GPT-5.5 (medium) vs GPT-5 mini (high): Which Model Should Developers Choose?

GPT-5.5 (medium) is the safer default for serious software work, while GPT-5 mini (high) is attractive only when its lower measured cost and strong math result outweigh substantial evidence gaps.

The data snapshot gives GPT-5.5 (medium) a coding index of 71.5, compared with 15.6 for GPT-5 mini (high). It also gives GPT-5 mini (high) a math index of 90.7, although GPT-5.5 (medium) has no corresponding value in the snapshot. The blended price is $11.25 for GPT-5.5 (medium) and $0.6875 for GPT-5 mini (high) per 1M tokens.

The most important qualification is operational, not numerical. OpenAI’s GPT-5.5 model page documents gpt-5.5 and explains that medium is a reasoning-effort setting. OpenAI’s model directory and pricing page do not currently provide equivalent confirmation for gpt-5-mini or GPT-5 mini (high). That makes this comparison useful for capability and economics, but incomplete as a production-readiness decision.

GPT-5.5 (medium) offers the stronger evidence-backed choice for coding and general intelligence, while GPT-5 mini (high) offers a much lower listed benchmark cost and a higher math score.

Decision factor	GPT-5.5 (medium)	GPT-5 mini (high)	Practical reading
Coding index	71.5	15.6	GPT-5.5 has the clear measured advantage for software tasks
Intelligence index	50.4	25.3	GPT-5.5 has the broader measured capability signal
Math index	No value provided	90.7	GPT-5 mini has a useful math signal, but the comparison is incomplete
Blended price per 1M tokens	$11.25	$0.6875	GPT-5 mini is the economical option in the supplied data
Input price per 1M tokens	$5	$0.25	GPT-5 mini is cheaper for input-heavy traffic
Output price per 1M tokens	$30	$2	GPT-5 mini is cheaper for generation-heavy traffic
Median latency	0.3 seconds	0.3 seconds	The supplied latency result is a tie

The capability gap matters most when a model must inspect unfamiliar code, plan changes across files, preserve constraints, or recover from failed attempts. The cost gap matters most when requests are simple, high-volume, and easy to validate.

The official evidence also differs sharply. OpenAI documents GPT-5.5 as a reasoning model, with a current snapshot of gpt-5.5-2026-04-23, configurable reasoning effort, and a broad tool surface. By contrast, the supplied research found no dedicated official entry for GPT-5 mini. Developers should therefore treat GPT-5 mini (high) as a candidate requiring verification, not as an equally documented production target.

GPT-5.5 (medium) is the stronger measured option for coding tasks, and the gap is large enough to affect architecture rather than just leaderboard position.

The supplied Artificial Analysis data gives GPT-5.5 (medium) a coding index of 71.5 and GPT-5 mini (high) a coding index of 15.6. That difference suggests a meaningful separation in tasks where the model must transform requirements into reliable code. Typical examples include repository-level changes, debugging, implementation planning, and edits that require preserving existing behavior. The index does not prove success on every codebase, but it is strong evidence against treating the two models as interchangeable coding engines. Data provided by https://artificialanalysis.ai/

The general intelligence index points in the same direction. GPT-5.5 (medium) scores 50.4, while GPT-5 mini (high) scores 25.3. For developers, that broader gap may appear in requirements analysis, tool selection, multi-step investigation, and decisions where the correct answer depends on several constraints rather than one isolated transformation. The data does not identify which individual tasks produced the scores, so the exact failure boundary remains unknown.

GPT-5 mini (high) does have the only supplied math result, at 90.7. That makes it a plausible specialist candidate for tightly scoped mathematical workloads, especially when outputs can be checked by deterministic code. It does not establish that GPT-5 mini is better for general reasoning, because GPT-5.5 has no math score in the supplied snapshot and the two models were not shown with a common math comparison.

Latency does not resolve the choice. Both models are listed at 0.3 seconds median latency, while median output speed is unavailable for both. The evidence therefore supports a quality-versus-cost decision, not a speed-based one. Artificial Analysis supplies the comparative data, but the research found no reproducible community measurement for stable coding speed, error rate, or real-world failure frequency.

The official capability evidence also favors GPT-5.5. The GPT-5.5 documentation lists structured outputs, function calling, file search, web search, prompt caching, code interpreter, hosted shell, computer use, and MCP support. The research does not confirm the same tool support for GPT-5 mini, so developers should test the actual endpoint before designing around it.

