gpt-4o-mini

GPT-4o mini is OpenAI's small, fast, cost-optimized model — a distilled variant of the GPT-4o architecture that retains multimodal capability (text and vision) while dramatically reducing inference cost and latency. It is the default choice for high-volume, cost-sensitive production workloads. It supports function calling, structured outputs, streaming, and a 128K-token context window, and is available on the same API endpoints as GPT-4o with a different model identifier.

GPT-4o mini is priced at $0.15 per million input tokens and $0.60 per million output tokens — among the lowest prices in OpenAI's lineup and competitive with budget-tier models from other providers. Prompt caching reduces repeated input to $0.075/1M. At 1,000 input + 500 output tokens per call, it costs approximately $0.00045 per 1,000 API calls. The 4:1 output-to-input ratio is consistent with OpenAI's other models. For applications where GPT-4o mini's quality is sufficient, the cost savings versus larger models are substantial.

GPT-4o mini's 128K-token context window is the same as GPT-4o, making it suitable for long-document tasks despite its smaller size. Median latency is approximately 150–200ms TTFT — noticeably faster than GPT-4o and suitable for real-time interactive applications. Throughput is around 180 tokens per second, fast enough for smooth streaming. Maximum output is 16,384 tokens. The model's smaller parameter count means it is less capable on complex reasoning, nuanced instruction following, and tasks requiring broad world knowledge — but for well-defined, structured tasks (classification, extraction, summarization, simple Q&A), the quality gap versus GPT-4o is often negligible in practice.

GPT-4o mini is best suited to high-volume, cost-sensitive workloads where task complexity is moderate: content moderation, entity extraction, document summarization, simple chatbots, code completion for common patterns, and classification pipelines. Its vision capability (same as GPT-4o) makes it useful for image classification, basic OCR, and screenshot analysis at a fraction of the cost of larger vision models. Function calling works reliably for well-defined schemas, though complex multi-step agentic workflows benefit from the stronger instruction following of GPT-4o or o-series models. It is the recommended starting point for new OpenAI integrations — test with mini first, then upgrade to GPT-4o only for tasks where quality is measurably insufficient.

GPT-4o mini occupies the efficient tier in OpenAI's lineup, sitting below the full GPT-4o. Its main competitors are efficient-tier models from Anthropic and Google, and open-weights models that can be self-hosted. For teams already on OpenAI's API, GPT-4o mini is the natural cost-optimization step before considering cross-provider alternatives. Its intelligence score of approximately 72/100 reflects solid but not frontier capability — adequate for the majority of production NLP tasks.