DeepSeek V3.1 Terminus (Non-reasoning)

The most impactful cost-saving measure is selecting the right provider for your workload. Don't default to a single provider for all tasks.

• For background tasks: Use the ultra-low-cost quantized models from Novita or Deepinfra for batch processing, report generation, or data analysis where latency and speed are not user-facing.
• For interactive tasks: Use a balanced provider like Eigen AI or Fireworks for chatbots and interactive tools that require a good mix of speed, latency, and reasonable cost.
• For high-throughput needs: Only use a premium-priced, high-speed provider like SambaNova when the business value of maximum throughput justifies the significant cost increase.

Quantization is the process of reducing the precision of the model's weights (e.g., from 16-bit floating point to 4-bit or 8-bit integers). This dramatically reduces the model's size and computational requirements, leading to lower hosting costs that providers pass on to you.

• The FP4 and FP8 versions from Deepinfra and Novita cut the blended price by nearly 50% compared to the next cheapest non-quantized option.
• While there can be a minor drop in output quality with quantization, for many tasks, it is imperceptible. Always test a quantized endpoint first to see if it meets your quality bar before opting for a more expensive, full-precision model.

The 128k context window is a powerful feature, but it's also a potential budget-breaker if misused. The cost of a request is directly proportional to the number of input tokens.

• Don't use it if you don't need it: For simple queries or conversations, use a much smaller context. Only send the full 128k tokens when you are genuinely processing a document that large.
• Implement context management: For chatbots, use summarization techniques or sliding window strategies to keep the context passed with each turn to a manageable size. Don't just append the entire chat history indefinitely.

Output tokens are consistently more expensive than input tokens. You can control the length of the model's response through careful prompting, directly impacting your costs.

• Set clear constraints: Add instructions to your prompt like "Be concise," "Respond in three sentences," "Use bullet points," or "Limit the response to 100 words."
• Refine your prompts: If you find the model is consistently too verbose for a specific task, iterate on the system prompt to guide it toward shorter, more direct answers. This small, one-time effort can lead to significant savings over time.

DeepSeek V3.1 Terminus is a large language model from DeepSeek AI. It is an open-weight model, meaning its architecture and weights are publicly available under a specific license. It is designed for a wide range of text-based tasks and is notable for its high intelligence score and very large 128,000-token context window.

The "(Non-reasoning)" designation typically implies that this version of the model is optimized for direct instruction-following, text generation, summarization, and comprehension tasks. It may be distinct from other variants in the DeepSeek family that are specifically fine-tuned for complex, multi-step logical reasoning, mathematical problem-solving, or advanced planning. This version is a general-purpose workhorse for content and language processing.

DeepSeek V3.1 Terminus is highly competitive. Its intelligence score of 46 places it among the top performers in its class, outperforming many similarly-sized models. Its key differentiators are the combination of this high intelligence with a 128k context window and the availability of very low-cost quantized endpoints, offering a unique blend of performance and value.

Quantization is a technique to reduce the memory and computational footprint of a model by using lower-precision numbers for its weights (e.g., 4-bit or 8-bit integers instead of 16-bit floats). This has two main effects:

• Cost: It makes the model much cheaper to run, a saving that providers like Novita and Deepinfra pass on to customers.
• Performance: It typically results in slower token generation speeds (output tokens/s). It can also slightly increase latency, though some optimized systems (like Deepinfra's) can achieve very low time-to-first-token. There may be a minor, often negligible, impact on output quality.

A 128,000-token context window allows the model to process and 'remember' a vast amount of information in a single request. This is equivalent to hundreds of pages of text. It is particularly useful for:

• Analyzing or summarizing entire books, long legal documents, or extensive financial reports.
• Answering questions about a large codebase without having to break it into smaller chunks.
• Maintaining long, coherent conversations in a chatbot without losing track of earlier parts of the discussion.
• Complex retrieval-augmented generation (RAG) where large amounts of source material need to be considered.

The model itself is 'free' in the sense that it has an open license (the DeepSeek License) that allows for commercial use, modification, and self-hosting. However, 'using' the model requires significant computational resources. The prices discussed on this page are for using the model via managed API providers, who handle the hosting and inference for you. Self-hosting is an option for expert teams but involves substantial hardware and operational costs.