GPT-5 mini (high) is dramatically cheaper in the supplied pricing comparison, but its lower token price does not automatically produce a lower system cost.

The data lists GPT-5 mini (high) at $0.6875 per 1M blended tokens, compared with $11.25 for GPT-5.5 (medium). Its input price is $0.25 versus $5, and its output price is $2 versus $30. For short, predictable requests with strong validation, those prices make GPT-5 mini the obvious first candidate for high-volume automation. Data provided by https://artificialanalysis.ai/

The economic conclusion can reverse when weaker task performance creates more work around the model. A model that needs repeated retries, larger prompts, human review, fallback routing, or post-generation repair can consume engineering and infrastructure resources that token pricing does not show. The supplied research does not quantify retry rates or repair costs for either model, so no exact break-even point can be calculated from the evidence.

GPT-5.5 has a specific long-context cost risk. OpenAI’s model page states that inputs above 272K tokens trigger higher pricing for the full session. That matters for repository analysis, large document workflows, and agents that repeatedly carry extensive state. A workflow can therefore be expensive even when the visible request count is low. Developers using GPT-5.5 should control context growth, summarize stable state, and avoid sending unchanged material repeatedly.

GPT-5.5 also has documented pricing modes beyond Standard. The OpenAI pricing page lists Batch and Flex prices, as well as Fast mode pricing. These alternatives may change the operational tradeoff for asynchronous jobs or latency-sensitive traffic, but the research does not verify a corresponding GPT-5 mini price or availability profile.

The biggest cost uncertainty is model identity. OpenAI’s model directory and pricing page do not list gpt-5-mini in the supplied research. A low benchmark price is useful only if the endpoint, configuration, limits, and service status can be verified in the target account. Until then, GPT-5 mini’s apparent savings remain conditional.

GPT-5.5 (medium) should be the default for production coding agents, while GPT-5 mini (high) should be evaluated as a constrained low-cost specialist.

Choose GPT-5.5 (medium) for repository-level coding, debugging unfamiliar systems, agentic terminal workflows, structured implementation tasks, and requests where a wrong answer is costly. Its coding index of 71.5 versus 15.6 provides the clearest evidence in the comparison. Its documented tool and API support also reduces integration uncertainty. OpenAI’s GPT-5.5 model page identifies gpt-5.5 as the callable model and medium as the reasoning-effort configuration.

Choose GPT-5 mini (high) when the task is narrow, the output is easy to validate, and token volume dominates the budget. Candidate uses include simple transformations, classification, repetitive extraction, and math-oriented operations where the supplied 90.7 math index is relevant. Do not assume that the math result transfers to coding or broad reasoning. The supplied evidence does not show that it does.

A sensible routing policy is to start with GPT-5 mini only after confirming that the endpoint exists and behaves as expected. Escalate difficult requests to GPT-5.5 when validation fails, the task spans multiple files, or the model must resolve conflicting requirements. This policy is an engineering recommendation based on the measured capability and price differences, not a measured production routing result.

The main unresolved question is whether “GPT-5 mini (high)” describes a currently callable model and whether “high” is a supported configuration. The research found no official confirmation. The OpenAI deprecations page also does not provide a clear statement that GPT-5.5 has been deprecated, so GPT-5.5 has the stronger documented lifecycle position in the supplied evidence. Developers should verify model listing, request success, rate limits, and pricing in their own account before committing either model to a long-lived interface.

GPT-5.5 (medium) is easier to validate before implementation because its model ID and reasoning configuration are documented by OpenAI.

The central preflight issue is not whether GPT-5 mini looks attractive on paper. Its supplied price and math score are attractive. The issue is whether the named model and the high setting can be called consistently in the environment that will run the application. The research does not answer that question.

Developers should also decide whether the workflow rewards first-pass quality or lowest token cost. The supplied data strongly separates those objectives. GPT-5.5 leads on coding and intelligence indices, while GPT-5 mini leads on the supplied blended, input, and output prices. Neither model has a measured output-speed result in the snapshot, and both have the same supplied latency value of 0.3 seconds.

For a production decision, benchmark the actual task distribution, record retries and human corrections, and verify the exact API model ID. The research provides enough evidence for a directional choice, but not enough to predict application-level reliability or total cost.