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

GPT-5.6 Luna (low) is the better default for cost-sensitive production workloads, while GPT-5 remains the more documented choice for demanding reasoning workflows. The data brief gives Luna a coding index of 44.2 versus GPT-5 at 37.8, and a blended price of $0.45 versus $3.4375 per 1M tokens. GPT-5 still has a math index of 94.3, while Luna has no corresponding value in the comparison data.\n\nOpenAI describes GPT-5 as a reasoning model for coding, reasoning, and agentic tasks, with support for function calling, structured outputs, streaming, and custom tools (GPT-5 for developers). OpenAI describes gpt-5.6-luna as a model for cost-sensitive, high-throughput workloads (OpenAI Models). Those positions point to different selection priorities.\n\nThe comparison is not a clean generational benchmark. GPT-5 has published official evaluations, while GPT-5.6 Luna (low) does not. GPT-5 has a documented 400,000-token context window and 128,000-token maximum output, while Luna’s corresponding limits were not found in the reviewed documentation (GPT-5 model documentation; OpenAI Models). The strongest conclusion is therefore operational: Luna looks better for economical throughput and coding according to the supplied data, but GPT-5 offers more evidence for specialized reasoning decisions.

GPT-5.6 Luna (low) wins the practical default decision when application volume and unit economics dominate, but GPT-5 wins the evidence and capability-documentation decision.\n\n| Decision area | Better-supported choice | Why it matters | |---|---|---| | Blended cost | GPT-5.6 Luna (low) | The supplied blended price is $0.45 per 1M tokens, compared with $3.4375 for GPT-5. | | Coding index | GPT-5.6 Luna (low) | Luna scores 44.2, while GPT-5 scores 37.8 in the supplied data. | | General intelligence index | GPT-5 | GPT-5 scores 34.7, while Luna scores 33.3. | | Math evidence | GPT-5 | GPT-5 has a reported math index of 94.3; Luna has no reported value. | | Output speed evidence | GPT-5.6 Luna (low) | Luna reports 166.399 median output tokens per second; GPT-5 has no reported value. | | API documentation | GPT-5 | GPT-5 documents reasoning effort, verbosity, tools, context, and output limits. | | Lifecycle confidence | GPT-5.6 Luna (low) | The reviewed pages do not show a replacement or shutdown notice, while GPT-5’s fixed snapshot is Deprecated. | \nGPT-5’s official documentation also states that the stable alias is gpt-5, while the fixed snapshot is gpt-5-2025-08-07 (GPT-5 model documentation). The reviewed materials found no official gpt-5-high model ID. “High” refers to reasoning_effort=high, not a separate API model (GPT-5 for developers).\n\nLuna’s official alias is gpt-5.6-luna, and the reviewed materials found no official gpt-5-6-luna-low API ID (OpenAI Models; OpenAI Pricing). That naming distinction matters because application configuration should use the documented alias, not the comparison label.

GPT-5.6 Luna (low) has the stronger measured coding result, but GPT-5 has the stronger documented case for high-effort reasoning and math tasks. Luna’s coding index is 44.2 versus GPT-5 at 37.8. For software teams, that gap suggests Luna may be a strong candidate for code generation, routine implementation, and high-volume repository assistance. It does not prove that Luna will make fewer regressions in a specific codebase.\n\nGPT-5’s official results cover SWE-bench Verified at 74.9%, Aider polyglot at 88%, τ²-bench telecom at 96.7%, and Scale MultiChallenge at 69.6% (GPT-5 for developers). OpenAI notes that the SWE-bench result excluded 23 of 500 problems that could not be passed reliably on its infrastructure, and that the Aider result used high reasoning effort. These qualifications make GPT-5’s evidence useful, but they do not create a direct apples-to-apples comparison with Luna.\n\nGPT-5 also has a reported math index of 94.3, while Luna has no math value in the data brief. That makes GPT-5 easier to justify for math-heavy validation, formal reasoning, and workflows where an explicit reasoning signal matters. Luna’s official page does not provide benchmark results, detailed context limits, maximum output limits, or model-specific parameters (OpenAI Models).\n\nThe supplied latency value is 0.3 seconds for each model. Luna reports 166.399 median output tokens per second, while GPT-5 has no reported output-speed value. A responsible latency decision therefore needs application testing. The materials do not establish whether Luna feels faster across streaming, long prompts, tool calls, or high reasoning effort.

