GPT-5.6 Terra (low) vs GPT-5 mini (high): Which Model Should Developers Choose?

GPT-5.6 Terra (low) is the stronger measured choice for coding and general intelligence, while GPT-5 mini (high) is dramatically cheaper. The available data gives Terra a coding index of 58.1 versus 15.6 for GPT-5 mini (high), and an intelligence index of 40.5 versus 25.3. GPT-5 mini (high) costs $0.6875 per 1M blended tokens, compared with $4.500000000000001 for Terra. Artificial Analysis provides the comparison data.

The larger issue is model identity. OpenAI’s model documentation does not list GPT-5.6 Terra (low), gpt-5-6-terra-low, GPT-5 mini (high), or gpt-5-mini as independently documented entries in the supplied research. Developers should therefore treat this comparison as useful for measured selection, but incomplete for production availability, limits, and API behavior.

GPT-5.6 Terra (low) offers the clearest measured advantage for software development, but GPT-5 mini (high) offers the clearest economic advantage. The coding index gap is 42.5 points, and the intelligence index gap is 15.2 points, both favoring Terra according to Artificial Analysis.

Decision factor	Better-supported choice	What the evidence says
Coding-oriented work	GPT-5.6 Terra (low)	Coding index: 58.1 versus 15.6
General intelligence	GPT-5.6 Terra (low)	Intelligence index: 40.5 versus 25.3
Math evidence	GPT-5 mini (high) has the only listed score	Math index: 90.7 for GPT-5 mini (high); Terra has no listed value
Blended token cost	GPT-5 mini (high)	$0.6875 versus $4.500000000000001 per 1M blended tokens
Measured output speed	GPT-5.6 Terra (low)	123.223 median output tokens per second; GPT-5 mini (high) has no listed value
Request latency	Tie	Both are listed at 0.3 seconds
Official model documentation	Neither variant is fully confirmed	The supplied OpenAI model page does not provide dedicated entries for these displayed variants

This is not a complete capability ranking. GPT-5 mini (high) has a math score of 90.7, while Terra has no math score in the supplied data. That asymmetry prevents a fair mathematical comparison. The benchmark also does not establish how either model behaves on tool use, long-context tasks, structured output, agent loops, or production error handling. OpenAI’s model documentation likewise does not provide variant-specific context windows, output limits, or API parameters for the compared names.

GPT-5.6 Terra (low) is the safer measured bet for coding-heavy workflows because its coding index leads GPT-5 mini (high) by 42.5 points. Artificial Analysis reports a coding index of 58.1 for Terra and 15.6 for GPT-5 mini (high).

That gap matters most when the model must produce useful code with limited human correction. A higher coding score supports choosing Terra for repository changes, implementation drafts, debugging explanations, and code-generation tasks where rework consumes engineering time. The benchmark does not prove that every coding task will show the same difference, so the practical conclusion is directional rather than absolute.

Terra also leads the listed intelligence index by 15.2 points, with 40.5 compared with 25.3. This makes Terra the stronger candidate for mixed workloads that combine coding with planning, interpretation, and multi-step reasoning. The supplied research does not include verified failure patterns or community testing for either displayed variant, so it cannot identify the exact task boundary where Terra’s advantage disappears. OpenAI’s model page also does not publish variant-specific benchmarks or capability limits.

GPT-5 mini (high) has one important evidence advantage: it is the only compared model with a listed math index, at 90.7. Terra has no listed math value in the supplied data. That result should not be interpreted as proof that GPT-5 mini (high) is better at all mathematical work, because the comparison lacks a corresponding Terra measurement and provides no test methodology in the supplied brief.

Speed evidence is similarly incomplete. Terra records 123.223 median output tokens per second, while GPT-5 mini (high) has no listed output-speed value. Both models show 0.3 seconds of latency. Developers should read this as evidence that Terra can stream quickly in the measured setup, not as proof that it will produce faster completed responses for every prompt. Output length, retries, tool calls, and application orchestration can change user-perceived speed, but the supplied materials do not quantify those effects.

GPT-5 mini (high) is the clear token-cost choice, but GPT-5.6 Terra (low) may be economically preferable when better first-pass output reduces review and repair work. Artificial Analysis lists blended pricing of $0.6875 per 1M tokens for GPT-5 mini (high) and $4.500000000000001 for Terra.

The price difference is substantial enough to favor GPT-5 mini (high) for high-volume, low-risk tasks. Examples include simple classification, short transformations, routine extraction, and other workloads where a lower-cost response can be checked cheaply. The supplied evidence does not provide task-level accuracy, retry rates, or correction costs, so it cannot calculate a true cost per successful result.

Terra’s listed token prices are also higher at both input and output levels. Input costs are $2 for Terra and $0.25 for GPT-5 mini (high) per 1M tokens. Output costs are $12 and $2 respectively. This distinction matters for coding agents, because generated patches, explanations, and test plans can create substantial output volume. A model that needs fewer retries may narrow the economic gap, but no retry or acceptance-rate data is available.

The cost conclusion also depends on whether the compared names are callable. OpenAI’s pricing documentation lists pricing for gpt-5.6-terra, but the supplied research does not find a separate official price for gpt-5-6-terra-low. It does not list gpt-5-mini pricing either. Therefore, the Artificial Analysis prices are useful for comparing observed records, but they do not confirm current billing terms for these exact displayed variants.

Batch, Flex, and Fast mode pricing is documented for gpt-5.6-terra, including different rates from Standard pricing, but the supplied sources do not establish that those modes apply to the (low) display name. Developers should verify the exact API identifier and billing mode before committing to a forecast.

GPT-5.6 Terra (low) should be the default shortlist candidate for coding-intensive development, while GPT-5 mini (high) should be the default shortlist candidate for cost-sensitive volume. The measured coding and intelligence scores favor Terra, and the listed blended price favors GPT-5 mini (high). Artificial Analysis supplies those measurements.

Choose GPT-5.6 Terra (low) when the workflow is judged by implementation quality, debugging usefulness, or the amount of human correction. Terra is also the better-supported option for mixed reasoning and coding because it leads the intelligence index. Its listed output speed of 123.223 median output tokens per second may help interactive applications, although GPT-5 mini (high) has no corresponding speed value for comparison.

Choose GPT-5 mini (high) when request volume dominates the decision and the task can tolerate weaker measured coding performance. Its $0.6875 blended price makes it suitable for inexpensive first passes, routing, preprocessing, and tasks where developers already have strong validation. The math index of 90.7 is a reason to test it for math-focused workloads, but the absence of a Terra math score prevents a winner declaration.

A staged architecture is the most defensible choice when the application contains both routine and high-value tasks. Use GPT-5 mini (high) for inexpensive screening, then route difficult coding or reasoning cases to GPT-5.6 Terra (low). This is a design recommendation based on the measured quality and price gap, not a result directly tested in the supplied research.

The main production blocker is identity and availability. OpenAI’s model documentation does not confirm the compared display names as dedicated current models. OpenAI’s pricing documentation does not provide exact variant pricing for both names. Before adoption, developers should confirm the callable model ID, supported parameters, context limits, output limits, and current billing behavior. The supplied materials provide no reliable answer for those points.

GPT-5.6 Terra (low) and GPT-5 mini (high) both require an availability check before production adoption because the supplied official documentation does not independently identify either displayed variant. OpenAI’s model documentation lacks dedicated entries for the compared names, and OpenAI’s pricing documentation lacks exact prices for both displayed variants. The FAQ below separates measured evidence from unresolved implementation risk.