GPT-5 (medium) vs o3-pro: Which OpenAI Model Should Developers Choose?

GPT-5 (medium) is the stronger default on the available measurements, while o3-pro remains the more clearly documented choice for deliberate reasoning tasks. Artificial Analysis reports an Intelligence Index score of 33.7 for GPT-5 (medium) and 32.5 for o3-pro. The same dataset reports a Math Index score of 91.7 for GPT-5 (medium), but no comparable o3-pro value. That makes GPT-5 (medium) the practical starting point for developers who need broad capability at controlled cost.\n\nThe comparison has an important qualification: the evidence is asymmetric. The supplied OpenAI model directory does not list gpt-5-medium, and the supplied pricing page does not list it either (OpenAI Models, OpenAI API Pricing). By contrast, OpenAI documents o3-pro with the alias o3-pro and the dated version o3-pro-2025-06-10 (o3-pro Model Documentation).\n\nThe result is not a simple quality ranking. GPT-5 (medium) has the better supplied general score and far lower supplied prices. o3-pro has a clearer official positioning around longer reasoning for difficult scientific, mathematical, and programming work (Introducing o3-pro). Developers should therefore separate measured output quality, documented product status, task-specific reasoning needs, and operational cost before committing to one model.

GPT-5 (medium) offers the better measured value, while o3-pro offers the clearer reasoning-oriented product story.\n\n| Decision factor | GPT-5 (medium) | o3-pro | Practical reading | |—|—:|—:|—| | Artificial Analysis Intelligence Index | 33.7 | 32.5 | GPT-5 (medium) leads on the supplied broad score | | Artificial Analysis Math Index | 91.7 | Not reported | The math comparison is incomplete | | Blended price per 1M tokens | $3.4375 | $35 | GPT-5 (medium) has the lower supplied blended price | | Input price per 1M tokens | $1.25 | $20 | Large prompt-heavy workflows favor GPT-5 (medium) | | Output price per 1M tokens | $10 | $80 | Long answers are more expensive with o3-pro | | Median latency | 0.3 seconds | 0.3 seconds | The supplied latency measurement is tied | \nThe strongest selection signal is the combination of 33.7 Intelligence Index points and $3.4375 blended pricing for GPT-5 (medium). The strongest counter-signal is documentation quality. OpenAI’s current supplied directory does not establish a callable name, stable alias, context window, output limit, or dedicated capability profile for GPT-5 (medium) (OpenAI Models).\n\no3-pro is easier to place in an API architecture because OpenAI publishes both its general alias and dated version identifier (o3-pro Model Documentation). OpenAI also describes o3-pro as a reasoning model that spends more time reasoning before producing an answer (Reasoning models guide). That description supports a targeted use case, but it does not prove superior performance on every developer workload.\n\nThe evidence therefore supports GPT-5 (medium) as the default candidate, subject to availability verification. It supports o3-pro as a deliberate escalation option when reasoning depth matters more than token cost and when the documented model endpoint fits the deployment.

GPT-5 (medium) leads the supplied general evaluation, but the available performance evidence does not establish a universal task winner.\n\nThe Artificial Analysis Intelligence Index is 33.7 for GPT-5 (medium) and 32.5 for o3-pro. That difference is small enough that developers should not treat it as proof that GPT-5 (medium) will win every coding, extraction, agent, or conversational workload. It does establish that the supplied broad evaluation favors GPT-5 (medium). The practical implication is that GPT-5 (medium) deserves the first benchmark slot for mixed workloads, especially when the application combines ordinary generation with structured reasoning.\n\nThe math evidence is more unusual. GPT-5 (medium) has a reported Artificial Analysis Math Index score of 91.7. o3-pro has no corresponding value in the supplied dataset. This is not a score of zero, and it is not evidence that o3-pro is weaker at mathematics. It is an evidence gap. A developer choosing a model for formal mathematics, theorem-related work, or numerical problem solving should run a matched evaluation rather than infer a ranking from missing data.\n\nThe official positioning points in a different direction. OpenAI introduced o3-pro as a model that thinks longer and provides more reliable answers for complex science, mathematics, and programming tasks (Introducing o3-pro). OpenAI’s reasoning guide says reasoning models spend longer reasoning before answering and are mainly used through the Responses API (Reasoning models guide). That supports o3-pro for tasks where verification quality matters more than conversational immediacy.\n\nNeither model has a reported median output speed in the supplied data. Both have a supplied latency of 0.3 seconds. Developers should therefore avoid claiming that o3-pro is slower or that GPT-5 (medium) streams faster. The missing output-speed values matter for interactive coding tools, where token generation can shape perceived responsiveness even when request latency is similar.

