GPT-5.6 Luna (medium) vs o3: The Ultimate Performance & Pricing Comparison

GPT-5.6 Luna (medium) is the stronger default for cost-sensitive, high-volume development workloads, while o3 remains the more defensible choice for math-focused work because the available evidence is specialized rather than complete.

The measured comparison favors GPT-5.6 Luna (medium) on the Artificial Analysis Intelligence Index, blended price, input price, output price, and median output speed. GPT-5.6 Luna (medium) records 38.1 on the Intelligence Index, compared with 30.4 for o3. Its blended price is $0.45 per 1M tokens, compared with $3.5 for o3. Its median output speed is 166.287 tokens per second, compared with 128.056 for o3.

That result does not establish a universal capability winner. The dataset reports GPT-5.6 Luna (medium) at 50.7 on the Artificial Analysis Coding Index, but provides no corresponding o3 coding score. It reports o3 at 88.3 on the Artificial Analysis Math Index, but provides no corresponding GPT-5.6 Luna (medium) math score. The missing cross-model scores prevent a complete domain-by-domain ranking.

OpenAI's current documentation also creates an availability risk. The OpenAI model documentation lists gpt-5.6-luna as a stable alias, but does not clearly list gpt-5-6-luna-medium as an independent API model. The OpenAI API pricing documentation lists pricing for gpt-5.6-luna, not the exact medium slug.

GPT-5.6 Luna (medium) offers the better default tradeoff, but o3 has the only reported math result and therefore deserves a targeted evaluation before replacement.

The practical interpretation is simple. Choose GPT-5.6 Luna (medium) for assistants, code generation, classification, extraction, and other workloads where volume and operating cost matter. Choose o3 only when its mathematical behavior is central enough to justify a focused validation process and potentially higher spend.

The official positioning supports the cost-sensitive interpretation for gpt-5.6-luna. OpenAI describes that product line for cost-sensitive, high-volume workloads in the model directory. However, that statement applies to gpt-5.6-luna, not explicitly to gpt-5-6-luna-medium. The distinction matters because the exact API identity, context window, output limit, parameters, and tool restrictions remain unconfirmed.

GPT-5.6 Luna (medium) is the faster measured generator, but the available benchmark coverage is too incomplete to prove that it is better for every engineering task.

The speed result should matter most in interactive products that stream answers to users. GPT-5.6 Luna (medium) produces 166.287 median output tokens per second, while o3 produces 128.056. The reported latency is 0.3 seconds for both models, so the advantage appears in generation throughput rather than initial response latency. Users may therefore perceive Luna as more fluid during longer responses, while short requests may show little difference before generation begins.

The intelligence result points in the same direction for broad workloads. GPT-5.6 Luna (medium) scores 38.1 on the Artificial Analysis Intelligence Index, compared with 30.4 for o3. That supports using Luna as the first candidate for mixed tasks, especially when the workload combines instruction following, reasoning, and ordinary software assistance.

The coding evidence is one-sided. GPT-5.6 Luna (medium) has a Coding Index score of 50.7, but the dataset contains no o3 coding score. The result supports testing Luna for coding, but it does not establish a coding margin over o3. A team selecting for code review, debugging, or repository-level changes should treat the missing o3 score as evidence insufficiency, not as an automatic win.

The math evidence favors investigation of o3. o3 has a Math Index score of 88.3, but the dataset contains no Luna math score. That makes o3 the safer candidate for math-heavy evaluation, while leaving the size of its advantage unknown. Neither the supplied official documentation nor the community material provides reliable model-specific failure cases. The OpenAI model documentation does not specify the exact comparison slugs' context windows, output limits, parameters, or tool constraints.

GPT-5.6 Luna (medium) is the clear price choice, but deployment cost depends on workload shape, model availability, and whether o3 prevents costly quality failures.

The measured blended price is $0.45 per 1M tokens for GPT-5.6 Luna (medium) and $3.5 for o3. Input pricing is $0.2 versus $2, and output pricing is $1.2 versus $8. The difference is most important in applications that generate long answers, run many background tasks, or serve large user volumes. Output-heavy workflows should pay particular attention to the reported output prices.

A lower token price does not automatically produce a lower total engineering cost. If a model needs more retries, more validation, or more human review, the savings can disappear. The supplied material does not provide reliable community measurements for either model's failure rate, coding rework, reasoning errors, or production stability. Teams should therefore compare completed-task cost, not token price alone.

The pricing evidence also has an identity problem. The official OpenAI API pricing page lists gpt-5.6-luna and describes Standard, Batch, Flex, Fast mode, and possible regional processing charges. It does not list gpt-5-6-luna-medium. The page states that eligible models released on or after 2026-03-05 may receive a 10% regional processing surcharge, but it does not confirm whether the exact medium slug qualifies.

That uncertainty changes the procurement decision. Luna is the economic winner if the comparison slug maps to the documented gpt-5.6-luna product and the workload uses the supplied price assumptions. Before migration, verify the callable model name, billing record, regional processing eligibility, and fallback behavior. The supplied sources do not answer those questions for the exact slug.

GPT-5.6 Luna (medium) should be the default production candidate, while o3 should remain a specialized challenger for mathematics and tasks where its unmeasured strengths may matter.

Choose GPT-5.6 Luna (medium) when the application has high request volume, strict token budgets, interactive streaming, or a broad mix of development tasks. Its reported Intelligence Index is 38.1, its Coding Index is 50.7, its blended price is $0.45 per 1M tokens, and its median output speed is 166.287 tokens per second. Those indicators align with the official cost-sensitive, high-volume positioning of gpt-5.6-luna in the OpenAI model directory.

Choose o3 when mathematical reasoning is a primary acceptance criterion. The available data gives o3 a Math Index of 88.3, and no corresponding Luna math score exists. That is enough to justify an o3 evaluation for theorem-style reasoning, quantitative analysis, or workflows where a small number of incorrect answers carries a high cost. It is not enough to claim that o3 is superior across general reasoning or coding.

Use a routing strategy only if evaluation confirms distinct strengths. A practical test can route ordinary, high-volume requests to Luna and math-sensitive requests to o3. However, the supplied evidence does not provide task-level routing thresholds, failure rates, or validated production examples. Adding routing before those measurements would introduce operational complexity without proven benefit.

The first deployment gate should be API identity. The exact slug gpt-5-6-luna-medium is not clearly confirmed by the official model or pricing pages. Verify that it can be called, determine whether it maps to gpt-5.6-luna, and confirm the billed price. If that verification fails, the measured comparison remains useful as a model-family signal, but it is not sufficient as a release decision.

GPT-5.6 Luna (medium) is the safer first test for most developers, but the unanswered documentation questions should shape the evaluation plan.

The evidence supports a staged decision: verify availability, run task-specific tests, compare completed-task cost, and then decide whether o3 deserves a specialized role. The supplied research contains no reliable Reddit, Hacker News, or X discussions for either exact model, so public developer sentiment cannot resolve the gaps.

OpenAI's current pages should be checked during implementation because model aliases, pricing, and availability can change. The models page and pricing page are the relevant primary references, but neither page fully documents the exact comparison slugs in the supplied research.