GPT-5.5 (medium) vs o3: Which OpenAI Model Should Developers Choose?

GPT-5.5 (medium) is the stronger default for documented developer workflows, while o3 remains the lower-cost option with a measured speed advantage. The available evidence does not support a universal winner for every task. GPT-5.5 (medium) records an Artificial Analysis Intelligence Index of 50.4, compared with 30.4 for o3. The same data gives GPT-5.5 (medium) a Coding Index score of 71.5, but provides no comparable o3 coding score. It gives o3 a Math Index score of 88.3, but provides no comparable GPT-5.5 mathematics score. That asymmetry matters: the data supports a broad capability advantage for GPT-5.5 (medium), but not a complete domain-by-domain ranking.

GPT-5.5 (medium) is also the better-documented product. OpenAI identifies gpt-5.5 as a reasoning model and describes it as a frontier model for complex professional work, including coding. The documented configuration is reasoning.effort: "medium", not a separate model identity. Developers should therefore treat gpt-5-5-medium as a comparison label, not as the official API model ID. OpenAI GPT-5.5 model page

The practical choice depends on whether the application values capability and integration breadth or token economics. GPT-5.5 (medium) has documented support for structured outputs, function calling, file search, web search, prompt caching, code execution, hosted shell, computer use, and MCP. The supplied material does not establish equivalent current support for o3. OpenAI GPT-5.5 model page

GPT-5.5 (medium) offers the stronger evidence-backed general choice, but o3 can be the rational pick for cost-sensitive workloads with a clear mathematical or throughput requirement.

Decision factor	GPT-5.5 (medium)	o3	What it means
Artificial Analysis Intelligence Index	50.4	30.4	GPT-5.5 (medium) leads the supplied broad capability measure
Artificial Analysis Coding Index	71.5	Not provided	Coding evidence is incomplete rather than directly comparable
Artificial Analysis Math Index	Not provided	88.3	o3 has a strong supplied mathematics signal, but GPT-5.5 has no matching value
Blended price per 1M tokens	$11.25	$3.5	o3 costs less for the supplied blended mix
Input price per 1M tokens	$5	$2	o3 is cheaper for input-heavy traffic
Output price per 1M tokens	$30	$8	o3 is substantially cheaper for generation-heavy traffic
Median output speed	Not provided	128.056 tokens per second	The supplied speed evidence exists only for o3
Latency	0.3 seconds	0.3 seconds	The supplied latency snapshot is tied

The most important comparison is not simply “newer versus older.” GPT-5.5 (medium) has a documented current model page, a documented reasoning setting, and an explicit professional-work positioning. o3 appears in the supplied benchmark snapshot, but the current OpenAI model directory and pricing page supplied for this review do not list its current API status or price. OpenAI model directory OpenAI pricing

That creates a deployment distinction. GPT-5.5 (medium) has a clearer operational path from documentation to implementation. o3 may still be attractive in an existing system, but a new integration should first verify whether the intended endpoint, alias, and billing path remain available. The provided material does not answer those questions.

GPT-5.5 (medium) has the stronger supplied general capability signal, but the evidence cannot prove that it is faster or more accurate on every developer task.

The Artificial Analysis snapshot places GPT-5.5 (medium) at 50.4 on its Intelligence Index and o3 at 30.4. That gap is large enough to influence model selection for work that combines planning, code understanding, tool decisions, and multi-step execution. It should not be read as a guaranteed win on every prompt. Index construction, task mix, and evaluation methodology are not explained in the supplied brief, while OpenAI’s model page does not publish GPT-5.5 benchmark methods or scores. OpenAI GPT-5.5 model page

The coding comparison is especially incomplete. GPT-5.5 (medium) has a Coding Index score of 71.5, yet no o3 Coding Index value appears in the data. That means the evidence supports choosing GPT-5.5 (medium) when coding is a central requirement, but it does not establish a measured coding margin between the models. A developer building an editor agent, repository refactoring workflow, or terminal agent should run a task set that matches the target codebase before treating the chart as a production forecast.

The mathematical comparison has the opposite limitation. o3 has a Math Index score of 88.3, while the GPT-5.5 value is absent. This makes o3 a plausible candidate for workloads dominated by formal mathematics, symbolic reasoning, or verification-heavy calculations. The supplied evidence still does not show whether that advantage transfers to production software tasks.