GPT-5 (medium) is the clear cost choice in the supplied price data, but workload quality and retry behavior can change the operational decision.\n\nThe supplied blended price is $3.4375 per 1M tokens for GPT-5 (medium) and $35 for o3-pro. Input pricing is $1.25 for GPT-5 (medium) and $20 for o3-pro. Output pricing is $10 for GPT-5 (medium) and $80 for o3-pro. These figures make GPT-5 (medium) the natural candidate for high-volume generation, classification, retrieval-augmented answers, and developer tools with frequent requests.\n\nThe important question is not only the price of one successful call. A cheaper model can become more expensive if it produces more failed tool calls, incorrect patches, or human review work. The supplied data does not measure those outcomes. It also does not provide a workload-specific success rate, so no break-even calculation is justified from the brief alone.\n\nThe cost conclusion also depends on whether GPT-5 (medium) is actually available under a stable API identifier. The supplied OpenAI model directory does not list gpt-5-medium, and the supplied OpenAI pricing page does not list it (OpenAI Models, OpenAI API Pricing). The Artificial Analysis snapshot provides prices, but the official pages supplied for verification do not confirm current availability.\n\nFor o3-pro, the official pricing page supplied in the research brief lists $20 per 1M input tokens and $80 per 1M output tokens (OpenAI API Pricing). That price makes selective routing important. A sensible architecture can reserve o3-pro for difficult cases, provided the application can identify those cases reliably. The brief does not provide evidence that such a router would improve total cost, so that decision requires testing.

GPT-5 (medium) is the recommended first choice for most developers who can verify its API availability and need broad quality at low measured cost.\n\nChoose GPT-5 (medium) for a general-purpose application with many calls, mixed task types, or strict token economics. Its supplied Intelligence Index score is 33.7, its Math Index score is 91.7, and its blended price is $3.4375 per 1M tokens. Those measurements make it the most attractive default in the supplied comparison. The recommendation remains conditional because the provided OpenAI directory does not confirm a dedicated model entry, stable alias, context window, output limit, or model-specific API behavior (OpenAI Models).\n\nChoose o3-pro when your application has a narrow set of high-value tasks where longer reasoning is central to the product. OpenAI positions o3-pro for complex science, mathematics, and programming, and documents the model as a reasoning model (Introducing o3-pro, Reasoning models guide). Its documented alias is o3-pro, with the dated version o3-pro-2025-06-10 (o3-pro Model Documentation). That clarity can reduce integration ambiguity even though its supplied price is much higher.\n\nDo not select o3-pro solely because its name signals deeper reasoning. The supplied benchmark does not provide an o3-pro math score, a reproducible benchmark protocol, output-speed data, or community test evidence. Do not select GPT-5 (medium) solely because it is cheaper. Its official availability and exact operational limits remain unconfirmed in the supplied research.\n\nThe safest implementation is a small task-based bake-off. Test representative prompts, tool calls, structured outputs, code edits, and failure recovery. Record correctness, retries, review time, and total tokens. The brief supports the candidate ranking, but it does not supply enough evidence to predict your application’s winner with confidence.

GPT-5 (medium) should be treated as a promising default candidate whose production status requires direct verification.\n\nThe questions below address the gaps that the benchmark numbers cannot resolve. They focus on callable identity, reasoning behavior, cost exposure, and evidence quality. Each answer distinguishes what the supplied material establishes from what it does not establish.