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

GPT-5.6 Terra (Non-reasoning) is the stronger default for coding-heavy applications, while GPT-5 remains the safer evidence-backed choice for math-focused workloads. The Artificial Analysis coding index places Terra at 52.3 and GPT-5 at 37.8, giving Terra a clear measured advantage in the supplied comparison. GPT-5 leads the Artificial Analysis intelligence index by a narrow margin, 34.7 versus 34. GPT-5 also has the only reported math result, at 94.3.\n\nGPT-5.6 Terra (Non-reasoning) is listed as an OpenAI Frontier model and remains available through the documented API surface, according to OpenAI Models. GPT-5 remains callable through its stable alias, but the fixed snapshot gpt-5-2025-08-07 is marked Deprecated in GPT-5 model documentation.\n\nThe comparison therefore has two different kinds of uncertainty. GPT-5 has more published capability evidence, while Terra has stronger coding and output-speed measurements in the supplied data. Data provided by https://artificialanalysis.ai/.

GPT-5.6 Terra (Non-reasoning) offers the better measured coding profile, but GPT-5 offers the broader documented evidence base. Terra reaches 52.3 on the coding index, compared with GPT-5 at 37.8. That gap is large enough to matter for code generation, repository changes, test creation, and implementation tasks, although the index alone cannot establish success on a particular codebase.\n\nGPT-5 leads the intelligence index by 0.7000000000000028 points, with 34.7 versus Terra at 34. The narrow gap does not support a broad claim that GPT-5 is generally smarter. It does support a cautious conclusion that the supplied intelligence measurement is effectively close, with GPT-5 holding the measured edge.\n\nGPT-5 has a reported math index of 94.3. Terra has no corresponding math score in the supplied data, so developers cannot claim a math winner from this comparison. The missing score is a decision risk, especially for workloads involving formal reasoning, symbolic manipulation, or numerical verification.\n\nThe product status also changes the practical choice. OpenAI’s developer documentation positions GPT-5 for coding, reasoning, and agentic tasks. OpenAI Models positions GPT-5.6 Terra as a balance between intelligence and cost. The supplied evidence makes Terra the performance-led choice and GPT-5 the evidence-led, lower-cost choice.

GPT-5.6 Terra (Non-reasoning) is the better measured choice for coding throughput, while GPT-5 has the only measured math result. Terra’s coding index is 52.3, compared with GPT-5 at 37.8. For developers, that difference can translate into a higher chance that a single request produces a usable patch, test, or implementation outline. It does not prove that Terra will modify every existing repository safely.\n\nGPT-5.6 Terra also reports a median output speed of 122.922 tokens per second. GPT-5 has no supplied median output-speed value, so the evidence cannot quantify a speed advantage against GPT-5. The latency measurement is 0.3 seconds for each model, which means faster streaming output should not be confused with faster request initiation. A user may see Terra produce text sooner after generation starts, while the initial response timing remains tied in the supplied data.\n\nGPT-5’s measured math index of 94.3 creates a meaningful boundary around the coding conclusion. Terra may be the better engineering assistant in the supplied coding comparison, but no reported Terra math score supports choosing it for math-intensive production work. Developers should treat that gap as missing evidence, not as evidence that Terra performs poorly.\n\nOpenAI reports GPT-5 results on SWE-bench Verified, Aider polyglot, τ²-bench telecom, and Scale MultiChallenge in GPT-5 for developers. Those official results establish a documented evaluation record for GPT-5, but they do not create a directly comparable official record for Terra. No supplied source provides a controlled Terra benchmark, a reproducible community test, or a stable consensus about Terra’s coding behavior.