Speed evidence also favors caution. o3 is reported at 128.056 median output tokens per second, while GPT-5.5 has no supplied value. Both models show 0.3 seconds of latency in the snapshot. The available data therefore cannot establish an end-to-end winner for interactive applications. A fast generation rate may matter less than tool-call delays, reasoning depth, retries, or the length of the final answer.

One community report describes using GPT-5.5 inside Claude Code for multi-repository work, file editing, and terminal-style agent workflows. The author did not provide a reproducible benchmark, task set, sample count, or speed measurement, so it is useful as workflow context rather than performance proof. Reddit workflow report

o3 is the clear price winner in the supplied snapshot, while GPT-5.5 (medium) can justify its premium only when better task completion reduces downstream work.

The blended price is $3.5 per 1M tokens for o3 and $11.25 for GPT-5.5 (medium). o3 also costs $2 per 1M input tokens and $8 per 1M output tokens, compared with $5 and $30 for GPT-5.5 (medium). Those differences make o3 the safer first choice for high-volume classification, batch reasoning, and applications where responses are short and easy to validate.

Price alone can mislead in agentic software. A cheaper model becomes more expensive if it needs extra retries, produces edits that require manual repair, or triggers additional tool calls. The supplied materials do not contain comparable error rates, retry counts, or total task-cost measurements. No evidence therefore shows that GPT-5.5 (medium) recovers its higher token price in real coding workflows.

GPT-5.5 (medium) also has a specific long-context cost boundary. Inputs above 272K tokens cause the full session to use 2x input pricing and 1.5x output pricing under Standard, Batch, and Flex. That rule can reverse an apparently acceptable architecture for repository-wide analysis, large document review, or persistent agent context. GPT-5.5 pricing details OpenAI pricing

Caching and processing mode change the economics further. GPT-5.5 (medium) lists Standard short-context cached input at $0.50 per 1M tokens, while Batch and Flex list short-context input at $2.50 per 1M tokens and output at $15. Fast mode lists short-context input at $12.50 and output at $75. The supplied pricing page does not list current o3 prices, so o3’s snapshot price should not automatically be treated as a currently available OpenAI invoice rate. OpenAI pricing

For a production estimate, compare cost per accepted task, not only cost per token. That calculation requires measurements absent from the brief, including successful completion rate, correction effort, tool-call count, and typical context length.

GPT-5.5 (medium) is the recommended default for new developer agents that need current documentation, broad tool support, and strong general capability evidence.

Choose GPT-5.5 (medium) for repository-level coding, professional automation, structured application outputs, and workflows that combine files, web search, code execution, or computer interaction. OpenAI documents these capabilities and identifies the model as a reasoning system for complex professional work. The official integration uses gpt-5.5 with reasoning.effort: "medium"; it does not use gpt-5-5-medium as a separate model ID. OpenAI GPT-5.5 model page

Choose o3 when token cost is the primary constraint, the workload is generation-heavy, or mathematical reasoning is more important than broad current integration documentation. Its supplied Math Index value is 88.3, its blended price is $3.5 per 1M tokens, and its reported median output speed is 128.056 tokens per second. Those signals make it worth testing for solver-style services and high-volume workloads.

Keep o3 behind a compatibility check for new systems. The supplied OpenAI model directory does not list o3, and the supplied pricing page does not list a current o3 price. The material also does not confirm a stable alias, endpoint, context window, output limit, or tool matrix. OpenAI model directory OpenAI pricing

Do not choose based on the benchmark chart alone for safety-critical coding automation. The brief contains no comparable coding accuracy, failure-rate, regression-rate, or production latency study. Run representative tasks with fixed prompts, identical tool permissions, validation tests, and recorded retries. The best selection may change if o3’s low price produces substantially more correction work, or if GPT-5.5’s broader capabilities reduce orchestration complexity.

The final implementation decision is therefore conditional. GPT-5.5 (medium) is the safer platform choice from the available documentation. o3 is the stronger economics choice when its actual availability is verified and its task quality meets the acceptance threshold.

GPT-5.5 (medium) should be evaluated against the application’s acceptance tests before any model migration becomes permanent.

The comparison has a clear directional result, but several operational facts remain unresolved. OpenAI’s current materials document GPT-5.5 in detail, while the supplied o3 materials do not establish current availability or equivalent limits. That documentation gap is itself a selection risk for developers who need predictable deployments.