOC2 | ✗ | ✗ |
| HIPAA | ✗ | ✗ |
| GDPR | ✗ | ✗ |
| ISO 27001 | ✗ | ✗ |
Benchmark Results
| Benchmark | DeepSeek V3 | Mixtral 8x22B | Winner |
|---|---|---|---|
| BBH | 84.9 | 74.5 | DeepSeek V3 |
| GSM8K | 89.3 | 78.6 | DeepSeek V3 |
| HUMANEVAL | 82.6 | 45.2 | DeepSeek V3 |
| MATH | 61.6 | 46 | DeepSeek V3 |
| MMLU | 88.5 | 77.8 | DeepSeek V3 |
Pricing Comparison
| Tier (per Mtok) | DeepSeek V3 | Mixtral 8x22B |
|---|---|---|
| Input | $0.27 | $1.2 |
| Output | $1.1 | $1.2 |
| Cache Read | $0.07 | $0 |
| Cache Write | $0.27 | $0 |
DeepSeek V3 против Mixtral 8x22B
Обзор модели
DeepSeek V3 and Mixtral 8x22B are both notable options in the AI model market. This page compares their benchmarks, pricing, and compliance.
Ключевые характеристики
| Поставщик | Дата выпуска | Окно контекста | Лицензия |
|---|---|---|---|
| Deepseek / Mistral | 2024-12-26 / 2024-04-10 | 64K / 64K | DeepSeek License / Apache 2.0 |
Производительность
| Бенчмарк | DeepSeek V3 | Mixtral 8x22B | Победитель |
|---|---|---|---|
| BBH (BIG-Bench Hard) | 84.9 | 74.5 | A |
| GSM8K (Grade School Math 8K) | 89.3 | 78.6 | A |
| HumanEval | 82.6 | 45.2 | A |
| MATH | 61.6 | 46.0 | A |
| MMLU (Massive Multitask Language Understanding) | 88.5 | 77.8 | A |
Сравнение цен
| Вход | Выход | Чтение кэша | Запись кэша |
|---|---|---|---|
| — / — | — / — | — / — | — / — |
за миллион токенов — A / B
Сильные стороны & Слабые стороны
DeepSeek V3
- ✅ MMLU score 88.5, strong knowledge reasoning.
- ✅ HumanEval 82.6, excellent code generation.
- ✅ GSM8K 89.3, robust math reasoning.
- ✅ 采用 MoE 混合专家架构。
- ⚠️ 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。
Мнение редактора
DeepSeek V3 and Mixtral 8x22B each have their strengths. Choose based on workload (code, long context, vision), referencing the tables above.
ЧЗВ
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.
Ссылки
Editor's Take
See Editor's Take section.