GPT-5 (high) vs GPT-5.4 (low): The Ultimate Performance & Pricing Comparison

GPT-5.4 (low) has the stronger measured intelligence score, while GPT-5 (high) is the safer documented choice for production developers. The Artificial Analysis Intelligence Index gives GPT-5.4 (low) a score of 39.1, compared with 34.7 for GPT-5 (high), but the available evidence is not symmetrical. GPT-5 has documented API behavior, reasoning controls, tool support, official benchmark results, and known limitations. GPT-5.4 (low) is listed in current OpenAI pricing, yet the supplied official material does not define a separate low configuration or confirm a gpt-5-4-low API alias.

The practical decision therefore depends on whether a developer values measured general capability or implementation certainty. GPT-5 (high) costs $3.4375 per 1M blended tokens, while GPT-5.4 (low) costs $5.625. Both models show 0.3 seconds latency in the supplied data, and neither has a reported median output speed. The available comparison does not establish that GPT-5.4 (low) is faster, cheaper to operate, or better at coding.

Data provided by https://artificialanalysis.ai/.

GPT-5.4 (low) leads the available aggregate intelligence measure, but GPT-5 (high) offers substantially stronger evidence for real API selection. The 39.1 versus 34.7 Artificial Analysis Intelligence Index result is the clearest capability signal in the comparison. It points toward GPT-5.4 (low) for broad reasoning tasks, but it does not show whether the advantage holds for software engineering, tool use, structured output, or agent workflows.

GPT-5 has an Artificial Analysis Coding Index score of 37.8 and a Math Index score of 94.3. GPT-5.4 (low) has no corresponding coding or math score in the supplied data. That absence is important. It prevents a defensible claim that GPT-5.4 (low) is the better coding model, even though its intelligence score is higher.

The official documentation also creates an availability distinction. OpenAI documents gpt-5 as a callable alias and describes high as the reasoning_effort=high setting, not as a separate model identifier. OpenAI's current model and pricing pages list gpt-5.4, but the supplied research does not confirm a standalone gpt-5-4-low alias or explain how the low setting is invoked. See GPT-5 for developers, GPT-5 model documentation, and OpenAI Models.

Community evidence also favors caution. A Reddit user reported useful results for small bug fixes with GPT-5, while describing weaker completion and design detail in full application generation. The same discussion mentioned possible hallucinations or incorrect changes in complex existing repositories. These observations come from an uncontrolled personal test, and no comparable community evidence was found for GPT-5.4 (low). Read Tried GPT-5 Here Are My First Impressions for the original account.

GPT-5.4 (low) is the measured performance leader only on the aggregate intelligence evidence currently available. Its Artificial Analysis Intelligence Index score is 39.1, while GPT-5 (high) scores 34.7. That gap may matter for open-ended reasoning, synthesis, and tasks that combine several kinds of judgment. It does not prove a better result for every developer workflow.

The coding evidence points in a different direction, or at least remains incomplete. GPT-5 has a Coding Index score of 37.8, but GPT-5.4 (low) has no coding score in the supplied snapshot. GPT-5 also has a Math Index score of 94.3, with no matching GPT-5.4 (low) result. A developer choosing a model for code generation, debugging, or mathematical verification therefore has stronger task-specific evidence for GPT-5, even though GPT-5.4 (low) leads the general intelligence measure.

The latency result does not separate the candidates. Both models are recorded at 0.3 seconds latency, and neither has a reported median output tokens-per-second value. The evidence is therefore insufficient to claim a speed advantage for either model. Developers should measure end-to-end response time in their own application, especially where prompt length, tool calls, retries, streaming, and output size affect the user experience.

GPT-5 also has documented controls for reasoning effort and verbosity, plus function calling, structured outputs, streaming, and custom tools. The supplied GPT-5.4 material does not confirm those capabilities specifically for the low configuration. See GPT-5 for developers and GPT-5 model documentation.

GPT-5 (high) is the lower-cost option across the supplied blended, input, and output prices. Its blended price is $3.4375 per 1M tokens, compared with $5.625 for GPT-5.4 (low). Input pricing is $1.25 versus $2.5, while output pricing is $10 versus $15. The difference becomes more consequential when an application produces long answers, code patches, tool arguments, or agent traces.

The price gap does not automatically make GPT-5 cheaper in every real workload. A more capable model could reduce retries, review effort, failed tool calls, or the amount of application-side correction required. The supplied evidence does not show whether GPT-5.4 (low) reduces those costs. It also does not provide task-level success rates for GPT-5.4 (low), so developers cannot convert its intelligence lead into a verified cost per successful task.

GPT-5.4 pricing includes separate short-context, long-context, Batch, Flex, and Fast mode prices in the official pricing material. The comparison snapshot uses the short-context standard input and output values for GPT-5.4 (low), not every available billing mode. That distinction matters for batch processing or applications with unusually large contexts.

For a known request volume and similar output behavior, GPT-5 (high) provides the clearer budget baseline. For GPT-5.4 (low), teams should run a representative evaluation before accepting a higher unit price. Consult OpenAI API Pricing and GPT-5 model documentation for the official pricing context.

GPT-5 (high) is the recommended default for production coding systems that need documented controls, known API behavior, and lower token cost. OpenAI documents gpt-5 as a callable alias, identifies high as a reasoning-effort setting, and describes support for function calling, structured outputs, streaming, and custom tools. GPT-5 also has published coding and math evidence, with Coding Index and Math Index scores of 37.8 and 94.3.

GPT-5.4 (low) is the better candidate for experiments where broad reasoning quality is the main objective and the team can validate availability independently. Its Intelligence Index score of 39.1 exceeds GPT-5's 34.7. However, the available sources do not establish that gpt-5-4-low is a stable API alias, nor do they document the low configuration's context window, parameters, tool behavior, or coding performance. That uncertainty should be treated as a selection risk, not as a minor documentation gap.

Choose GPT-5 (high) for repository maintenance, structured agent workflows, cost-sensitive products, and systems that need a documented migration path. Community reports still suggest review is necessary for complex existing codebases and full application generation, so published capability does not remove operational safeguards.

Choose GPT-5.4 (low) when its higher aggregate intelligence score matches the workload and the team can confirm the actual endpoint, configuration, behavior, and failure rate. The supplied research does not provide enough evidence to recommend it for audio or video workflows, coding superiority, or response-speed gains.

The version story also matters. OpenAI marks the fixed GPT-5 snapshot gpt-5-2025-08-07 as Deprecated, while the supplied material does not state that GPT-5.4 is discontinued. Developers using a fixed GPT-5 snapshot should plan for migration review. See OpenAI Models and GPT-5 model documentation.

GPT-5.4 (low) requires more direct validation before adoption because the supplied sources do not define its low configuration as a complete, independently documented API product. The available material supports a higher aggregate intelligence score and a higher price, but it does not answer several questions that determine production suitability.

Developers should verify the actual model identifier, the method for selecting low reasoning effort, supported tools, context behavior, output limits, and coding performance. They should also test failure recovery, repository edits, structured output compliance, and cost per successful task. These checks are especially important because no reliable community evaluation was found for GPT-5.4 (low), while the available GPT-5 community discussion is subjective and uncontrolled.

GPT-5 has more documented behavior, but its fixed snapshot carries a Deprecated label. A stable alias may still require monitoring because model availability and recommended versions can change. The evidence supports a cautious, workload-specific decision rather than a universal winner.