DeepSeek V4 Pro (Reasoning, High Effort) vs GPT-5.6 Terra (max): The Ultimate Performance & Pricing Comparison

GPT-5.6 Terra is the safer default for demanding developer work because it leads most measured quality evaluations and has clearer current documentation. The measured comparison from Artificial Analysis gives GPT-5.6 Terra a 76.7 coding index and a 56.6 intelligence index, versus 58.7 and 43.7 for DeepSeek V4 Pro 0424 High. DeepSeek remains materially cheaper at $0.544 per 1M blended tokens, while GPT-5.6 Terra costs $4.5. The central selection question is therefore not simply which model scores higher. It is whether your application can trade higher model spend for stronger coding and tool-oriented reliability, or whether DeepSeek’s lower price justifies more validation and version uncertainty.

GPT-5.6 Terra offers the stronger measured capability profile, while DeepSeek V4 Pro 0424 High offers the stronger price profile and one important tool-use advantage. GPT-5.6 Terra leads coding at 76.7 versus 58.7, terminalbench_v2_1 at 0.880149812734082 versus 0.647940074906367, and scicode at 0.539 versus 0.464, according to Artificial Analysis. Those gaps suggest a meaningful advantage for repository changes, command-line workflows, and code-heavy reasoning tasks, not just a marginal benchmark win. GPT-5.6 Terra also leads GPQA at 0.925 versus 0.905, HLE at 0.429 versus 0.352, and LCR at 0.796666666666667 versus 0.67. DeepSeek leads tau2 at 0.941520467836257 versus 0.862573099415205, so some agent interaction tasks may favor it. IFBench is effectively level in the supplied data, with DeepSeek at 0.712925170068027 and GPT-5.6 Terra at 0.712244897959184. Developers should treat that narrow result as a reminder that no single index predicts every workflow. The evidence is incomplete for mathematics, MMLU Pro, LiveCodeBench, Math 500, and AIME because both entries are null. DeepSeek’s official documentation currently describes deepseek-v4-pro as DeepSeek-V4-Pro-0813, with 1M context and 384K maximum output, but it does not verify the target 0424 High snapshot. DeepSeek’s pricing page therefore supports current API facts, not historical identity claims. GPT-5.6 Terra has a current model page, a stable ID, and a documented release record in the OpenAI API Changelog.

GPT-5.6 Terra is the performance choice for code-heavy and terminal-driven workflows, while DeepSeek V4 Pro remains competitive on selected interaction tasks. The supplied Artificial Analysis results show GPT-5.6 Terra ahead on the broad coding index, terminalbench_hard, terminalbench_v2_1, scicode, LCR, GPQA, and HLE. The largest practical-looking separation appears in terminalbench_v2_1, where GPT-5.6 Terra records 0.880149812734082 and DeepSeek records 0.647940074906367. That result is relevant when an agent must inspect files, issue commands, recover from errors, and complete a multi-step terminal objective. It does not prove success on your own repository, because the brief does not include task definitions, model prompts, tool settings, or confidence intervals. GPT-5.6 Terra also produces output at a measured median of 108.749 tokens per second, compared with 61.151 for DeepSeek. Faster token emission can reduce the visible wait after generation begins, especially for long answers or streamed code. Yet the latency result reverses the simple speed story: DeepSeek is listed at 33.973 seconds and GPT-5.6 Terra at 196.311 seconds. The brief does not explain whether this latency includes queueing, reasoning time, tool calls, or time to first token. You should therefore avoid treating output speed and end-to-end response time as interchangeable metrics. DeepSeek’s tau2 score is 0.941520467836257 versus 0.862573099415205 for GPT-5.6 Terra, which may matter for a narrow class of agent interaction tests. The source materials do not identify the underlying tau2 scenarios, so a production team should reproduce its own tool-call loop before selecting DeepSeek on that result alone. GPT-5.6 Terra’s documented support for function calling, file search, web search, Code Interpreter, Hosted Shell, Apply Patch, Computer Use, MCP, and Tool Search appears on the official model page. DeepSeek’s current page lists JSON output, tool calling, Responses API, Anthropic API, and completion features, but explicitly marks FIM Completion as limited to non-thinking mode on the DeepSeek documentation. The brief cannot confirm whether that limitation applies to 0424 High. That uncertainty matters for coding assistants that depend on fill-in-the-middle behavior.

