GPT-5.5 (low)

GPT-5.5 (low) looks attractive to developers who value coding performance, but its public product identity is not sufficiently documented for an automatic production choice.

The model ranks 44 of 578 on the Artificial Analysis Intelligence Index and 41 of 202 on the Artificial Analysis Coding Index. Those positions place GPT-5.5 (low) among the stronger models in the supplied evaluation set, with coding ranking slightly ahead of general intelligence ranking.

That result supports a practical thesis: GPT-5.5 (low) is worth testing for software tasks, structured reasoning, and developer workflows where model quality matters more than the lowest token cost. It does not support a stronger claim that the model is a clearly available, independently specified OpenAI product.

OpenAI’s official models page describes current model capabilities and API access, including text and image input, text output, multilingual ability, and vision support. The page does not identify gpt-5-5-low as a standalone entry. OpenAI’s pricing page lists gpt-5.5, but not gpt-5.5-low as a separate billing model or stable API alias.

For developers, that distinction matters. A favorable score can justify a controlled evaluation. It cannot replace confirmation of the exact model name, limits, parameters, and billing behavior in the target account.

GPT-5.5 (low) offers a credible quality choice for coding, but adjacent models make its value proposition harder to defend on cost alone.

The supplied data places GPT-5.5 (low) close to several neighboring models. Motif 3 (Beta) and Kimi K2.6 score higher on the Artificial Analysis Intelligence Index, while DeepSeek V4 Pro (Reasoning, High Effort) and Muse Spark score lower. GPT-5.5 (low) also scores higher on the supplied coding index than DeepSeek V4 Pro and Muse Spark, while Motif 3 and Kimi K2.6 score higher.

Decision factor	GPT-5.5 (low)	What the adjacent data suggests
General intelligence	Strong position at 43.5, ranked 44 of 578	Some nearby models score higher, so GPT-5.5 (low) is not the automatic quality leader
Coding	Strong position at 60.9, ranked 41 of 202	Coding is the clearest reason to evaluate GPT-5.5 (low)
Cost	$11.25 per 1M blended tokens	DeepSeek V4 Pro is much cheaper in the supplied comparison data
Operational certainty	Official standalone documentation is missing	Alias and limits require direct verification

The official OpenAI models page currently positions GPT-5.6 Sol as the preferred model for complex reasoning and coding. It does not list GPT-5.5 (low) as an independent product entry. That weakens the case for adopting GPT-5.5 (low) without an internal compatibility check.

The conclusion is narrow but useful. GPT-5.5 (low) deserves a place on a developer shortlist because its coding result is strong. It should compete through measured task quality, not through assumed availability, assumed feature parity, or price leadership.

GPT-5.5 (low) is best suited to coding evaluations where repository-level correctness matters more than raw generation speed.

The model’s Artificial Analysis Coding Index score is 60.9, with a ranking of 41 of 202. That is the strongest direct evidence in the brief for a developer-facing advantage. The general intelligence result is also solid at 43.5, ranked 44 of 578, so the coding result does not appear isolated from broader reasoning ability within the supplied data.

A developer should therefore test GPT-5.5 (low) on tasks such as bug diagnosis, implementation changes, code explanation, test generation, and multi-file edits. These are recommendations based on the coding and intelligence rankings, not claims that the brief contains task-level measurements for each workflow. The evaluation should score accepted patches, test outcomes, regression rates, and reviewer corrections.

The available latency data shows 0.3 seconds to first token. Median output tokens per second is not reported. That makes responsiveness partly assessable, but sustained streaming behavior remains unknown. A model that starts quickly may still be a poor fit for long code generation if its output rate or completion reliability is weak.

The evidence also leaves important performance questions unanswered. OpenAI does not publish a dedicated gpt-5-5-low context window, maximum output limit, API parameter set, or official benchmark result in the cited materials. The OpenAI models page describes general model capabilities, but it does not establish those limits for this specific variant.

The practical verdict is conditional. GPT-5.5 (low) is promising for coding-heavy evaluation suites, especially if first-token latency helps interactive use. It is not yet possible to guarantee repository-scale performance, long-context behavior, or output stability from the supplied evidence.

GPT-5.5 (low) is reasonably priced only when its quality advantage reduces enough downstream engineering work to offset cheaper alternatives.

The supplied blended price is $11.25 per 1M tokens, based on input pricing of $5 and output pricing of $30 per 1M tokens. Those figures make output-heavy workflows especially important to model. Code generation, long explanations, repeated test repairs, and agent loops can consume more output than a simple question-answering workload.

The closest-model data shows a sharp cost contrast. DeepSeek V4 Pro (Reasoning, High Effort) is listed at $0.54375 per 1M blended tokens, with input at $0.435 and output at $0.87. Its intelligence score is 43.1 and its coding score is 58.7. That comparison means GPT-5.5 (low) cannot win a default procurement decision on benchmark quality and price simultaneously. Its justification must come from better results on the buyer’s own tasks, stronger integration confidence, or lower human review cost.

OpenAI’s pricing page lists the standard gpt-5.5 prices, including short-context input at $5 and output at $30 per 1M tokens. The page does not list gpt-5.5-low independently. Therefore, the data brief’s price should be treated as the comparison record for this model entry, while the production billing path still needs confirmation.

Caching, batch processing, and flexible execution may change the economics for gpt-5.5, according to the official pricing page. The brief does not establish whether those modes apply identically to gpt-5.5-low.

Choose GPT-5.5 (low) for quality-sensitive workflows with meaningful review costs. Avoid making it the default for bulk classification, low-risk transformations, or large agent fleets until task-level accuracy and actual billing are verified.

GPT-5.5 (low) is worth a controlled pilot for coding-intensive products, but developers should require API identity and task-level evidence before committing.

The strongest case is a product that needs capable code assistance, values a 0.3-second first-token latency, and can absorb a blended cost of $11.25 per 1M tokens. The coding ranking of 41 of 202 gives this model a defensible reason to enter that pilot. A useful pilot should compare accepted changes, test pass rates, human correction time, and failure recovery against at least one lower-cost adjacent model.

The weakest case is a high-volume workload where every generated token carries direct cost and the task does not need advanced coding judgment. DeepSeek V4 Pro offers a much lower supplied price while remaining close on the intelligence and coding indexes. That does not prove it will perform better for a particular application, but it makes GPT-5.5 (low) a poor cost-first choice without local validation.

A second rejection condition is operational ambiguity. The official OpenAI models page does not list GPT-5.5 (low) as an independent product. The official OpenAI pricing page lists gpt-5.5, not gpt-5-5-low, as a separate documented billing entry. The brief provides no reliable evidence for the dedicated alias, context window, output ceiling, or variant-specific parameters.

The recommendation is to proceed in stages: confirm that the exact alias is callable, record the returned model identifier, run a representative coding suite, measure total review cost, and then compare production economics. If any of those checks fail, select a documented model instead.

GPT-5.5 (low) is therefore a measured bet. Its benchmark position makes testing rational. Its price and documentation gaps make unverified adoption hard to defend.

GPT-5.5 (low) should be treated as an evaluation candidate until OpenAI documents its standalone identity, limits, and billing behavior more clearly.

The answers below separate what the supplied data supports from what remains unknown. The OpenAI models page and OpenAI pricing page are the relevant primary references.