o4-mini
Understand o4-mini reasoning and visual tasks, its 200,000-token context and example prompts. Review limitations, current availability and Vife credit costs.
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Model capabilities
o4-mini is an OpenAI reasoning model optimized for coding and visual tasks, succeeded by GPT-5 Mini. OpenAI documents a 200,000-token context window and up to 100,000 output tokens. It accepts text and image input and produces text output. It can reason about an image but does not generate images. Response time depends on the task, reasoning workload and requested output. Evaluate it with representative prompts before choosing it for an interactive application.
Example prompt
Inspect this chart and the accompanying data table. Explain the trend, identify missing labels or units, and show the calculation behind each numerical conclusion. State what cannot be inferred from the image. Provide a readable image and the original data when possible, then independently check the arithmetic and any uncertain visual interpretation.
Frequently Asked Questions
How should I compare speed and quality?
Test the same representative prompts across available models. Review correctness, instruction following, response time, and the displayed credit cost. Speed depends on the prompt, output length, reasoning settings, and service load; a fixed speed multiplier is not a reliable promise.
What about cost-effectiveness?
Vife uses credits and its own plan terms. Provider API token prices and per-image estimates are different billing units. Check Vife's displayed quote and pricing page before generating.
Is it suitable for production applications?
Evaluate the model on your own tasks before using its output in production. Review security-sensitive code, verify factual claims, and plan for failed or delayed requests. Check the actual service terms for support, data handling, and availability commitments.
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