GPT-5 (high) vs GPT-5.6 Luna (medium): The Ultimate Performance & Pricing Comparison

GPT-5.6 Luna (medium) is the stronger measured value choice, while GPT-5 remains the better-documented engineering choice.

The available data gives GPT-5.6 Luna (medium) higher scores on the Artificial Analysis Intelligence Index and Coding Index. GPT-5.6 Luna (medium) also has a much lower blended price and a published median output speed of 166.287 tokens per second. GPT-5 has a measured latency of 0.3 seconds, but the same latency is reported for GPT-5.6 Luna (medium), so the comparison does not establish a latency advantage.

The central qualification is model identity. OpenAI’s model directory lists gpt-5.6-luna, while the comparison target is gpt-5-6-luna-medium. The materials do not confirm that these names refer to the same directly callable API model. That uncertainty matters more than a small benchmark difference because production systems need a stable endpoint, known limits, and repeatable behavior.

GPT-5.6 Luna (medium) wins the available quality and cost comparison, but GPT-5 wins on documented capabilities and operational transparency.

GPT-5 has a documented API alias, a fixed snapshot, a 400,000-token context window, and a maximum output of 128,000 tokens. OpenAI’s developer announcement also documents reasoning effort, verbosity controls, tool calling, structured outputs, and published benchmark results.

GPT-5.6 Luna has a clear official positioning for cost-sensitive, high-volume workloads in OpenAI’s model directory. However, the supplied evidence does not establish the exact capabilities of the gpt-5-6-luna-medium slug. Its context window, output limit, specific parameters, tool restrictions, and official benchmark results are not available in the brief.

For a new workload, the practical choice depends on whether measured economics or documented behavior is the primary constraint. Developers should treat GPT-5.6 Luna (medium) as a promising high-volume candidate that requires endpoint validation before commitment.

GPT-5.6 Luna (medium) has the stronger measured coding and general intelligence profile, but GPT-5 offers the broader evidence package for reasoning-heavy selection.

The Coding Index is 50.7 for GPT-5.6 Luna (medium) and 37.8 for GPT-5. That gap suggests Luna may be the better first candidate for routine code generation, code transformation, and developer-facing automation. The score alone does not prove that it will make fewer regressions in a specific repository. It also does not show how either model behaves with a particular framework, test suite, or tool orchestration design.

The Intelligence Index points in the same direction, at 38.1 for GPT-5.6 Luna (medium) and 34.7 for GPT-5. This supports using Luna for broad task throughput when the application can validate outputs. It does not answer whether Luna handles long planning chains, difficult instructions, or production-specific edge cases better, because the supplied materials do not provide those tests.

GPT-5 has one important evidence advantage: OpenAI publishes results for SWE-bench Verified, Aider polyglot, τ²-bench telecom, and Scale MultiChallenge in its developer announcement. OpenAI states that the SWE-bench result excluded 23 problems from 500 because they could not be stably passed on its infrastructure, and that the Aider evaluation used high reasoning effort. Those qualifications make the results more interpretable, but they still do not create a direct head-to-head comparison.

Math is an unresolved boundary. GPT-5 has an Artificial Analysis Math Index of 94.3, while no Luna math value is reported. Developers choosing a model for mathematical verification, symbolic reasoning, or numerical analysis therefore lack enough evidence to declare Luna the winner. GPT-5’s documented reasoning_effort options may also matter for systems that need adjustable reasoning behavior, although the brief does not provide an equivalent Luna parameter set.

The speed evidence is asymmetric. Luna reports 166.287 median output tokens per second, while GPT-5 has no corresponding value. Both models show 0.3 seconds of latency. The data therefore supports a measured Luna throughput signal, not a complete speed ranking.

GPT-5.6 Luna (medium) is dramatically cheaper on the supplied token prices, but total system cost still depends on validation, retries, output volume, and endpoint certainty.

The blended price is $0.45 per 1M tokens for GPT-5.6 Luna (medium), compared with $3.4375 for GPT-5. Luna’s input price is $0.2 and its output price is $1.2, versus $1.25 and $10 for GPT-5. These differences make Luna the natural starting point for high-volume classification, extraction, routing, and code-assistance workloads where the quality is sufficient.

The price advantage can narrow in practice if Luna requires more repair loops. A cheaper first response is not cheaper if the application must ask for repeated corrections, run additional verification calls, or send the same task to another model. The brief contains no controlled retry-rate, defect-rate, or task-success data for either model. Developers should therefore compare cost per accepted result, not token price alone, during a pilot.

GPT-5 can still be economically rational for high-value reasoning tasks. Its output price is $10 per 1M tokens, but its documented reasoning controls and published benchmark evidence may reduce integration uncertainty. That benefit is difficult to price from the supplied material because no data describes development hours, failure recovery, or maintenance effort.

Luna’s official pricing page also describes short-context, long-context, Batch, Flex, Fast mode, and possible regional-processing pricing conditions in OpenAI API Pricing. The brief does not confirm which of those conditions apply to gpt-5-6-luna-medium. A deployment that assumes the $0.45 blended figure should first verify the exact model mapping, context category, processing mode, and region.

The correct cost conclusion is specific: Luna has the better listed token economics, while GPT-5 may remain cheaper at the application level if it produces more accepted outputs with fewer corrective calls. The evidence does not establish that second comparison.

GPT-5.6 Luna (medium) should be the default pilot for high-volume development workloads, while GPT-5 should serve workloads that require documented controls or stronger evidence coverage.

Choose GPT-5.6 Luna (medium) first when the workload is cost-sensitive, traffic is high, and the application can enforce tests or structured validation. Its Coding Index of 50.7, Intelligence Index of 38.1, blended price of $0.45, and reported output speed of 166.287 tokens per second make it an attractive candidate for code assistants, batch transformations, repository search helpers, and automated drafting.

Choose GPT-5 when the integration depends on known API behavior. GPT-5 model documentation specifies the gpt-5 alias, a 400,000-token context window, a maximum output of 128,000 tokens, text and image input, and text output. The same documentation identifies function calling, structured outputs, streaming, and model limitations. Those details reduce the number of unknowns in an architecture review.

Use GPT-5 for multimodal text and image workflows, but do not select it for direct audio or video input and output because the supplied documentation says those modalities are unsupported. For complex codebase changes, add repository tests and human review. A Reddit discussion reports useful small-bug debugging experiences but also describes concerns about simplified application generation and incorrect changes in complex existing codebases. That discussion is anecdotal and not a controlled evaluation. The Reddit report should inform risk questions, not determine the model decision.

Before production use, validate whether gpt-5-6-luna-medium is an accepted API identifier or an application label for gpt-5.6-luna. Confirm context limits, output limits, tool behavior, reasoning controls, regional pricing, and failure handling. The brief does not answer these questions. That missing evidence is the main reason to pilot Luna before treating the benchmark and price advantage as a final procurement conclusion.

GPT-5 also carries a version risk. The fixed snapshot gpt-5-2025-08-07 is marked Deprecated in the supplied model documentation, even though the gpt-5 alias remains listed. Teams using a pinned snapshot should include migration planning in the selection decision.

GPT-5.6 Luna (medium) requires endpoint and capability verification before developers can make a fully confident production decision.

The evidence is strong enough to rank the available benchmark and price signals. It is not strong enough to confirm the exact API contract for the comparison target. The following questions focus on the gaps most likely to change an implementation choice.

Developers should run a task-specific pilot with acceptance tests, especially for code changes, structured outputs, tool calls, and long-context prompts. The supplied materials do not provide controlled head-to-head results, stable community consensus, or a complete failure-mode inventory for Luna.