GPT-5.5 (xhigh) vs GPT-5.6 Terra (max): A Developer's Model Selection Guide

GPT-5.6 Terra (max) is the better default for most new developer workloads because it nearly matches GPT-5.5 (xhigh) on intelligence, leads coding, and costs less (comparison data).

These labels do not describe two equally named model IDs. gpt-5.5 is the stable GPT-5.5 model, and xhigh is its reasoning.effort setting, not a separate model. The GPT-5.5 model documentation states that medium is the default and that higher effort can add cost and latency without guaranteeing better output. gpt-5.6-terra is the stable Terra ID, while the reasoning guide describes GPT-5.6 family reasoning modes and effort values that include max. The Terra model page does not publish a complete Terra-specific effort matrix, so xhigh versus max is a configuration comparison with an evidence gap, not a clean product-tier comparison.

Release status also matters. OpenAI records GPT-5.6 Terra’s release on 2026-07-09 in the API Changelog. GPT-5.5 was released on 2026-04-23 and became available through the API on 2026-04-24, according to Introducing GPT-5.5. The GPT-5.5 model page and pricing entry remain available, and the Deprecations page does not list Terra in the supplied research. That combination suggests a newer, cheaper candidate, not an announced replacement.

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

GPT-5.6 Terra (max) wins the supplied comparison on coding, intelligence, and blended price, while GPT-5.5 (xhigh) offers stronger public benchmark documentation (comparison data; Introducing GPT-5.5).

Decision factor	GPT-5.5 (xhigh)	GPT-5.6 Terra (max)
Coding index	74.9	76.7
Intelligence index	54.8	55
Blended price per 1M tokens	$11.25	$4.50
Latency	0.3 seconds	0.3 seconds
Median output speed	No supplied value	144.252 tokens per second
Public model-specific benchmark record	Available	Not found in supplied material

The practical reading is straightforward. Terra is the stronger starting hypothesis for a new application because the supplied indices slightly favor it while its listed blended price is lower. GPT-5.5 remains important as a control candidate because OpenAI has published several task-specific results and detailed guidance for complex professional work.

OpenAI’s product pages create a mild selection tension. The current Models page foregrounds the GPT-5.6 family, yet GPT-5.5 retains direct API documentation, supported tools, and pricing. The supplied research found no official statement that GPT-5.5 is deprecated, so the product-page emphasis should not be treated as a shutdown notice.

For an existing GPT-5.5 integration, migration remains unproven. The supplied material does not include a matched migration study, a Terra-specific public benchmark, or a compatibility report showing that prompts and agent loops behave identically. Terra is therefore the default candidate for evaluation, not an automatic replacement.

GPT-5.6 Terra (max) has the stronger measured coding result, but GPT-5.5 (xhigh) has better disclosed evidence for judging complex agent work (comparison data; Introducing GPT-5.5).

The supplied Artificial Analysis snapshot places Terra at 76.7 on its coding index and GPT-5.5 at 74.9. Terra also leads the intelligence index at 55 versus 54.8. That makes Terra the better first candidate for code-centered evaluations, especially implementation, refactoring, and repository-level tasks. The gap does not establish a per-ticket win rate. The brief provides no task-level success rate, sample size, or matched test protocol, so engineering teams still need their own acceptance tests.

GPT-5.5 has a different advantage: public evidence. OpenAI reports GPT-5.5 results including 82.7% on Terminal-Bench 2.0 and 58.6% on SWE-Bench Pro. Those figures provide useful context for the model’s intended coding and agent scope, but they cannot be ranked directly against Terra’s Artificial Analysis indices. Terra’s official model page does not provide a Terra-specific public benchmark record in the supplied material.

Speed evidence is incomplete. Both models show 0.3 latency in the supplied snapshot. Terra alone has a reported median output speed of 144.252 tokens per second, while GPT-5.5 has no supplied value. Terra may be the only model with a measured output-speed result here, but the data does not establish a fair speed winner. Latency and generation speed should be tested under identical prompts, tools, output lengths, and concurrency.

Reasoning settings further complicate the label comparison. GPT-5.5 xhigh is an effort setting, while the reasoning documentation describes GPT-5.6 modes and effort controls without publishing a complete Terra-specific support matrix. The same guide explains that reasoning tokens consume the output budget and that a low max_output_tokens limit can produce an incomplete response.

