DeepSeek V4 Pro (Non-reasoning) vs GPT-5.6 Terra (max)

GPT-5.6 Terra (max) is the stronger default for difficult developer work, because its measured Intelligence Index is 56.6 versus 31.9 for DeepSeek V4 Pro (Non-reasoning). Its measured results also lead on GPQA, HLE, SciCode, IFBench, LCR, and TerminalBench Hard. That pattern matters when the task needs planning, careful instruction following, or recovery from a complex tool result.

DeepSeek V4 Pro (Non-reasoning) is the practical budget choice when fast first response and low token spend matter more than maximum task reliability. Its listed blended price is $0.544 per 1M tokens, compared with $4.5 for Terra. Its measured latency is 1.24 seconds, compared with 196.311 seconds for Terra.

The comparison has an important qualification: the exact DeepSeek version has sparse official documentation. DeepSeek’s current documentation describes a stable alias that now maps to a later model, so it cannot establish the exact capabilities, availability, or API behavior of this benchmarked version. Read the current DeepSeek pricing documentation as present-day platform context, not as a version-specific contract.

Data provided by https://artificialanalysis.ai/

GPT-5.6 Terra (max) is the safer choice for an agent that must reason through ambiguous work and use a broad hosted tool set. OpenAI positions Terra as a model that balances intelligence and cost, and documents text and image input, text output, structured output, function calling, prompt caching, and supported tools such as web search, file search, Code Interpreter, Hosted Shell, Apply Patch, Skills, Computer Use, MCP, and Tool Search in its model documentation. That documented surface reduces integration uncertainty for teams building an agent rather than a single text endpoint.

DeepSeek V4 Pro (Non-reasoning) is the stronger choice for a tightly scoped generation path where unit economics and perceived responsiveness dominate. The benchmark shows it wins Tau2, at 0.912280701754386 versus 0.862573099415205. That result suggests it should not be dismissed as broadly unusable. It does mean that a selective evaluation against your own workflow is more important than choosing only from aggregate scores.

The biggest unanswered buyer question is reproducibility. Terra has a directly callable stable model ID in the official documentation, while the research material does not confirm whether DeepSeek’s exact benchmarked model name remains directly callable. The current DeepSeek pricing documentation documents JSON output, tool calling, Responses API, Anthropic API, Chat Prefix Completion, and FIM Completion for the current stable alias, but it does not prove those features for this older benchmark entry.

Decision need	Better starting point	Why
High-stakes coding and agent work	GPT-5.6 Terra (max)	Stronger measured reasoning-oriented results and documented hosted tools
High-volume, simple generation	DeepSeek V4 Pro (Non-reasoning)	Lower listed blended token cost and lower measured latency
Image understanding	GPT-5.6 Terra (max)	Officially documented image input support
Exact-version operational certainty	GPT-5.6 Terra (max)	Official documentation identifies the callable model directly

GPT-5.6 Terra (max) leads the measured capability results, but its 196.311-second latency can make it unsuitable for every interactive path. Its 56.6 Intelligence Index, 76.7 Coding Index, and 0.575757575757576 TerminalBench Hard score support using it where a failed answer creates expensive engineering review or repeated tool calls. The page chart shows the broader score pattern, so the operational point is simpler: Terra is positioned for work where a more capable first attempt can be worth a slower response.

DeepSeek V4 Pro (Non-reasoning) leads measured latency at 1.24 seconds and reaches 62.894 median output tokens per second. That profile is better suited to rapid UI feedback, short transformations, classification, extraction, and workflows where users expect an immediate response. Yet it is not automatically the better end-to-end experience. A quick first answer can cost more in product time if it needs repeated correction on tasks where Terra’s measured reasoning advantage matters.

GPT-5.6 Terra (max) also has a speed distinction that is easy to miss. Its median output rate is 108.749 tokens per second, faster than DeepSeek’s 62.894, while its latency is much higher. Developers should therefore separate time-to-first-result from generation rate. A long reasoning process may delay the answer even if visible text streams quickly afterward.

Evidence remains incomplete for production planning. OpenAI does not publish Terra-specific public benchmarks, latency guarantees, or success-rate guarantees in the cited material. The research material also found no reliable community reports for either exact model. Treat the measured chart as a useful comparison point, then run representative prompts that include your tools, output limits, retries, and evaluation criteria. OpenAI’s reasoning guide also warns that reasoning tokens consume the generation budget, which can produce incomplete responses when max_output_tokens is set too low.