GPT-5 is the lower-cost option under every supplied standard token price shown in the comparison. Its blended price is $3.4375 per 1M tokens, compared with GPT-5.6 Terra at $4.500000000000001. GPT-5 also costs less for input tokens, at $1.25 versus $2, and output tokens, at $10 versus $12.\n\nThe practical cost decision depends on how much rework each model causes. A cheaper request is not necessarily cheaper delivery if it produces incomplete patches, requires repeated prompts, or increases review time. The supplied community evidence describes GPT-5 as useful for small debugging and modification tasks, while also reporting concerns about simplified output for complete applications and incorrect changes in complex existing codebases. Those observations come from an uncontrolled Reddit post, so they should inform testing rather than determine the result. See Tried GPT-5 Here Are My First Impressions.\n\nGPT-5.6 Terra may justify its higher token price when its coding advantage reduces retries or human correction. The supplied data does not measure completion rate, retry count, review effort, or cost per accepted change. Developers therefore cannot calculate total engineering cost from the token prices alone.\n\nThe pricing model also matters operationally. OpenAI Pricing lists standard, Batch, Flex, and Fast mode prices for Terra, with different cost tradeoffs. The comparison data uses standard blended pricing, so it should not be treated as a universal invoice estimate for every processing mode.

GPT-5.6 Terra (Non-reasoning) is the recommended first test for coding-centric products that value current model availability and fast generated output. Its 52.3 coding index is materially above GPT-5’s 37.8, and its 122.922 median output tokens per second gives it the only reported output-speed measurement in the comparison. Start with repository-specific evaluations for patch correctness, test quality, and unwanted edits before committing to a production migration.\n\nGPT-5 is the better fit when math capability is central, when lower token cost dominates, or when a documented evaluation record matters more than the latest coding score. GPT-5’s 94.3 math index is the only supplied result for that capability. Its $3.4375 blended price is also lower than Terra’s $4.500000000000001.\n\nGPT-5.6 Terra is a poor choice for a math-led workload if the team requires a measured, directly cited score before deployment. The evidence does not show that Terra fails at math. It shows that the supplied official material does not publish a Terra benchmark. That distinction should shape the acceptance test.\n\nGPT-5 carries a lifecycle concern. GPT-5 model documentation marks the fixed snapshot gpt-5-2025-08-07 as Deprecated and describes GPT-5 as a previous-generation model. Applications depending on that snapshot should plan migration monitoring. The stable gpt-5 alias remains documented, but alias stability and snapshot longevity are different concerns.\n\nThe cleanest selection process is a workload split: test Terra first for coding throughput, test GPT-5 for math-heavy reasoning and cost-sensitive traffic, then compare accepted task outcomes rather than token prices alone.

GPT-5.6 Terra (Non-reasoning) has the larger public evidence gap, so teams should allocate more validation work before relying on it for a critical path. The supplied official model page does not list Terra’s context window, maximum output length, complete parameter limits, or official benchmark scores. The supplied research also found no reliable Reddit, Hacker News, or X posts that verify Terra’s coding experience, speed perception, or recurring failure modes.\n\nGPT-5 has clearer documented boundaries. The model documentation describes text and image input with text output, while audio and video input and output are unsupported. The same documentation marks fine-tuning and predicted outputs as unsupported. Developers needing audio or video processing cannot treat GPT-5 as a direct solution based on the supplied official capabilities.\n\nThe community evidence for GPT-5 is also limited. One Reddit author reported fast diagnosis of small bugs, but considered complete applications and UI generation less detailed. Comments described possible hallucinations or incorrect modifications in complex existing codebases. The post does not provide a controlled benchmark or reproducible experiment, so these claims cannot establish a general failure rate.\n\nThe central unresolved question is whether Terra’s coding-index advantage survives real repository constraints, long instructions, tool use, and review requirements. The supplied materials do not answer that question directly. A private evaluation remains necessary.

GPT-5 is easier to evaluate from public evidence, while GPT-5.6 Terra is more promising for measured coding performance. GPT-5 has official benchmark results and a reported math index of 94.3. Terra has a higher supplied coding index, but its official benchmark coverage is missing.\n\nGPT-5.6 Terra is the more current-looking API choice in the supplied materials, while GPT-5 requires lifecycle scrutiny. Terra is listed among OpenAI Frontier models. GPT-5’s fixed snapshot is marked Deprecated, so teams should distinguish the callable alias from the longevity of a pinned version.\n\nNeither model can be selected responsibly from benchmark scores alone. The supplied data does not measure accepted patches, retry rates, review time, or total task cost. Those missing measures are especially important because Terra costs more per blended 1M tokens, while GPT-5 may require more correction for some complex application tasks according to limited community evidence.