Community evidence reinforces the need for task-specific testing. One coding discussion praises GPT-5.5 for architecture, debugging, planning, and long project sessions. A separate Codex discussion reports terse explanations, fragile code, and difficulty mapping domain concepts without explicit constraints. Neither discussion provides a reproducible benchmark, and no reliable Terra-specific community evidence was found.

GPT-5.6 Terra (max) is the cost winner on the supplied blended measure, but workload shape can narrow that advantage through output volume or long-context billing (comparison data; OpenAI API pricing).

The supplied 3-to-1 blended measure lists Terra at $4.50 per 1M tokens and GPT-5.5 at $11.25. That measure is useful for comparing a consistent workload mix, but it is not an invoice for every application. Actual standard short-context rates are $2 input and $12 output for Terra, versus $5 input and $30 output for GPT-5.5. Output-heavy coding agents therefore preserve Terra’s price advantage, while applications with large prompts should examine caching and context length separately.

Long-context billing can change the economics. Both model documents state that requests above 272K input tokens receive higher full-request input and output charges. A retrieval-heavy agent that repeatedly sends large repositories may therefore spend more than a short-context estimate suggests. Cached input, cache writes, Batch, Flex, and Fast mode also use different pricing rules, so the selected processing mode must be included in the cost model.

The cheaper API rate can become the more expensive engineering choice if the model needs more retries, stricter validation, or additional human review. The supplied research does not quantify those costs for Terra or GPT-5.5. That missing evidence is important because the coding-index difference is modest, and a small quality gap in a high-volume workflow can outweigh a lower token bill.

Use the charted prices as a screening signal, then measure cost per accepted task. Record input tokens, output tokens, retries, incomplete responses, tool calls, and reviewer interventions. This approach captures the operational cost that a per-token comparison cannot show.

GPT-5.6 Terra (max) should be the first production candidate for cost-sensitive coding, while GPT-5.5 (xhigh) deserves a targeted trial for evidence-heavy agent workflows (comparison data; Using GPT-5.5).

• Choose GPT-5.6 Terra (max) first for new, high-volume API workloads. Its supplied coding index is 76.7, its intelligence index is 55, and its blended price is $4.50 per 1M tokens. Those results support a cost-conscious default when the application can validate quality with its own tests.

• Choose GPT-5.5 (xhigh) for a benchmark-backed agent pilot. OpenAI positions GPT-5.5 for complex professional work, coding, tool-intensive agents, long-context retrieval, and high-execution-quality workflows in Using GPT-5.5. Its public release announcement also gives developers more task-specific evidence than Terra currently has.

• Test both models for large repositories and persistent sessions. Official documentation gives both models a 1,050,000-token context window, but requests above 272K input tokens trigger higher pricing. Terra additionally documents a maximum input of 922,000 tokens. The relevant question is not only whether the model can accept the repository, but whether the resulting agent loop remains complete, affordable, and maintainable.

• Do not select by xhigh or max alone. These labels describe reasoning configuration rather than a universal quality ranking. The reasoning guide warns that output limits can produce incomplete responses, while Using GPT-5.5 recommends explicit reuse rules, testing expectations, acceptance criteria, and stopping conditions.

A sensible rollout uses representative coding tickets, identical tool permissions, fixed acceptance tests, and logged retries. Compare accepted outcomes, not just model scores. The evidence currently supports Terra as the economic default and GPT-5.5 as the better-documented challenger. It does not establish a universal winner for every repository, agent loop, or customer-facing workflow.

GPT-5.6 Terra (max) is the safer first candidate for most cost-sensitive pilots, but GPT-5.5 (xhigh) remains the better-documented candidate (comparison data; Introducing GPT-5.5).

Terra lacks a reliable public record for real-world coding behavior, speed under matched load, failure patterns, and community preferences. The supplied research found no dependable discussion of the exact gpt-5.6-terra model. GPT-5.5 has more public evidence, but that evidence is divided between official benchmark reporting and personal accounts with no reproducible testing method.

Several selection questions remain open. The material does not show whether Terra’s coding lead survives strict repository conventions, whether its 144.252 output-speed result applies under the same workload as GPT-5.5, or whether its lower token price survives retries and review. Developers should treat the recommendation as a starting hypothesis and validate it with representative production tasks.