Mixtral 16x7B
Next-gen sparse MoE model for high-efficiency inference and research tasks.
Performance Benchmarks
MMLU (General Knowledge)
Measures broad knowledge across 57 subjects
Coding Performance
Code generation, debugging, and understanding
Reasoning & Logic
Complex problem-solving and analytical thinking
Overall Score: 83.0% - Very good performance, suitable for most tasks
About Mixtral 16x7B
Next-gen sparse MoE model for high-efficiency inference and research tasks.
Mixtral 16x7B is designed for cost-efficient research, multi-domain ai, enterprise self-hosting, making it an ideal choice for developers and businesses looking for cost-effective AI capabilities. With a context window of 64k, it can handle standard conversations and documents.
Priced at $0.00 per million tokens, Mixtral 16x7B offers exceptional value for high-volume applications. It's particularly well-suited for self-hosted ai agents, high-throughput inference, research experiments.
Key Strengths
- Sparse MoE efficiency
- Open-weight support
- High inference throughput
- Fine-tuning flexibility
- Scalable deployment
Limitations to Consider
- Complex MoE management
- Limited prebuilt tools
- Closed multimodal roadmap
- Requires advanced infra
- Community still growing
Ideal Use Cases
Mixtral 16x7B excels in the following applications and scenarios:
Pricing & Cost Analysis
Extremely affordable for high-volume applications
💡 Cost Tip: For applications processing over 1 billion tokens monthly, consider this model offers excellent value at scale.
Quick Stats
Top Competitors
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DeepMindFrequently Asked Questions
What is Mixtral 16x7B best used for?
Mixtral 16x7B is specifically optimized for cost-efficient research, multi-domain ai, enterprise self-hosting. It excels in self-hosted ai agents, high-throughput inference, research experiments, making it ideal for both individuals and enterprises looking for reliable AI capabilities in these areas.
How much does Mixtral 16x7B cost?
Mixtral 16x7B is priced at $0.00 per million tokens. For typical usage of 10 million tokens per month (approximately 300,000 words), this translates to $0.00 monthly. This makes it one of the more affordable options in its category.
How does Mixtral 16x7B compare to GPT-4?
Mixtral 16x7B provides solid performance with a coding score of 82.5% and reasoning score of 83%. At $0.00 per million tokens, it's more cost-effective than GPT-4 Turbo's $10.00 pricing. See detailed comparison →
What is the context window size?
Mixtral 16x7B has a 64k context window, which is suitable for standard conversations and documents.
Ready to Try Mixtral 16x7B?
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