GPT-5 vs GPT-5.5 (low): Which Model Should Developers Choose?

GPT-5.5 (low) is the stronger measured option, while GPT-5 (high) is the safer documented and cheaper API choice. Artificial Analysis reports a coding index of 60.9 for GPT-5.5 (low), compared with 37.8 for GPT-5 (high). Its intelligence index is also higher at 43.5 versus 34.7. GPT-5 remains much cheaper at $3.4375 per 1M blended tokens, compared with $11.25 for GPT-5.5 (low).

The comparison has an important qualification: GPT-5.5 (low) is a data-label used in the supplied benchmark snapshot, not a clearly documented OpenAI API product. OpenAI’s model directory does not list it as an independent model entry. OpenAI’s pricing page lists GPT-5.5, but not gpt-5-5-low.

Data provided by https://artificialanalysis.ai/.

GPT-5.5 (low) leads the available capability evidence, but GPT-5 (high) offers substantially clearer deployment evidence. The supplied data gives GPT-5.5 (low) the advantage on the coding index, 60.9 versus 37.8, and on the intelligence index, 43.5 versus 34.7. GPT-5 also has a math index of 94.3, while no corresponding GPT-5.5 (low) value is provided. That missing value prevents a complete mathematical comparison.

GPT-5 (high) has a documented API identity. OpenAI describes gpt-5 as a reasoning model for coding, reasoning, and agentic tasks, with a stable alias and a fixed snapshot documented in GPT-5 for developers. The model documentation also describes its supported inputs, outputs, tools, and parameters at GPT-5 model documentation.

GPT-5.5 (low) has weaker product evidence. The supplied research does not confirm its context window, maximum output, reasoning parameters, dedicated benchmarks, or independent API alias. The official pages describe general text and image capabilities and list GPT-5.5 pricing, but they do not establish that the low setting is directly callable as a separate model.

The practical split is clear. Choose GPT-5.5 (low) for workloads where the measured coding advantage can justify a higher bill. Choose GPT-5 when reproducibility, documented controls, and lower operating cost matter more than the newer benchmark signal. Neither model has usable output-speed data in the supplied snapshot, so perceived responsiveness cannot be ranked from throughput evidence.

GPT-5.5 (low) has the stronger measured coding profile, but the evidence does not prove that every production workflow will improve. The coding-index gap is 60.9 versus 37.8, which is large enough to matter for code generation, repository changes, and agentic development tasks. It does not identify which task families create that gap, how much prompting affects it, or whether the tested model label maps to a callable OpenAI alias.

GPT-5 has a useful counterpoint in the official evidence. OpenAI reports results for SWE-bench Verified, Aider polyglot, τ²-bench telecom, and Scale MultiChallenge in GPT-5 for developers. Those results support GPT-5’s positioning for coding, tool use, and reasoning, but they are not directly comparable with the Artificial Analysis coding index. Different tests, prompts, harnesses, and model settings can produce different rankings.

The intelligence index favors GPT-5.5 (low) at 43.5 versus 34.7. That suggests a broader capability advantage in the supplied dataset, yet it still does not answer whether the model is better for structured extraction, long-running agents, or production debugging. The supplied data gives GPT-5 a math index of 94.3 and no GPT-5.5 (low) math result. Developers with mathematical workloads therefore face an evidence gap, not a confirmed winner.

Latency is tied at 0.3 seconds. Median output tokens per second are unavailable for both models. A fast first response may therefore feel similar, while total completion time remains unknown. Developers should measure their own prompts, tool loops, output lengths, and retry rates before treating the coding-index lead as a universal productivity gain.

GPT-5 (high) is the clear cost winner, and GPT-5.5 (low) needs a measurable productivity gain to justify its premium. The supplied blended price is $3.4375 for GPT-5 versus $11.25 for GPT-5.5 (low). Input pricing is $1.25 versus $5, and output pricing is $10 versus $30. The premium affects both sides of the token bill, so verbose agent workflows face greater exposure than short-answer workloads.

The price difference matters most when requests are frequent, outputs are long, or the model is used inside repeated tool loops. A coding model that produces fewer retries, fewer corrective edits, or more reliable repository changes could offset a higher token price. The supplied evidence does not measure those operational outcomes. It provides capability indices and prices, but no cost per successful task, task completion rate, or production error rate.

GPT-5.5 pricing also carries an identity problem. OpenAI’s pricing documentation lists GPT-5.5 at $5 input and $30 output per 1M tokens for short context, but it does not list gpt-5-5-low as a separate billable model. Those prices therefore cannot be treated as definitive low-setting economics without confirming the actual endpoint and billing behavior.

GPT-5 has a more actionable pricing reference in its model documentation, including $1.25 input and $10 output per 1M tokens. It also has documented cached-input pricing. The economic conclusion can reverse only if GPT-5.5 (low) materially reduces human review or failed agent work. The supplied research does not establish that reduction, so the premium remains a decision hypothesis to test.

GPT-5 (high) is the default recommendation for documented production systems, while GPT-5.5 (low) is the targeted choice for coding-heavy experiments. GPT-5 combines a stable documented alias, published controls, clear tool support, and the lower $3.4375 blended-token price. Those properties reduce uncertainty for teams that need predictable deployment and manageable operating cost.

GPT-5.5 (low) deserves a controlled trial when repository-level coding quality is the main constraint. Its 60.9 coding index is materially above GPT-5’s 37.8 in the supplied snapshot. The trial should test representative pull requests, repair tasks, tool calls, review burden, and rollback frequency. The benchmark result alone cannot establish production superiority because the research does not disclose a dedicated low-setting benchmark method or an official model alias.

Avoid selecting GPT-5.5 (low) as a default platform dependency until OpenAI confirms the callable identifier, pricing semantics, context limits, output limits, and version lifecycle. OpenAI’s current model directory positions GPT-5.6 Sol as the preferred model for complex reasoning and coding, while omitting GPT-5.5 (low) as an independent product. That creates migration and availability risk.

GPT-5 is also not risk-free. OpenAI’s model page marks the fixed GPT-5 snapshot as Deprecated and recommends GPT-5.6. GPT-5 has documented limits around unsupported audio and video inputs, fine-tuning, and predicted outputs in the model documentation. Select it when those constraints fit the application, and record a migration plan for the alias lifecycle.

GPT-5 (high) has stronger public deployment evidence, while GPT-5.5 (low) has stronger supplied coding-index evidence. The unresolved product identity of GPT-5.5 (low) should shape the evaluation plan before any broad rollout.

The community evidence is also asymmetric. A Reddit coding report describes GPT-5 as useful for small debugging tasks, but reports weaker completeness for full applications and possible incorrect changes in complex existing codebases. The report is a single uncontrolled experience, and the research found no reliable, directly relevant community consensus for GPT-5.5 (low).

That means the cleanest selection process is staged: verify model availability, run the same task set against both candidates, measure successful outcomes, then compare total cost. The supplied research does not provide enough evidence to skip that validation.