GPT-5.6 Luna (low) is dramatically cheaper per token, but GPT-5 can still be economically rational when one successful response replaces repeated retries or manual correction. The supplied blended price is $0.45 per 1M tokens for Luna and $3.4375 for GPT-5. Luna’s listed input price is $0.2, while GPT-5’s is $1.25. Luna’s listed output price is $1.2, while GPT-5’s is $10.\n\nThe price advantage matters most in workloads with predictable prompts, short feedback loops, and high request volume. It is especially attractive for routing, extraction, routine code changes, test generation, and other tasks where a lower-cost response is valuable even if a human or automated check remains in the loop. The coding index of 44.2 also means Luna’s price advantage is not paired with a weaker coding score in the supplied comparison.\n\nGPT-5 becomes easier to defend when the workflow depends on its documented reasoning controls, structured outputs, custom tools, or published math and agentic-task evidence (GPT-5 for developers). A failed generation can create review work, broken builds, rollback effort, or another model call. The research brief does not provide failure-rate, retry-rate, token-consumption, or engineering-cost data, so it cannot prove the total cost of ownership for either model.\n\nLuna pricing also varies by service mode and context category. The official pricing page lists Standard, Batch, Flex, and Fast mode prices, with separate short-context and long-context rates, but the reviewed page does not define the token boundary between those categories (OpenAI Pricing). Teams should therefore benchmark their real prompt distribution before treating the lowest listed rate as the final bill.\n\nThe key reversal condition is simple: Luna is cheaper when it completes the task at an acceptable quality level. If a workflow requires repeated repair, strict reasoning validation, or manual intervention, GPT-5’s higher token price may be offset by fewer downstream actions. The available materials do not measure that tradeoff.

GPT-5.6 Luna (low) should be the first model tested for high-throughput coding workloads, while GPT-5 should remain the escalation path for reasoning-sensitive or poorly specified tasks.\n\nChoose GPT-5.6 Luna (low) for routine code generation, code transformation, test scaffolding, classification, extraction, and other workloads where the team can enforce quality with tests or validators. Its supplied coding index is 44.2, its blended price is $0.45 per 1M tokens, and its reported median output speed is 166.399 tokens per second. OpenAI positions the model for cost-sensitive, high-throughput workloads (OpenAI Models).\n\nChoose GPT-5 when the workflow needs a documented reasoning control, stronger available math evidence, or advanced tool-oriented behavior. OpenAI documents reasoning_effort values of minimal, low, medium, and high, plus verbosity values of low, medium, and high (GPT-5 for developers; GPT-5 model documentation). GPT-5 also supports text and image input, structured outputs, function calling, streaming, and custom tools.\n\nDo not select solely by the comparison labels. Use gpt-5.6-luna for Luna and gpt-5 or its documented fixed snapshot for GPT-5, because the reviewed sources did not verify gpt-5-6-luna-low or gpt-5-high as official API model IDs (OpenAI Models; GPT-5 model documentation).\n\nGPT-5’s fixed snapshot carries migration risk because OpenAI marks gpt-5-2025-08-07 as Deprecated and recommends GPT-5.6 (GPT-5 model documentation). Luna has less public evidence, not necessarily weaker behavior. The reviewed materials found no reliable community tests for Luna, and no direct benchmark comparison against GPT-5. Teams should run representative prompts, compile checks, tool-call validation, and human review before final routing.

GPT-5.6 Luna (low) is the stronger starting point for teams that prioritize economical throughput and coding performance. The supplied data gives Luna a coding index of 44.2, a blended price of $0.45 per 1M tokens, and a reported median output speed of 166.399 tokens per second. GPT-5 remains the better-supported escalation option when published reasoning evidence, math coverage, and documented controls matter more than token cost.\n\nThe evidence is asymmetric, so developers should treat the comparison as a routing recommendation rather than a definitive model ranking. GPT-5 has official benchmark disclosures and extensive API documentation. Luna has a lower price and a higher supplied coding index, but its official materials do not provide benchmark results or detailed failure cases.\n\nCommunity evidence also needs restraint. A Reddit author reported useful results for small bug fixes but criticized GPT-5’s completeness and design detail in full application and user-interface generation. The post describes a personal, uncontrolled test and does not establish a general result (Tried GPT-5 Here Are My First Impressions). No reliable community evaluation for Luna was found in the reviewed material.