GPT-5.5 (medium)

GPT-5.5 (medium) is a high-ranking reasoning configuration for demanding coding and professional work, but its value depends on whether quality matters more than throughput cost.

OpenAI positions GPT-5.5 as a new class of intelligence for coding and professional work, with a frontier-model role for complex professional tasks (GPT-5.5 model page). The documented API identity is gpt-5.5, with medium selected through reasoning.effort: "medium". Therefore, gpt-5-5-medium is best treated as a comparison label for a configuration, not a separate official model ID. A client that sends the comparison slug as the model name could fail routing, so implementation teams should verify the actual request shape against the official documentation.

GPT-5.5 accepts text and image inputs and returns text. Its documented surface includes Responses, Chat Completions, Batch, Structured Outputs, function calling, File Search, web search, and tools for code and computer-oriented workflows (GPT-5.5 model page). That breadth fits repository work, structured automation, and agent loops. It does not make the model a universal interface: the official output modality is text, so native audio generation is outside the documented scope.

Data provided by https://artificialanalysis.ai/. Benchmark comparisons in this review use Artificial Analysis. OpenAI’s model page does not publish GPT-5.5 benchmark scores or test methods, so the external snapshot should guide shortlisting rather than serve as a production guarantee. The practical conclusion is clear: GPT-5.5 (medium) deserves a serious coding pilot, while reliability, error rates, and total workflow cost still require local evidence.

GPT-5.5 (medium) earns its clearest case in coding, while the available evidence does not prove that it is the best general-purpose value.

The supplied Artificial Analysis snapshot ranks GPT-5.5 (medium) 18 of 202 on the coding index at 71.5. That placement is a strong selection signal for difficult engineering tasks. The intelligence index is less decisive as a buying argument because a high general score does not reveal how the model edits a real repository, handles tools, or recovers from a bad intermediate action. The two indexes also use different comparison pools, so their positions should not be combined into a single universal verdict.

Decision lens	What GPT-5.5 (medium) offers	What the evidence does not prove
Coding-heavy professional work	A high relative coding position and a documented reasoning configuration.	Patch success, edit precision, or recovery quality in a developer’s repository.
Mixed knowledge work	A strong general intelligence position in the supplied snapshot.	That it beats every nearby model on research, analysis, or tool use.
Cost-sensitive production	A premium option that may earn its place if it reduces costly review or retries.	That its higher unit cost is recovered in total workflow spend.
Long-context workflows	OpenAI documents a very large context capability (GPT-5.5 model page).	The data snapshot leaves the context field blank, and billing behavior needs endpoint-level verification.

Adjacent models clarify the tradeoff without turning this into a head-to-head review. Gemini 3.5 Flash (high) and Muse Spark 1.1 (xhigh) sit close on the supplied intelligence or coding measures while carrying lower blended prices. Claude Opus 5 (Adaptive Reasoning, Low Effort) provides a closer price reference and a higher intelligence score, but a lower coding score in the same snapshot. Those comparisons make GPT-5.5 a quality-led choice, not an obvious default for every request.

The model catalog currently recommends newer GPT-5.6 variants, yet GPT-5.5 remains in the full catalog and does not appear on OpenAI’s current deprecation list (model catalog, deprecation list). That means the model is viable to evaluate, but the evidence does not support a claim that it is the safest long-term default.

GPT-5.5 (medium) should be treated as a quality-first coding option whose benchmark position supports difficult work, not as proof of autonomous reliability.

The coding result, 18 of 202 at 71.5, places GPT-5.5 (medium) near the leading edge of the supplied coding field. For a developer, that changes the first question from whether the model belongs on a shortlist to which workflow can make its reasoning useful. Repository migrations, multi-file changes, architectural decisions, and tool-mediated debugging are plausible candidates because errors in those tasks have high review cost.

Rank position still has a narrow meaning. It describes relative performance inside a tested pool. It does not report how often a patch passes, whether edits preserve existing behavior, or how consistently the model follows repository conventions. OpenAI’s official model page gives no GPT-5.5 benchmark scores or test methodology (GPT-5.5 model page). The supplied materials also contain no reliable, reproducible measurements for coding error rate, task completion, or tool-call recovery. Those are important gaps, not minor footnotes.

Medium reasoning is the documented default for this configuration. OpenAI also exposes low, high, and xhigh effort settings, which gives teams a way to test deeper reasoning against different operating points (GPT-5.5 model page). The evidence does not quantify how changing effort affects quality, latency, or spend. A production team should therefore compare settings on its own representative tasks rather than assume that more effort always produces better patches.