DeepSeek V4 Pro is the clear unit-cost winner, but GPT-5.6 Terra can still be economically rational when quality failures create engineering work. The blended figure is $0.544 per 1M tokens for DeepSeek and $4.5 for GPT-5.6 Terra, based on Artificial Analysis. DeepSeek’s listed input price is $0.435 and output price is $0.87, while GPT-5.6 Terra lists $2 input and $12 output in the supplied snapshot. That output-price gap makes verbose reasoning, generated patches, and large test explanations especially important in a real budget model. A cheap request is not necessarily a cheap feature if engineers must rerun prompts, review more incorrect patches, or add guardrails. The supplied evidence does not measure retry rates, human review time, defect escape, or total cost per completed task, so no honest break-even claim can be made. GPT-5.6 Terra’s official Pricing page documents Standard, Batch, Flex, and Fast mode prices, giving teams more operating modes than the DeepSeek brief confirms for the target historical snapshot. OpenAI also states that requests above 272K input tokens receive higher input and output multipliers on the GPT-5.6 Terra model page. That rule can reverse an apparent long-context bargain if your application repeatedly sends very large repositories or transcripts. GPT-5.6 Terra supports prompt caching, and the model documentation describes a 1,050,000-token context window, but the source materials do not establish equivalent context or caching behavior for DeepSeek 0424 High. DeepSeek’s current 0813 page lists 1M context, yet the brief explicitly warns that this cannot be projected backward to 0424 High. For a high-volume classification or short-answer service, DeepSeek’s price advantage is likely decisive if quality is acceptable. For autonomous coding, the correct cost unit is a completed, reviewed change rather than a million-token invoice.

GPT-5.6 Terra is the recommended primary model for production coding agents, while DeepSeek V4 Pro is the recommended cost-control candidate for validated, high-volume workloads. Choose GPT-5.6 Terra when the model must modify a repository, operate a shell, use files and tools, or sustain complex reasoning across many turns. Its measured coding index of 76.7 and terminalbench_v2_1 score of 0.880149812734082 provide the strongest supplied evidence for those tasks, and its current documentation covers the relevant tools at developers.openai.com. Choose DeepSeek when request volume dominates, outputs are short enough to keep review cheap, and your team can pin and test the exact endpoint behavior. Its $0.544 blended price and tau2 score of 0.941520467836257 create a credible case for routing narrowly defined agent interactions to it. Do not select DeepSeek 0424 High solely because the current DeepSeek page shows 1M context, 384K maximum output, or a deepseek-v4-pro alias. The current DeepSeek pricing documentation identifies the documented product as DeepSeek-V4-Pro-0813, and the research brief found no verified 0424 High page, stable alias, or replacement notice. Do not select GPT-5.6 Terra solely because it is newer or has more documented tools. The brief contains no Terra-specific official benchmark publication, latency guarantee, or reliable community sample. The practical decision is a controlled evaluation on your own tasks: compare completed changes, tool-call recovery, review effort, and end-to-end latency under the same prompts. Keep the model ID explicit in configuration. OpenAI’s Deprecations page does not currently list GPT-5.6 Terra, but the absence of a deprecation notice is not a lifetime guarantee. The safest architecture is a primary GPT-5.6 Terra path with a DeepSeek fallback or batch route only after endpoint identity and regression tests are verified.

DeepSeek V4 Pro requires the most verification before adoption because the target 0424 High identity is not confirmed by current official documentation. The research brief found no reliable Reddit, Hacker News, or X discussion for either exact model, so community sentiment cannot settle coding quality, speed feel, or failure patterns. GPT-5.6 Terra has clearer current documentation, but the supplied evidence still lacks a Terra-specific official benchmark report and guaranteed production latency. Teams should make the final choice with a task replay set, explicit tool permissions, and cost accounting that includes retries and review.