Solar Mini (non-reasoning)

Solar Mini's 4k token context window means that every token you send in counts towards your input cost. For tasks where information density is crucial, ensure your prompts are concise and directly relevant. Avoid including unnecessary conversational filler or redundant instructions.

• Prune Inputs: Before sending a prompt, remove any irrelevant information or historical conversation turns that are no longer critical for the current task.
• Summarize Prior Context: For multi-turn interactions, summarize previous turns into a compact context rather than sending the full history.
• Efficient Prompt Design: Craft prompts that are clear, direct, and provide all necessary information without being overly verbose.

While specific verbosity data is unavailable, you can still guide Solar Mini to produce outputs of an appropriate length, thereby controlling output token costs. Explicitly instruct the model on the desired length or format of the response.

• Specify Length: Use phrases like "Summarize in 3 sentences," "Provide a short paragraph," or "List 5 key points."
• Define Format: Request specific formats like bullet points, short answers, or single-sentence responses to naturally limit output length.
• Iterative Refinement: If initial outputs are too long, refine your prompt to be more restrictive in subsequent calls.

Solar Mini's slower output speed (76.1 tokens/s) can be a factor for high-volume tasks. To mitigate this, consider batching multiple requests together and processing them asynchronously, rather than waiting for each individual response.

• Group Requests: For tasks like generating multiple product descriptions or summarizing several articles, send them in batches to optimize API call overheads.
• Asynchronous Calls: Implement asynchronous API calls in your application to prevent your system from idling while waiting for responses, improving overall throughput.
• Queue Management: Utilize message queues to manage and process large volumes of requests efficiently, especially during peak times.

As a non-reasoning model with below-average intelligence, Solar Mini is best utilized for tasks that align with its capabilities. Using it for complex analytical or highly creative tasks might lead to unsatisfactory results, requiring more re-prompts and thus increasing costs.

• Identify Core Use Cases: Focus on tasks like summarization, rephrasing, basic content generation, and simple Q&A.
• Avoid Complex Reasoning: For tasks requiring deep understanding, logical inference, or multi-step problem-solving, consider more capable models to avoid iterative prompting.
• Pilot Testing: Thoroughly test Solar Mini on your specific use cases to ensure it meets quality requirements without excessive prompt engineering.

Regularly monitoring your token usage and costs is crucial for identifying inefficiencies and optimizing your budget. Leverage Upstage's analytics tools or integrate your own tracking mechanisms.

• Track Token Usage: Keep a close eye on both input and output token counts for different types of requests.
• Analyze Cost Trends: Identify patterns in your spending to understand which applications or user behaviors are driving costs.
• Set Alerts: Configure alerts for unusual spikes in token usage or costs to quickly address potential issues.

Solar Mini is a compact, open-source, non-reasoning language model developed by Upstage. It is designed for general generative AI tasks, offering a balance of accessibility and performance within a 4k token context window.

Solar Mini has an Artificial Analysis Intelligence Index score of 19 (out of 55), a median output speed of 79 tokens per second (benchmarked at 76.1 tokens/s), and a latency (TTFT) of 1.05 seconds on Upstage.

Solar Mini is priced at $0.15 per 1 million tokens for both input and output. Its input token price is somewhat expensive compared to the average ($0.10), while its output token price is moderately priced compared to the average ($0.20).

Solar Mini is well-suited for tasks such as short-form content generation, text summarization, rephrasing, basic customer service responses, and other generative tasks that do not require complex reasoning or extensive context.

Its limitations include a lower intelligence score for complex analytical tasks, a slower output speed compared to market averages, and a relatively small 4k token context window, which can restrict its use for longer documents or conversations.

While its 1.05-second latency is reasonable, its output speed of 76.1 tokens/s might make it less ideal for highly latency-sensitive or very high-throughput real-time applications where instantaneous responses or massive scale are critical.

To optimize costs, focus on concise prompt engineering, explicitly guide the model for desired output lengths, consider batching requests for efficiency, and ensure the model is applied to tasks that align with its non-reasoning capabilities to minimize re-prompts.

The model's training data includes knowledge up to October 2023, meaning it may not be aware of events or information that have occurred since that time.