The snapshot reports 0.3s to first token, but it does not report median output tokens per second. The first signal supports responsive interaction, yet it cannot predict the time needed to finish a long code review or generate a large patch. A Reddit user describes a positive GPT-5.5 workflow inside Claude Code for multi-repository projects and terminal-style agent work (community report). That account is useful as a feasibility signal, but it discloses no reproducible task set, sample size, speed method, or error measurement. Production confidence therefore remains an open evaluation task.

GPT-5.5 (medium) is expensive for routine generation, and its premium needs a quality or workflow reason to pay for it.

The supplied pricing snapshot lists $11.25 per 1M blended tokens, with standard input at $5 and output at $30. That structure makes verbose outputs, repeated agent turns, and failed retries more consequential than short prompts. The model can still be economical when one careful response replaces several weaker attempts or reduces human review, but the provided data does not measure either effect. Buyers should treat those benefits as a hypothesis to test, not as an established return.

OpenAI lists lower-cost Batch and Flex modes, along with a higher-priced Fast mode, and documents separate billing behavior for very long inputs (OpenAI pricing, GPT-5.5 model page). The long-input rule matters for codebase snapshots, large logs, and document-heavy prompts. A workload that looks affordable under ordinary context can move into a different cost band when the prompt becomes very large. The source material does not provide a workload calculator or a production token mix, so a precise monthly estimate would be speculation.

Nearby models sharpen the decision. Gemini 3.5 Flash (high) lists a $3.375 blended price and 270.227 median output tokens per second, while its coding score is 70.1. Muse Spark 1.1 (xhigh) lists a $2 blended price and a 71.3 coding score. Those entries create credible lower-cost baselines for throughput-heavy or price-sensitive work. GPT-5.5 keeps an argument for tasks where coding quality, tool behavior, or review reduction matters more than the lowest token rate. Similar scores do not establish equal reliability, integrations, or output quality, so the comparison should end in a controlled pilot.

The cost conclusion flips under different workloads. It is easier to justify GPT-5.5 for complex changes, ambiguous requirements, and agent workflows with expensive mistakes. It is harder to justify for routine extraction, simple transformations, bulk drafting, or requests where output volume dominates. The missing evidence is total cost per successful task, which requires local success and token logs.

GPT-5.5 (medium) is worth choosing for high-stakes coding workflows that can convert stronger benchmark standing into fewer review cycles.

Choose GPT-5.5 (medium) when the work combines difficult code changes with repository context, structured outputs, function calls, or other documented tools. OpenAI lists those capabilities alongside image input, File Search, web search, Code Interpreter, Hosted Shell, Computer Use, and MCP support (GPT-5.5 model page). The combination gives engineering teams a broad workflow surface, especially when the model must inspect artifacts, plan changes, and return machine-readable results.

Keep a cheaper model as the default when requests are repetitive, output-heavy, or easy to verify. The supplied snapshot shows nearby models with lower blended prices and close intelligence or coding results. That makes GPT-5.5 a targeted escalation model in many systems: route hard tasks to it, then keep simpler work on a lower-cost baseline. The exact routing policy should follow observed success per dollar, which the supplied materials do not provide.

Choose GPT-5.5 (medium) for	Prefer another default for
Complex repository changes where review time is expensive.	High-volume generation where output throughput dominates.
Agent workflows that need structured results and tool calls.	Simple extraction, formatting, or classification with clear checks.
Ambiguous engineering work that benefits from deliberate reasoning.	Native audio output, which is outside the documented output modality.

Implementation should call gpt-5.5 and set the reasoning effort explicitly, rather than sending gpt-5-5-medium as a model ID. OpenAI’s catalog currently highlights newer GPT-5.6 variants, but its deprecation page does not announce GPT-5.5 as deprecated (model catalog, deprecation list). That supports a current pilot, not a promise of indefinite product priority.

The best acceptance test measures patch correctness, regression rate, tool-call recovery, instruction adherence, completed-response time, and token spend on representative work. The external ranking can select the candidate. It cannot replace that test.

GPT-5.5 (medium) needs a short implementation check before production because its documented identity and benchmark evidence are easy to confuse.

Before shipping, confirm the request uses the official gpt-5.5 ID and sets reasoning.effort to medium when that is the intended configuration (GPT-5.5 model page). Keep the comparison slug in internal metadata only. This avoids a routing failure caused by treating a configuration label as a model name.

Next, separate documented capabilities, external benchmark comparisons, and local task results. The official page does not supply benchmark methods, while the snapshot does not measure repository-level reliability. Record successful patches, review changes, retries, tool failures, and token usage during the pilot. Do not infer production quality from a first-token latency figure alone, because output throughput is not reported.

Finally, test billing with ordinary prompts and very long code or document inputs. OpenAI documents different pricing modes and higher charges for very long sessions (OpenAI pricing). The exact cost impact depends on the endpoint and workload. This is an evidence gap the supplied materials cannot close, so finance and engineering should validate it together before setting a default route.