GPT-5.2 Codex (xhigh) vs o3: The Ultimate Performance & Pricing Comparison

GPT-5.2 Codex (xhigh) is the stronger measured general-intelligence choice, while o3 is the cheaper and faster documented data point for developers.

The comparison is unusual because the performance dataset and the current official OpenAI pages answer different questions. Artificial Analysis reports a higher Intelligence Index for GPT-5.2 Codex (xhigh), at 40.1 compared with 30.4 for o3. The same dataset reports o3 at 128.056 median output tokens per second, while no corresponding output-speed value is available for GPT-5.2 Codex (xhigh).

The official record is less decisive. OpenAI's current model directory does not list either exact model name in the supplied material. OpenAI's pricing page also does not list a current price for either exact model. That means the measured comparison can guide model fit, but it cannot by itself establish a safe production migration path.

For a developer selecting among available endpoints, the first decision is operational: confirm that the required model identifier, access path, and billing behavior exist in the target account. If access is confirmed, GPT-5.2 Codex (xhigh) has the stronger measured quality signal. If response throughput and lower blended cost dominate, o3 has the clearer numerical advantage.

GPT-5.2 Codex (xhigh) leads the measured Intelligence Index, but o3 presents the better cost and speed profile.

These figures come from Artificial Analysis. They should be treated as comparison evidence, not as a substitute for workload testing. The Intelligence Index does not prove that GPT-5.2 Codex (xhigh) will produce better patches in every repository. The math result does not prove that o3 will be better for software maintenance. The missing values matter because they limit direct claims about GPT-5.2 Codex (xhigh)'s throughput and about comparative mathematical performance.

The official documentation adds an important constraint. OpenAI's model documentation does not provide an exact current catalog entry for either name in the supplied research. Therefore, version identity and lifecycle status remain unresolved. A team should separate model quality selection from endpoint procurement, because the best measured candidate may not be the candidate that can be reliably called.

GPT-5.2 Codex (xhigh) has the higher measured Intelligence Index, but the available evidence cannot identify which coding tasks create that advantage.

The reported Intelligence Index is 40.1 for GPT-5.2 Codex (xhigh) and 30.4 for o3. That gap is large enough to justify testing GPT-5.2 Codex (xhigh) first for tasks that combine planning, code understanding, implementation, and judgment. It is not enough to claim universal superiority, because the supplied research does not include task definitions, repository characteristics, pass criteria, or a coding-specific breakdown for the index.

The practical implication is workload sensitivity. A model with the stronger aggregate score may save engineering review time when tasks require multi-step reasoning or broad context interpretation. A faster model may still win interactive workflows where developers issue many short requests and inspect results immediately. The chart can show the measured ranking, but it cannot show how often a model needs correction, how large a patch it proposes, or whether tests pass on the first attempt.

o3 has a reported Math Index of 88.3, whereas GPT-5.2 Codex (xhigh) has no corresponding value in the supplied data. That makes o3 the only model with a documented mathematical result here, but it does not establish a direct winner because the missing counterpart prevents a like-for-like comparison.

The speed evidence favors o3 only on the metric that has a reported value. Artificial Analysis reports 128.056 median output tokens per second for o3 and no value for GPT-5.2 Codex (xhigh). Both models have reported latency of 0.3 seconds, so first-response delay does not separate them in this dataset. The missing throughput value is evidence insufficiency, not evidence of slower performance.

o3 has the lower blended-token price, but GPT-5.2 Codex (xhigh) can be cheaper for input-heavy workloads.

The supplied blended comparison places o3 at $3.5 per 1M blended tokens and GPT-5.2 Codex (xhigh) at $4.8125. That makes o3 the default cost choice when the traffic mix resembles the stated blended basis. Developers should not treat that ranking as universal, because input and output are priced differently.

GPT-5.2 Codex (xhigh) has the lower input price, at $1.75 per 1M input tokens versus $2 for o3. This matters for repository-heavy prompts, repeated file context, long instructions, and workflows that send substantial material while asking for a comparatively compact answer. In that pattern, the cheaper blended model can lose its advantage if output volume is modest and input volume dominates.

o3 has the lower output price, at $8 per 1M output tokens versus $14 for GPT-5.2 Codex (xhigh). That difference matters for agents that produce long explanations, large patches, generated tests, or repeated candidate solutions. A model with a higher capability score can become more expensive in practice if it generates verbose outputs or requires fewer but larger calls, so teams should measure total tokens per completed task rather than price alone.

Artificial Analysis supplies the numerical pricing snapshot, while OpenAI's pricing documentation does not list an exact current price for either model in the supplied research. The chart therefore supports relative economics, not a procurement quote. Before adoption, verify the account-level price, billing mode, and model identifier.

GPT-5.2 Codex (xhigh) is the better first candidate for quality-sensitive coding evaluation, while o3 is the better first candidate for cost- and throughput-sensitive evaluation.

Choose GPT-5.2 Codex (xhigh) when the central risk is incorrect reasoning across a complex codebase. Its Intelligence Index result of 40.1 exceeds o3's 30.4 in the supplied Artificial Analysis snapshot. That evidence supports a focused trial for repository navigation, architectural changes, difficult debugging, and tasks where review effort is more expensive than token spend. The recommendation remains conditional because the supplied research does not identify the benchmark composition or coding-task correlation.

Choose o3 when the workflow rewards fast generation, lower output cost, or mathematical reasoning. Its reported median output speed is 128.056 tokens per second, its blended price is $3.5 per 1M tokens, and its Math Index is 88.3. Those signals fit interactive assistants, high-volume automation, and workloads that generate substantial text. They do not prove that o3 produces better code, because no reliable coding-specific community test or direct coding benchmark is included.

Do not approve either model solely from the current official pages. OpenAI's model directory does not confirm an exact current catalog entry for either name in the supplied material. The pricing page does not confirm current prices either. The research also found no reliable community posts with verifiable methods, samples, or test environments for either model.

A sensible evaluation uses the same repositories, prompts, tools, tests, and review rubric for both candidates. Record task completion, correction count, output tokens, elapsed time, and endpoint reliability. The evidence currently supports a split decision: GPT-5.2 Codex (xhigh) for measured capability, o3 for measured economics and reported throughput. Availability must be verified separately.

GPT-5.2 Codex (xhigh) and o3 require an availability check before their benchmark and pricing differences can support a production decision.

The supplied official sources do not confirm stable aliases, exact endpoint access, or lifecycle status for either model. Treat those unknowns as procurement questions, not as performance conclusions. The following answers separate what the data supports from what remains unverified.