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

GPT-5.2 Codex (xhigh) is the stronger measured intelligence choice, while GPT-5 is the safer documented and lower-cost API choice. GPT-5.2 Codex reaches an Artificial Analysis Intelligence Index score of 40.1, compared with 34.7 for GPT-5 in the supplied data. GPT-5 costs $3.4375 per 1M blended tokens, while GPT-5.2 Codex costs $4.8125. Both models show 0.3 seconds of latency, so the available data does not establish a speed advantage.\n\nThe central selection problem is evidence asymmetry. GPT-5 has public OpenAI documentation covering its API position, tools, modalities, and constraints at GPT-5 for developers and the GPT-5 model documentation. GPT-5.2 Codex lacks a dedicated entry in the current OpenAI Models directory and lacks a matching listing on OpenAI API Pricing. That absence prevents a clean capability-to-capability comparison.

GPT-5.2 Codex (xhigh) leads the supplied intelligence evaluation, but GPT-5 offers the more defensible production decision when documentation, price, and known behavior matter. The measured intelligence score is 40.1 for GPT-5.2 Codex and 34.7 for GPT-5. The supplied comparison does not report a coding score for GPT-5.2 Codex, so its apparent advantage cannot be translated directly into software engineering quality. GPT-5 has a coding index of 37.8 and a math index of 94.3, but those results do not create a like-for-like comparison.\n\nGPT-5 has a documented stable alias, supported endpoints, function calling, structured outputs, streaming, image input, and text output. Those capabilities are described in GPT-5 for developers and GPT-5 model documentation. GPT-5.2 Codex has no independently verified API alias, context limit, output limit, modality description, or official benchmark result in the supplied research. The current OpenAI Models page does not provide enough evidence to confirm its direct availability or lifecycle status.\n\nThe practical conclusion is conditional. Choose GPT-5.2 Codex for an approved environment where its higher intelligence score is meaningful and access is already verified. Choose GPT-5 for a public production integration that needs documented behavior, predictable pricing, and an auditable API contract.

GPT-5.2 Codex (xhigh) has the better measured intelligence result, but GPT-5 has the stronger evidence base for coding decisions. The Artificial Analysis Intelligence Index favors GPT-5.2 Codex at 40.1 versus GPT-5 at 34.7. That gap suggests GPT-5.2 Codex may be preferable for tasks requiring broader reasoning, planning, or complex tradeoffs. It does not prove that the model edits repositories more safely or completes coding tasks more reliably.\n\nGPT-5 has a reported Artificial Analysis Coding Index score of 37.8, while the supplied data reports no coding score for GPT-5.2 Codex. This missing value is the most important limitation in the comparison. Developers cannot infer a Codex coding lead from its intelligence score alone. Coding quality depends on repository navigation, tool use, patch discipline, test interpretation, and recovery from failed changes. The research provides no controlled community evidence for GPT-5.2 Codex on those behaviors.\n\nGPT-5 also has an Artificial Analysis Math Index score of 94.3, which supports its use for mathematical reasoning, verification, and tasks where numerical logic is central. That result is not directly comparable with a Codex score because no corresponding Codex math value is supplied.\n\nBoth models report 0.3 seconds of latency. The dataset therefore supports a tie on latency, not a claim that either model feels faster during long generations. Output speed is unreported for both models. Developers should avoid treating the equal latency figure as proof of equal end-to-end responsiveness, especially for workflows involving tools, retries, or large outputs.\n\nQualitative evidence is also uneven. A Reddit evaluation of GPT-5 describes useful small bug fixes but criticizes some complete application and UI generation for being too concise. The same post mentions possible hallucinations or incorrect modifications in complex existing codebases, but it is an uncontrolled personal test. No comparable community evidence was found for GPT-5.2 Codex.

GPT-5 is the lower-cost option, but GPT-5.2 Codex could be economically preferable if its higher measured intelligence reduces repair work. GPT-5 costs $3.4375 per 1M blended tokens, compared with $4.8125 for GPT-5.2 Codex. Its input price is $1.25 versus $1.75, and its output price is $10 versus $14. The cost difference is therefore visible in both prompt-heavy and generation-heavy workloads.\n\nThe chart below the section should be treated as a budget baseline, not a complete operating-cost model. A cheaper token price does not guarantee a cheaper feature. If GPT-5 requires more clarification, more retries, or more human review for a difficult coding task, the saved token spend may be offset by engineering time. The supplied research does not measure completion rate, retry frequency, patch correctness, or review effort for either model.\n\nGPT-5.2 Codex may justify its premium for high-value engineering work where reasoning failures are expensive and access has already been confirmed. The supplied intelligence result of 40.1 gives that argument some support, but no evidence connects the score to lower task failure rates. GPT-5 remains the better default for cost-sensitive services because its price and API behavior are documented.\n\nThe pricing comparison also has a procurement risk. OpenAI API Pricing lists a Codex product entry for GPT-5.3 Codex but does not list GPT-5.2 Codex. That page does not establish a price, billing alias, or availability for the compared Codex model. Treat any GPT-5.2 Codex cost estimate as unverified until the deployment environment provides an authoritative contract.

GPT-5 is the recommended default for documented production APIs, while GPT-5.2 Codex is a conditional choice for verified high-reasoning development workflows. GPT-5 combines a 37.8 coding index, a 94.3 math index, a $3.4375 blended price, and 0.3 seconds of latency in the supplied data. More importantly, OpenAI documents its stable alias, supported endpoints, reasoning controls, structured outputs, function calling, and image-input boundary in GPT-5 for developers and GPT-5 model documentation.\n\nChoose GPT-5.2 Codex when your organization has already verified that the model can be called in its intended environment and when the 40.1 intelligence result is more valuable than the higher $4.8125 blended price. Suitable candidates include complex planning, repository-level reasoning, and agent workflows where a stronger reasoning signal could reduce manual intervention. The evidence does not show that GPT-5.2 Codex is better at coding, faster, or safer. Those claims remain unproven.\n\nUse GPT-5 when the integration needs a stable public contract, transparent pricing, or image-and-text handling with documented limits. GPT-5 does not support audio or video input and output according to the GPT-5 model documentation, so multimedia requirements need a separate design.\n\nThere is also a lifecycle concern. The fixed GPT-5 snapshot is marked Deprecated in the model documentation, even though the GPT-5 alias remains listed. That creates migration work for systems tied to a snapshot. The current OpenAI Models directory does not provide enough information to determine whether GPT-5.2 Codex is active, replaced, or directly callable. Procurement and platform teams should resolve that question before committing to it.

GPT-5 is easier to evaluate before deployment because its public documentation exposes more of the operating contract. The available evidence for GPT-5.2 Codex is too incomplete to support assumptions about API access, limits, or coding behavior.\n\nThe most important unresolved issue is not which model has the higher intelligence score. It is whether that score predicts the specific repository and agent tasks your team cares about. GPT-5.2 Codex scores 40.1 on the supplied intelligence index, but its coding result is absent. GPT-5 scores 37.8 on the coding index, yet no paired Codex result is available.\n\nA responsible evaluation should therefore validate model access, task completion, patch correctness, tool-call behavior, retry needs, and total engineering cost in the target environment. The supplied materials establish useful selection signals, but they do not establish a complete winner for software development.