DeepSeek V4 Pro (Non-reasoning) is cheaper on the listed benchmark price, at $0.544 per 1M blended tokens versus $4.5 for GPT-5.6 Terra (max). That difference gives DeepSeek a clear advantage for stable, high-volume requests with predictable prompts and short review cycles. The same pattern appears in listed input and output prices: $0.435 and $0.87 for DeepSeek, versus $2 and $12 for Terra.

GPT-5.6 Terra (max) can still be the lower-cost product decision when a stronger answer reduces follow-up work. This is most plausible for repository changes, investigation tasks, multi-step tool use, and specifications that would otherwise need several model attempts or extensive human checking. The benchmark does not provide retry rates, human-review time, tool-call counts, or task completion rates. Do not claim total-cost savings without measuring those factors in your own system.

GPT-5.6 Terra (max) also has a pricing edge case that can reverse an expected budget. OpenAI states that requests above 272K input tokens receive higher input and output pricing for the whole request, according to the Terra model documentation. Long repository context, document archives, and large conversation histories need separate budget tests. OpenAI also documents Standard, Batch, Flex, and Fast pricing in its pricing documentation, so the actual serving mode changes the economics.

DeepSeek V4 Pro (Non-reasoning) has a different cost uncertainty. The cited benchmark pricing exists, but current DeepSeek platform pricing is documented for the stable alias rather than this exact version. The current DeepSeek pricing documentation should be checked before shipping a cost model, because current alias behavior and current platform prices are not evidence that the benchmarked version remains available at the same terms.

GPT-5.6 Terra (max) should be the primary model for developer agents that must inspect files, reason over screenshots, select tools, and produce careful code changes. Its documented image input and hosted tool support make it a more complete starting platform for this kind of product. Its higher measured capability scores reinforce that choice, although OpenAI’s public materials do not supply Terra-specific vendor benchmark claims. Review the supported interface details in the OpenAI model documentation before committing to a tool architecture.

DeepSeek V4 Pro (Non-reasoning) should be the primary model for cost-sensitive, low-risk request paths that need rapid responses. Examples include text cleanup, structured extraction after validation, short-form generation, routing candidates, and user-facing drafts where the application can check results. Its 1.24-second latency and $0.544 blended price are compelling when the request can be bounded and failure is cheap to catch.

GPT-5.6 Terra (max) should not be placed blindly behind a real-time interaction. Its 196.311-second measured latency conflicts with the expectation of immediate feedback. A product can use Terra for background jobs, explicit deep-work actions, or workflows that show clear progress. The benchmark does not explain the source of that latency, so do not promise a specific user experience from the comparison alone.

DeepSeek V4 Pro (Non-reasoning) should not be selected solely from price until the exact model can be invoked and tested. The research material does not establish its version-specific context limit, multimodal support, parameter behavior, benchmarks from the vendor, or known failure patterns. DeepSeek’s current platform documentation describes the current alias, not a guaranteed substitute for this exact entry.

The practical decision is therefore a two-lane design: use DeepSeek for bounded, high-throughput work, and reserve Terra for tasks where correctness, tool orchestration, and difficult reasoning have visible product value.

GPT-5.6 Terra (max) has the clearer official operational contract, while DeepSeek V4 Pro (Non-reasoning) has the clearer low-cost benchmark profile. That distinction is more important than a single winner label. Terra’s official documentation identifies its callable model and supported APIs. The DeepSeek research material instead documents a current stable alias that points to a later version, leaving the benchmarked version’s direct availability unconfirmed.

DeepSeek V4 Pro (Non-reasoning) needs a capability verification test before it enters a production route. The current DeepSeek documentation states that FIM Completion is available only in non-thinking mode, but this documentation applies to the current alias and cannot confirm the same limitation for the benchmarked entry. That is a meaningful gap for coding products that depend on completion behavior. See the DeepSeek pricing documentation for the current platform statement.

GPT-5.6 Terra (max) needs an output-budget test before it enters an autonomous workflow. OpenAI explains that max_output_tokens limits reasoning tokens, visible output tokens, and other generated tokens together. A limit that looks sufficient for the final answer can still leave too little room for reasoning and return an incomplete result. The detailed behavior is described in the reasoning guide.

GPT-5.6 Terra (max) also has no cited replacement or deprecation notice. The OpenAI deprecations documentation does not list Terra in the research material. That is useful operational context, but it is not a promise of future availability. Version pinning, fallback behavior, and recurring evaluation remain necessary for both vendors.