llm-jp-3.1-8x13b-instruct4

llm-jp-3.1-8x13b-instruct4 is a budget-tier language model from Sakura Internet AI Engine. llm-jp-3.1-8x13b-instruct4 is an open-weights model, meaning the weights are publicly available for self-hosting, fine-tuning, and deployment on your own infrastructure — not just through Sakura Internet AI Engine's API. The LLM-jp family is Sakura Internet AI Engine's model series for this capability tier.

llm-jp-3.1-8x13b-instruct4 is priced at $0.15 per 10k tokens (JPY). This is a chat model billed per 10k tokens (JPY) rather than per token — token input/output pricing does not apply.

Its 4K-token context window is standard for this tier — adequate for most chat and completion tasks, but may require chunking for long-document workloads.

llm-jp-3.1-8x13b-instruct4 is best suited to chat, open-weights, cost. Supported capabilities include streaming token output for real-time UIs. When evaluating this model for a specific workload, the most important factors are: context window (does your input fit?), output price (how long are your generations?), and latency (is this interactive or batch?). The pricing table above shows how llm-jp-3.1-8x13b-instruct4 compares to similar models across these dimensions.

llm-jp-3.1-8x13b-instruct4 sits in the budget tier — the most cost-efficient models, best for high-volume simple tasks, classification, extraction, and applications where cost per call is the primary constraint. As an open-weights model, llm-jp-3.1-8x13b-instruct4 can be self-hosted on GPU compute (see the GPU rental comparison on this site) to eliminate per-token API costs entirely — the break-even point versus API pricing depends on your volume and the GPU hourly rate. When comparing llm-jp-3.1-8x13b-instruct4 to alternatives, use the similar models section on this page to find models at comparable price points with different capability tradeoffs. The intelligence score (N/A/100) reflects aggregate benchmark performance — a useful proxy for general capability, though real-world performance varies significantly by task type. Always test with your specific prompts and use case before committing to a model for production.