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

GPT-5 (medium) is the stronger measured choice, but o3 is the safer performance choice when streaming speed matters. The data snapshot gives GPT-5 (medium) an Artificial Analysis Intelligence Index of 33.7 and a Math Index of 91.7, compared with 30.4 and 88.3 for o3. The same snapshot reports 0.3 seconds of latency for each model. It reports o3 at 128.056 median output tokens per second, while GPT-5 (medium) has no reported output-speed value.

That result needs a qualification. The current OpenAI Models documentation does not list GPT-5 (medium) or o3, and it does not provide model-specific context limits, output limits, API parameters, or benchmark results for either one. The current OpenAI API Pricing documentation also does not list either model. The data therefore supports a practical snapshot comparison, but it does not establish current production availability.

For a new application, GPT-5 (medium) fits quality-sensitive workloads if the model is actually callable in your account. For latency-sensitive interfaces, o3 deserves testing because its measured output rate is explicit. For a long-lived production dependency, neither model should be selected without verifying endpoint access, aliases, quotas, and retirement risk.

GPT-5 (medium) leads the available quality and blended-cost evidence, while o3 leads the available streaming-speed evidence. GPT-5 (medium) scores 3.3 points higher on the Artificial Analysis Intelligence Index and 3.4 points higher on the Artificial Analysis Math Index. Those margins are meaningful for tasks where answer quality, planning, and mathematical reliability drive review effort.

The pricing picture is more nuanced. GPT-5 (medium) costs $1.25 per 1M input tokens and $10 per 1M output tokens in the data snapshot. o3 costs $2 per 1M input tokens and $8 per 1M output tokens. The reported 3-to-1 blended price is $3.4375 for GPT-5 (medium) and $3.5 for o3. A workload dominated by large prompts favors GPT-5 (medium). A workload dominated by generated text favors o3.

Neither model has a reported context-window value in the data snapshot. Neither model has a documented dedicated profile in the supplied official pages. The models page describes capabilities for current models in general, including text and image inputs, text output, multilingual use, and vision, but it does not confirm that those statements apply to either comparison target.

The central selection issue is therefore not simply quality versus cost. It is measured quality versus operational certainty, with the evidence incomplete on both sides.

GPT-5 (medium) has the stronger measured evaluation profile, while o3 has the only explicit median output-speed result. GPT-5 (medium) reaches 33.7 on the Artificial Analysis Intelligence Index versus 30.4 for o3. It also reaches 91.7 on the Artificial Analysis Math Index versus 88.3 for o3. The data snapshot reports 0.3 seconds of latency for both models.

For developers, the quality gap matters most when model output requires human correction or downstream validation. A higher intelligence score can support broader reasoning workloads, while the higher math score is especially relevant to symbolic work, quantitative explanations, and code that depends on numerical logic. The scores do not prove that GPT-5 (medium) wins every task. They indicate that the supplied evaluation evidence favors it across both listed indices.

The streaming result changes the operational story. o3 is reported at 128.056 median output tokens per second. GPT-5 (medium) has no reported value, so the comparison cannot establish that o3 is faster overall. It can establish that o3 has documented output-speed evidence in this snapshot and GPT-5 (medium) does not.

A chat product that reveals tokens progressively may benefit from o3 even if its final answers need more checking. A batch workflow may care less about visible streaming and more about answer quality. Because the official OpenAI Models page does not provide dedicated profiles for either model, developers should validate first-token latency, sustained generation, tool-call behavior, and failure recovery in their own workload. The supplied materials do not answer those questions.

GPT-5 (medium) has the lower blended price, but o3 can be cheaper for output-heavy workloads. The data snapshot lists GPT-5 (medium) at $3.4375 per 1M blended tokens and o3 at $3.5. That makes GPT-5 (medium) the nominal blended-price winner under the stated 3-to-1 mix.

The input and output rates point in opposite directions. GPT-5 (medium) charges $1.25 per 1M input tokens, compared with $2 for o3. This favors applications that repeatedly send large instructions, documents, conversation history, or retrieved context. o3 charges $8 per 1M output tokens, compared with $10 for GPT-5 (medium). This favors applications that generate long reports, verbose code, or multi-step responses.

The blended figure should not be treated as a universal bill estimate. It describes one token mix, while real applications vary by prompt size, response length, retries, tool traces, and caching behavior. A short-answer assistant may be input-heavy. A report generator may be output-heavy. A coding agent can be expensive in both directions because it repeatedly resends context and produces intermediate actions.

The OpenAI API Pricing page does not list either model, so the data snapshot prices cannot be independently confirmed as current public prices from the supplied official source. Before committing, developers should verify the actual account price, billing mode, availability, and any rate limits. The materials do not provide enough evidence to predict total application cost from the blended figure alone.

GPT-5 (medium) is the best first candidate for quality-sensitive development workflows, while o3 is the better test candidate for output-heavy or streaming-sensitive products. GPT-5 (medium) leads both supplied evaluation scores, with 33.7 on the Artificial Analysis Intelligence Index and 91.7 on the Artificial Analysis Math Index. It also has the lower reported blended price of $3.4375 per 1M tokens.

Choose GPT-5 (medium) for code review, technical analysis, mathematical reasoning, and applications where reducing correction work matters more than maximizing output throughput. Its lower input price also suits retrieval-heavy systems that send substantial context. That recommendation remains conditional because the current official OpenAI Models documentation does not list the model or confirm a stable API identity.

Choose o3 for interfaces where visible generation speed is central, especially if long responses are common. The snapshot reports 128.056 median output tokens per second for o3, while no corresponding GPT-5 (medium) value is available. Choose o3 for output-heavy workloads only after confirming that its lower output rate, $8 per 1M tokens, offsets its higher input rate of $2 per 1M tokens.

Do not make either model a default production dependency based on this comparison alone. The supplied official OpenAI API Pricing documentation lists neither model, and the research found no reliable community tests, failure reports, or model-specific limitations. The evidence is sufficient to prioritize experiments, not sufficient to certify availability or long-term support.

A sensible evaluation sequence is straightforward: first verify that each model can be called, then replay representative prompts, then measure quality, first-token latency, sustained output speed, retries, and token mix. If GPT-5 (medium) is unavailable or unstable, o3 becomes the practical choice regardless of the score gap. If both are available, GPT-5 (medium) is the default quality choice and o3 is the speed and output-cost challenger.

The evidence supports a cautious integration decision. The questions below address the gaps that the supplied benchmark and research materials do not resolve directly.