GPT-5 (high) vs GPT-5.1 (high): The Ultimate Performance & Pricing Comparison

GPT-5.1 (high) is the stronger default for developers because it leads GPT-5 on coding and intelligence scores without a listed price or latency disadvantage. The Artificial Analysis snapshot gives GPT-5.1 a coding index of 49.4 versus GPT-5 at 37.8, while the intelligence index is 36.9 versus 34.7. Mathematics is effectively close, with GPT-5 at 94.3 and GPT-5.1 at 94.0. Data provided by https://artificialanalysis.ai/.

The recommendation needs an important qualification. OpenAI’s current model directory does not provide a dedicated gpt-5-1 entry in the supplied research, so the comparison has stronger measured evidence for performance than for API lifecycle, context limits, modalities, or configuration details. Developers choosing GPT-5.1 should therefore validate availability and behavior in their own account before committing production architecture.

GPT-5.1 (high) wins the overall comparison because its coding index advantage is large, its intelligence index is higher, and its listed cost and latency match GPT-5. The coding gap is 49.4 versus 37.8, which is the clearest signal for software teams evaluating code generation, refactoring, debugging, and repository-level assistance. The intelligence difference is narrower at 36.9 versus 34.7, so it supports the recommendation without defining it alone.

GPT-5 retains a narrow mathematics lead at 94.3 versus 94.0. That result matters for workloads where mathematical accuracy is the primary acceptance criterion, but it does not outweigh GPT-5.1’s coding advantage for general development work. The data does not provide output-speed measurements for either model, while latency is tied at 0.3 seconds. Developers should not treat the comparison as evidence that GPT-5.1 generates tokens faster.

The official evidence is asymmetric. OpenAI positions GPT-5 for coding, reasoning, and agentic tasks and documents its API details, while the supplied research found no equivalent official model entry for GPT-5.1. That documentation gap is a selection risk, not proof that GPT-5.1 is unavailable or inferior outside the measured evaluation.

GPT-5.1 (high) is the better performance choice for most software engineering workflows because its coding index reaches 49.4 compared with GPT-5 at 37.8. The practical implication is not that every generated patch will be better. It is that GPT-5.1 has the stronger measured basis for tasks where code quality determines acceptance, such as implementing features, understanding unfamiliar code, and repairing failures.

The advantage should change how teams evaluate the models. A coding workflow often includes several dependent steps: interpreting a request, locating relevant code, proposing a change, and producing a patch that survives tests. A higher coding score supports selecting GPT-5.1 for that chain, but the supplied data does not identify which subtask creates the difference. Teams should still test their own repository patterns, especially if correctness depends on undocumented conventions or broad context.

GPT-5 remains marginally ahead in mathematics, scoring 94.3 against 94.0. That narrow lead can justify GPT-5 for a narrowly defined mathematical workload, but it is weak evidence for a broad engineering recommendation. OpenAI reports GPT-5 results on SWE-bench Verified, Aider polyglot, τ²-bench telecom, and Scale MultiChallenge, yet the supplied research contains no equivalent official benchmark disclosure for GPT-5.1. Cross-source benchmark equivalence is therefore not established.

Latency does not separate the models because both are listed at 0.3 seconds. Output speed is unavailable for both models. Any claim that one feels faster remains unsupported by the supplied snapshot.

GPT-5.1 (high) is the better value when coding quality drives business value because GPT-5 and GPT-5.1 share the same listed prices. The blended price is $3.4375 per 1M tokens for each model, input tokens cost $1.25 per 1M tokens for each, and output tokens cost $10 per 1M tokens for each. There is no price-based reason to choose GPT-5.1 over GPT-5, but there is also no price penalty for choosing the higher-scoring coding model.

The equal price does not guarantee equal total cost. A model that produces a correct patch in fewer review cycles can be cheaper for a team, while a model that requires repeated prompts, manual edits, or rollback work can cost more even at the same token rate. The supplied data does not measure completion length, retry frequency, review effort, or production error rates. Those are evidence gaps that require an application-level pilot.

GPT-5 may still be the economical choice for a mathematics-first service if its 94.3 mathematics index translates into fewer corrections than GPT-5.1 at 94.0. That conclusion is conditional because the benchmark does not describe the exact task mix or the operational cost of errors. OpenAI’s model documentation lists GPT-5’s current input, cached-input, and output prices, but the supplied research does not establish a comparable official pricing record for GPT-5.1. The data brief shows equal prices, while the official documentation evidence remains incomplete.

GPT-5.1 (high) should be the first candidate for coding-heavy applications, provided the model is available and passes a representative validation run. Its 49.4 coding index is materially higher than GPT-5’s 37.8, and its 36.9 intelligence index also leads 34.7. The equal blended price of $3.4375 per 1M tokens removes the most obvious adoption objection.

GPT-5 is the safer choice when documented API behavior is more important than the measured coding lead. OpenAI’s GPT-5 documentation identifies the gpt-5 alias, describes a 400,000-token context window, and lists a maximum output of 128,000 tokens. It also documents text and image input, text output, tool calling, structured outputs, streaming, reasoning effort, and verbosity controls. The same documentation marks the fixed snapshot gpt-5-2025-08-07 as Deprecated, so choosing GPT-5 does not remove lifecycle concerns.

GPT-5.1 is not automatically the right choice for every product. The supplied research found no reliable official specification for its context window, maximum output, modalities, API parameters, or lifecycle status. It also found no reliable community posts specifically attributable to gpt-5-1. Developers should treat GPT-5.1 as a performance-led candidate whose operational contract still needs verification.

A sensible decision rule is straightforward: choose GPT-5.1 for repository work, code generation, and engineering agents; choose GPT-5 for workflows that require the documented GPT-5 API surface or where mathematics is the primary measured priority. Neither choice is supported by evidence about output speed, and neither should be selected for audio or video API processing based on the supplied GPT-5 documentation.

GPT-5.1 (high) requires a short availability and behavior check before production adoption because its official specification is not established in the supplied research. OpenAI’s model directory does not list gpt-5-1 in the provided evidence, and OpenAI’s pricing page does not list a standard price for it. That does not prove that the model cannot be called. It means the supplied sources cannot confirm whether it is available, hidden, migrated, or represented under another identifier.

GPT-5 also carries lifecycle and scope constraints. The fixed GPT-5 snapshot is marked Deprecated, while the documented model supports text and image input but not audio or video input and output. A team using either model should verify the exact identifier, response format, tool behavior, context handling, and migration policy in its deployment environment.

Community evidence does not resolve the uncertainty. A Reddit discussion about GPT-5 reports useful small-bug debugging but criticizes some full application and UI generation outputs. The post is a subjective, uncontrolled test, and no comparable GPT-5.1 evidence was found. Treat those observations as hypotheses for testing, not as stable model properties.