DeepSeek R1 Distill Llama 70B (Distilled)

Your choice of provider has the single biggest impact on cost and performance. Don't default to one provider for all tasks.

• For cost-sensitive batch jobs: Use Deepinfra. It offers the lowest blended price and is ideal for asynchronous tasks where latency is not a primary concern.
• For speed-critical applications: Use SambaNova. The higher cost is justified by its industry-leading throughput, which can be crucial for user-facing features.
• For real-time chat: Use Deepinfra. Its sub-second latency provides the best user experience for interactive applications.

This model tends to be verbose, which directly increases output token costs. Use prompt engineering to enforce brevity.

• Include explicit instructions in your prompts, such as "Be concise," "Answer in one paragraph," or "Use bullet points."
• Set a lower max_tokens limit in your API call to create a hard cap on the output length, preventing runaway generation.
• Experiment with temperature settings; lower temperatures often lead to more focused and less rambling responses.

The 128k context window is powerful but expensive to fill. Avoid passing unnecessary information to the model.

• Instead of feeding an entire document, use a pre-processing step (like embeddings-based search) to identify and send only the most relevant chunks of text.
• For chat applications, implement a context summarization strategy. Periodically summarize the conversation history and use the summary as context for future turns, rather than the full transcript.

Many user queries are repetitive. Caching responses to common prompts can eliminate redundant API calls and save significant costs.

• Implement a simple key-value store (like Redis) to cache results for identical prompts.
• For similar but not identical prompts, consider semantic caching, which uses embeddings to find and return cached responses for semantically similar queries.

It is a large language model from DeepSeek AI that has been "distilled" from the Llama 70B architecture. This process aims to create a faster, more efficient model that retains the core intelligence of its larger predecessor, making it suitable for high-speed applications.

Distillation is a training technique where a smaller "student" model learns to mimic the outputs and behavior of a larger, more complex "teacher" model. The result is a student model that is significantly faster and requires fewer computational resources for inference, often with only a minor trade-off in performance quality.

It is designed to be much faster at generating responses. While it aims to match the intelligence of the original, there may be subtle differences or slight performance degradation on highly specific or nuanced tasks. The primary trade-off is sacrificing a small amount of potential capability for a large gain in speed.

Its primary advantage is speed. With some providers achieving over 300 tokens per second, it is one of the fastest models in the 70B parameter class, making it excellent for real-time, interactive use cases where response time is critical.

Its main disadvantage is cost. Both input and output token prices are substantially higher than the average for other open-weight models of a similar size. Its tendency to be verbose can further amplify these costs if not properly managed.

The 128k context window is incredibly powerful for tasks requiring analysis of large documents or long conversation histories. However, due to the high input token price, filling this window is expensive. It is most effective when used judiciously for specific, high-value tasks rather than as a default for all queries.

There is no single "best" provider; it depends on your priority:

• Lowest Cost: Deepinfra
• Highest Speed: SambaNova
• Lowest Latency: Deepinfra
• Balanced (but expensive): Together.ai