GPT-5 mini (medium) vs o3-pro: The Ultimate Performance & Pricing Comparison

GPT-5 mini (medium) is the pragmatic default for cost-sensitive applications, while o3-pro is the stronger choice when the small measured quality lead justifies a much higher token bill. The comparison data from Artificial Analysis gives o3-pro an Artificial Analysis Intelligence Index score of 32.5, compared with 30.9 for GPT-5 mini (medium). GPT-5 mini (medium) also has an Artificial Analysis Math Index score of 85, while no corresponding o3-pro value appears in the supplied data. Both models show 0.3 seconds of latency in the dataset, and neither model has a supplied median output speed. The practical decision therefore depends less on raw responsiveness and more on whether your application needs the highest measured general score, or a dramatically lower token cost. Availability creates a separate risk. OpenAI’s current model directory does not list GPT-5 mini or GPT-5 mini (medium), while the o3-pro model documentation confirms the o3-pro API name and its dated version identifier. That difference makes o3-pro easier to identify operationally, even though its current availability status is not explicitly confirmed in the supplied research.

o3-pro leads measured general intelligence, but GPT-5 mini (medium) offers the far stronger cost case and a separate math score that o3-pro cannot be compared against from the supplied evidence. Artificial Analysis reports 32.5 for o3-pro and 30.9 for GPT-5 mini (medium) on its Intelligence Index. That result supports a quality advantage for o3-pro on the reported general measure, but it does not establish superiority across every developer task. The supplied data does not include a complete benchmark protocol, task-level breakdown, or a matching math result for o3-pro. Developers should treat the score gap as directional evidence rather than a universal production outcome.

GPT-5 mini (medium) is priced at $0.6875 per 1M blended tokens, compared with $35 for o3-pro, according to the supplied comparison data. That difference changes the architecture question. A high-volume classification, extraction, routing, or routine code-assistance workload may be constrained more by spend than by a modest general-score gap. A difficult planning, mathematical, scientific, or debugging workflow may value o3-pro’s reasoning-oriented positioning more highly. OpenAI’s reasoning guide describes reasoning models as spending more time reasoning before producing an answer and primarily using the Responses API. The research does not provide reliable community posts that would validate speed perception, coding style, or recurring failure patterns for either model.

o3-pro has the higher reported general intelligence score, while GPT-5 mini (medium) has the only supplied math score and shares the same measured latency. The Artificial Analysis data reports 32.5 for o3-pro and 30.9 for GPT-5 mini (medium) on the Intelligence Index. In a real application, that lead may matter most when the model must sustain multi-step reasoning, resolve ambiguous requirements, or check its own intermediate conclusions. It does not prove that o3-pro will produce better code, explanations, or tool calls for every prompt because the supplied material lacks task-level results and a reproducible test protocol.

GPT-5 mini (medium) records an Artificial Analysis Math Index score of 85. No o3-pro math score is supplied, so developers should not interpret the comparison as evidence that either model wins mathematics overall. The missing value is itself important: a team selecting for mathematical reliability needs a local evaluation before assigning the work to either model.

Both models report 0.3 seconds of latency in the supplied snapshot. Neither model has a supplied median output tokens-per-second value, so the data cannot support a throughput winner. OpenAI’s reasoning guide supports the expectation that o3-pro is designed for longer reasoning behavior, but the research does not provide an end-to-end latency distribution, streaming measurements, or verified production failure cases. Developers should test time to first token, completion time, tool-call behavior, and retry rates with representative prompts.

GPT-5 mini (medium) is the clear price winner, but o3-pro may be cheaper for workflows where a single reliable answer replaces repeated attempts or human review. The supplied Artificial Analysis snapshot prices GPT-5 mini (medium) at $0.6875 per 1M blended tokens and o3-pro at $35. GPT-5 mini (medium) has listed input and output prices of $0.25 and $2 per 1M tokens, while o3-pro has listed prices of $20 and $80. Those values make GPT-5 mini (medium) the natural first candidate for large request volumes, frequent retries, and broad product surfaces.

The chart cannot show the cost of an unsuccessful workflow. A cheaper model may require extra validation calls, fallback calls, longer prompt scaffolding, or manual correction. An expensive reasoning model may be economically sensible for high-value debugging, scientific analysis, or code changes where an incorrect answer creates downstream engineering work. The supplied research does not quantify correction rates, answer acceptance, or total task cost, so this remains a decision hypothesis rather than a measured conclusion.

Pricing certainty also differs from pricing comparison. OpenAI’s current pricing page does not list GPT-5 mini (medium) in the supplied research, while the data snapshot supplies comparison prices for it. Developers should confirm the live account-level price and model access before launch. Treat the Artificial Analysis values as the comparison dataset, not as a substitute for current billing documentation.

GPT-5 mini (medium) should be the starting point for high-volume developer products, while o3-pro should be reserved for tasks where reasoning quality has measurable business value. Choose GPT-5 mini (medium) for request routing, structured extraction, routine transformations, lightweight coding help, and product features where token economics dominate. Its $0.6875 blended-token price and 85 Artificial Analysis Math Index score make it attractive for broad experimentation, provided the model is actually available through the intended OpenAI account and API path.

Choose o3-pro for difficult planning, advanced debugging, mathematical analysis, scientific reasoning, and decisions where a stronger general intelligence signal may reduce rework. Its 32.5 Intelligence Index score leads GPT-5 mini (medium)’s 30.9, and OpenAI describes o3-pro as a reasoning model available under the o3-pro API name with the dated version identifier o3-pro-2025-06-10. The reasoning models guide explains the longer reasoning behavior that shapes this positioning.

Do not choose either model solely from the supplied score comparison. The evidence does not confirm GPT-5 mini (medium)’s current API availability, context window, output limit, parameter support, or lifecycle. It also does not confirm o3-pro’s current availability, complete limits, multimodal boundary, or recurring failure modes. Run a small production-shaped evaluation with acceptance criteria, tool use, retries, and review cost. If the evaluation shows no meaningful quality gain from o3-pro, its higher price is difficult to justify. If GPT-5 mini (medium) needs repeated correction, its nominal price advantage may shrink.

GPT-5 mini (medium) requires an availability check before technical selection because the current official directory does not confirm the model or its alias. OpenAI’s model directory also does not provide the complete capability and lifecycle details needed for a confident production commitment. o3-pro has clearer naming evidence through its model documentation, but the supplied research still does not confirm its current availability or replacement status.

The central unanswered question is task economics, not headline score. The data does not show whether o3-pro’s general score advantage reduces retries, review, or engineering rework. It also does not provide a comparable math score, output-speed statistic, context window, or reproducible benchmark protocol for both models. Developers should resolve those gaps with account checks and a representative evaluation before selecting a default.