GPT-5.6 Terra (max) vs GPT-5 nano (high): The Ultimate Performance & Pricing Comparison

GPT-5.6 Terra is the safer production choice for general developer workloads, while GPT-5 nano is the low-cost specialist whose current status remains unclear.

The data shows a clear split. GPT-5.6 Terra records an Artificial Analysis Intelligence Index of 55, compared with 19.9 for GPT-5 nano. GPT-5.6 Terra also records a Coding Index of 76.7, although GPT-5 nano has no comparable coding score in the supplied data. GPT-5 nano records a Math Index of 83.7, while GPT-5.6 Terra has no reported value for that evaluation.

The product evidence points in the same direction for deployment confidence. OpenAI documents the stable model ID gpt-5.6-terra, its APIs, tools, modalities, and pricing on the GPT-5.6 Terra model page. The current OpenAI model directory does not list GPT-5 nano. That omission does not prove that GPT-5 nano cannot be used, but it makes the model harder to validate for a new production integration.

Data provided by https://artificialanalysis.ai/

GPT-5.6 Terra wins general-purpose selection, but GPT-5 nano remains compelling for narrow, price-sensitive mathematical workloads.

GPT-5.6 Terra offers a documented path through the Responses API, Chat Completions API, and Batch API. Its official page also lists structured outputs, function calling, file search, web search, image input, and other tools. The reasoning models guide documents reasoning controls and explains how reasoning tokens affect output budgets.

GPT-5 nano is much cheaper in every supplied pricing measure. That advantage matters for high-volume classification, filtering, scoring, or lightweight mathematical calls. It does not compensate for missing official evidence about its current model ID, context limits, API parameters, or production lifecycle. The pricing directory lists gpt-5.4-nano, not gpt-5-nano, and explicitly does not establish the older model's price.

GPT-5.6 Terra is the stronger default for mixed reasoning and coding tasks, while GPT-5 nano should be treated as an unverified mathematical alternative.

The Intelligence Index gap is the most important general signal: GPT-5.6 Terra scores 55, and GPT-5 nano scores 19.9. That gap suggests a meaningful difference for tasks that combine instructions, context, tool decisions, and multi-step reasoning. It does not prove that Terra wins every task. The supplied data has no direct coding score for GPT-5 nano and no mathematics score for Terra, so the comparison is incomplete by design.

GPT-5 nano's Math Index of 83.7 is the strongest isolated result in the dataset. Developers building a constrained mathematical evaluator may therefore want to test nano directly instead of assuming that the broader intelligence score predicts every domain. The evidence is insufficient to determine whether that math result transfers to symbolic manipulation, explanation quality, or application-level reliability.

GPT-5.6 Terra reports a median output speed of 144.252 tokens per second. GPT-5 nano has no supplied output-speed value, so Terra is the only model with a measurable speed result here. Both models show reported latency of 0.3 seconds in the data brief, which means the available latency evidence does not separate them.

Official documentation gives Terra a wider operational surface. The GPT-5.6 Terra model documentation lists text and image input, text output, structured outputs, function calling, file search, web search, and several hosted tools. The reasoning guide also documents reasoning.mode, reasoning.effort, and reasoning.context. Comparable official details for GPT-5 nano were not found in the current model directory.

GPT-5 nano is dramatically cheaper on the supplied price measures, but its lower price only matters if its output is reliable enough for the job.

The blended price is $0.1375 for GPT-5 nano and $4.500000000000001 for GPT-5.6 Terra per 1M tokens. Input pricing is $0.05 for nano and $2 for Terra, while output pricing is $0.4 for nano and $12 for Terra. Those figures make nano the natural candidate for workloads dominated by inexpensive, repeatable calls.

Price alone can reverse the decision when a weak answer triggers retries, human review, validation calls, or downstream corrections. The data brief does not provide failure rates, retry rates, or task-level quality thresholds, so no exact break-even point can be calculated. Developers should measure total workflow cost, not only model invoice cost.

GPT-5.6 Terra also has documented pricing conditions that make long-context architecture important. The Terra model page states that requests above 272K input tokens receive higher input and output pricing. The same page documents a context window of 1,050,000 tokens, but the data brief does not provide a comparable GPT-5 nano context value. That asymmetry prevents a fair long-context cost comparison.

Batch and Flex pricing are documented for Terra in the OpenAI pricing documentation. No equivalent current price was found for GPT-5 nano. Developers should not substitute the listed gpt-5.4-nano price for nano, because the research brief explicitly treats those as different models.

GPT-5.6 Terra should be the primary choice for production systems that need documented capabilities, broad reasoning, and coding support.

Choose GPT-5.6 Terra for agentic applications, code generation and review, tool-using assistants, document workflows, and products where model availability must be easy to verify. OpenAI identifies Terra as a model that balances intelligence and cost in its Models documentation. The OpenAI API Changelog records its release on 2026-07-09 and identifies the stable model ID as gpt-5.6-terra.

Choose GPT-5 nano only when the workload is narrow, the cost target is strict, and your team can validate access and behavior independently. Its Math Index of 83.7 makes it worth testing for mathematical scoring or other bounded tasks. Its lower prices also suit large volumes of simple requests. The official evidence does not establish its current context window, maximum output, API parameters, stable alias, or deprecation status.

For a new integration, Terra carries less lifecycle uncertainty. The OpenAI deprecations page does not currently list gpt-5.6-terra, while the current official pages do not provide a confirmed lifecycle record for GPT-5 nano. That does not make Terra risk-free. It means the available evidence supports a clearer procurement and integration decision.

A sensible selection process is to use Terra as the baseline, then test nano against the exact mathematical or low-complexity tasks that drive volume. Keep nano only if its measured success rate avoids enough retries and review work to preserve its cost advantage.

GPT-5.6 Terra is easier to evaluate before adoption because OpenAI publishes a current model page, while GPT-5 nano lacks equivalent official detail.

The unanswered questions matter more than a simple leaderboard. GPT-5 nano has a strong Math Index and a much lower listed data price, but the research brief found no current official listing for the exact model. GPT-5.6 Terra has stronger general and coding evidence, but the supplied data does not show whether it is better at mathematics. Developers should treat the comparison as a routing decision based on workload, evidence quality, and operational risk.