GPT-5.2 Codex (xhigh) vs GPT-5 mini (high): The Ultimate Performance & Pricing Comparison

GPT-5.2 Codex (xhigh) is the stronger measured general-intelligence option, while GPT-5 mini (high) is the safer cost-first choice only if its availability and coding behavior are confirmed in your environment.

The comparison has an important evidence boundary. Artificial Analysis reports an Intelligence Index of 40.1 for GPT-5.2 Codex (xhigh) and 25.3 for GPT-5 mini (high). The same dataset reports a Coding Index of 15.6 and a Math Index of 90.7 for GPT-5 mini (high), but no corresponding values for GPT-5.2 Codex (xhigh).

That missing data prevents a clean claim that Codex is better at software engineering. It supports a narrower conclusion: Codex has the stronger measured intelligence result in the supplied snapshot, but the coding-specific comparison remains unresolved.

The official evidence adds operational uncertainty. OpenAI's current model directory does not list an independent entry for either supplied model name. OpenAI's pricing page also does not list GPT-5.2 Codex or GPT-5 mini as current priced entries. Treat the names in this comparison as candidates requiring an API availability check, not as confirmed current product identifiers.

Data provided by https://artificialanalysis.ai/

GPT-5.2 Codex (xhigh) offers the better measured intelligence signal, but GPT-5 mini (high) offers a radically lower token price and the only supplied coding and math measurements.

The practical choice depends on what failure is most expensive. A system that values broad reasoning quality, complex repository navigation, or difficult multi-step planning may start with GPT-5.2 Codex (xhigh), subject to an availability test. A high-volume assistant, lightweight code transformation service, or budget-constrained workflow may start with GPT-5 mini (high), but should not assume the low price guarantees lower total cost.

The official documentation does not close the gaps. The model directory does not provide independent context, output, parameter, or tool details for these supplied names. The lack of a current official entry means deployment compatibility, alias stability, and migration risk need direct validation.

The central selection principle is therefore conditional: choose Codex for the stronger measured intelligence signal, choose Mini for cost efficiency, and run task-specific tests before treating either choice as a coding recommendation.

GPT-5.2 Codex (xhigh) has the stronger supplied general-intelligence result, but the evidence does not show whether it writes better code than GPT-5 mini (high).

The Intelligence Index gap is meaningful as a directional signal. A score of 40.1 for Codex versus 25.3 for Mini suggests that Codex may be better suited to tasks requiring several linked judgments, ambiguous requirements, or broader problem decomposition. That interpretation should remain limited to the reported index. It does not prove superior debugging, code generation, tool use, repository editing, or test repair.

The coding evidence is asymmetric. Mini has a Coding Index value of 15.6, while Codex has no supplied Coding Index value. The correct conclusion is not that Mini wins coding, and not that Codex wins coding. The correct conclusion is that the coding comparison is unmeasured in the available snapshot.

Mini also has a Math Index value of 90.7. That result may matter for developer workloads involving formal reasoning, algorithmic validation, or numerical transformations. It still cannot establish an overall performance advantage because no matching Codex value appears in the supplied data.

Reported latency is 0.3 seconds for each model, and median output tokens per second are unavailable for each model. This means the data cannot support a throughput ranking. A latency tie also does not guarantee equal interactive feel. Streaming behavior, prompt size, queueing, tool-call overhead, and output length can change the user experience, but the supplied materials do not quantify those factors.

The community evidence is similarly incomplete. The research brief found no reliable Reddit, Hacker News, or X posts with verifiable methods, environments, or sample sizes for either model. OpenAI's model documentation also does not list model-specific failure modes for these names. Developers should test repository-scale edits, compile failures, tool-call recovery, long instructions, and structured output before selecting a production default.

GPT-5 mini (high) is the clear token-price winner, but GPT-5.2 Codex (xhigh) could be cheaper in practice if it materially reduces retries, reviews, or failed tool executions.

Mini costs $0.6875 per 1M blended tokens compared with $4.8125 for Codex. Its input rate is $0.25 versus $1.75, and its output rate is $2 versus $14. Those prices make Mini the obvious first candidate for high-volume calls, short transformations, classification-style developer utilities, and workflows where a human or another system already validates the result.

The price chart cannot show the cost of failure. A cheaper model can consume more tokens through repeated attempts, generate patches that require extra repair cycles, or create review work that is more expensive than the API bill. These risks are plausible selection factors, not measured findings here. The research brief contains no verified production error rate, retry rate, token expansion rate, or human-review cost for either model.

Codex becomes economically defensible when one successful call replaces several weaker calls, especially in complex planning or repository-level tasks. That claim remains a hypothesis because the supplied benchmark data does not include a coding score for Codex, and the community research found no reproducible experience reports.

The official pricing evidence creates a second cost risk: availability. The current OpenAI pricing page lists GPT-5.3 Codex as a Codex entry but does not list GPT-5.2 Codex or GPT-5 mini. Therefore, the displayed prices should be treated as comparison data from the supplied snapshot, not as confirmed current billing terms for a live API alias.

A responsible cost test should measure successful task completion, total tokens, retries, tool calls, review time, and latency under the same workload. None of those additional operational values can be invented from this brief.

GPT-5.2 Codex (xhigh) is the better starting point for high-consequence engineering tasks, while GPT-5 mini (high) is the better starting point for cost-sensitive workloads with strong validation.

Choose GPT-5.2 Codex (xhigh) when the workflow involves ambiguous requirements, broad architectural reasoning, multi-file changes, or decisions where failed attempts create substantial engineering overhead. Its supplied Intelligence Index result of 40.1 is the strongest comparative signal available. Confirm that the exact model identifier is callable before building around it, because OpenAI's current model directory does not show an independent entry for the supplied name.

Choose GPT-5 mini (high) when request volume dominates, outputs are easy to validate, and a lower token bill matters more than an unverified general-intelligence advantage. The supplied prices favor Mini by a wide margin, and Mini is the only model with reported Coding Index and Math Index values. Those measurements make Mini testable for targeted workloads, but they do not prove that it is the better coding model overall.

Use a staged routing policy if the product can tolerate evaluation overhead. Route routine, bounded tasks to Mini. Escalate ambiguous or failed tasks to Codex only after verifying availability and measuring the actual improvement. This policy is a recommendation based on the asymmetric evidence, not a result directly tested in the supplied brief.

Do not select either model solely from the label “xhigh” or “high.” The research found no official explanation connecting those display names to a confirmed API model identifier or reasoning parameter. The official pricing documentation does not resolve that naming relationship.

The minimum acceptance test should include representative code changes, failing tests, tool calls, long repository context, output validation, retries, and total cost. The supplied materials do not provide enough evidence to skip that test.

GPT-5.2 Codex (xhigh) is not proven to be the better coding model because the supplied Coding Index includes only GPT-5 mini (high).

The unanswered questions matter more than the model labels. Developers need to confirm availability, API identifiers, context behavior, tool support, and production economics directly in their target environment.