DeepSeek V4 Pro (Reasoning, Max Effort) vs GPT-5 nano (high): The Ultimate Performance & Pricing Comparison

DeepSeek V4 Pro (Reasoning, Max Effort) is the stronger documented general-purpose choice, while GPT-5 nano (high) is the cheaper option with the stronger measured math result. The comparison is asymmetric because the supplied evidence does not describe two equally current, equally documented API products.

The data brief reports an Artificial Analysis Intelligence Index of 44.3 for DeepSeek V4 Pro and 19.9 for GPT-5 nano. It reports a Math Index of 83.7 for GPT-5 nano, but no corresponding DeepSeek value. It reports a Coding Index of 59.4 for DeepSeek V4 Pro, but no corresponding GPT-5 nano value. Those gaps prevent a complete capability ranking.

DeepSeek V4 Pro has a documented official API entry under deepseek-v4-pro. The official page states a 1M-token context length, a 384K-token maximum output, JSON Output, Tool Calls, Anthropic API access, and OpenAI-format API access (DeepSeek pricing and model documentation).

GPT-5 nano is different. The current OpenAI model directory does not list GPT-5 nano, gpt-5-nano, or gpt-5-nano-2025-08-07 (OpenAI models). Its supplied benchmark and price values are useful for comparison, but its current production availability, limits, and stable API name are not confirmed by the cited official pages.

For a developer selecting a production model, the practical result is clear. DeepSeek offers the better documented route for broad reasoning and coding-oriented evaluation. GPT-5 nano offers a much lower listed blended price, but the buyer must first verify that the intended model identifier still resolves and that the measured profile matches the deployed endpoint.

DeepSeek V4 Pro (Reasoning, Max Effort) is the safer documented default, but GPT-5 nano (high) can be the better economic choice for verified, math-heavy workloads. The strongest result in the supplied data is not a universal winner because the models lead on different measured dimensions.

DeepSeek leads the reported general intelligence comparison with an Artificial Analysis Intelligence Index of 44.3 versus 19.9. The reported difference is 24.4 points. That gap suggests a meaningful advantage for tasks that require broader reasoning, interpretation, and multi-step judgment, although the brief does not provide the benchmark methodology or task distribution needed to predict every application outcome.

GPT-5 nano leads the available math evidence with a Math Index of 83.7. DeepSeek has no reported Math Index in the data brief, so the result cannot establish that GPT-5 nano is better at every mathematical workload. It establishes only that GPT-5 nano has the reported score in the supplied comparison.

The coding comparison is incomplete. DeepSeek has a Coding Index of 59.4, while GPT-5 nano has no reported Coding Index. Developers should therefore avoid claiming that DeepSeek wins coding outright. The evidence supports a documented coding signal for DeepSeek, not a head-to-head coding verdict.

The official product evidence also points in different directions. DeepSeek documents a current model name and API endpoints, but warns that prices may increase substantially (DeepSeek pricing and model documentation). OpenAI documents general capabilities for its latest models, including text and image input, text output, multilingual ability, Responses API access, and official SDK access, but the cited page does not clearly assign those statements to GPT-5 nano (OpenAI models).

The most important unresolved question is whether the compared GPT-5 nano endpoint is still directly callable under a stable name. The supplied research does not answer that question. Treat availability as a release-validation task, not as an assumed product property.

DeepSeek V4 Pro (Reasoning, Max Effort) has the stronger documented performance profile, but the missing GPT-5 nano speed and coding values make the performance conclusion partial. The chart below should be read as a map of available evidence, not as a complete benchmark ranking.

DeepSeek records an Artificial Analysis Intelligence Index of 44.3 and a Coding Index of 59.4. GPT-5 nano records an Artificial Analysis Intelligence Index of 19.9 and a Math Index of 83.7. These measurements point to different deployment strengths. DeepSeek appears better supported for broad reasoning and coding evaluation, while GPT-5 nano has a clear math signal in the supplied data.

The intelligence gap matters most when an application must interpret ambiguous requirements, maintain a coherent plan, or combine several kinds of reasoning in one response. A higher general index does not guarantee better answers for a narrow task. It does give DeepSeek the stronger available evidence for workloads that cannot be reduced to a single specialized capability.

The math score changes the decision for applications that repeatedly solve formal, quantitative, or verification-heavy problems. GPT-5 nano may be attractive in that setting, but the research does not show whether its math result transfers to code generation, tool orchestration, long-context analysis, or production reliability. Those are open questions.

Latency is tied at 0.3 seconds in the data brief. That means neither model gains an advantage on the reported latency measure. DeepSeek also records median output speed of 59.583 output tokens per second. GPT-5 nano has no supplied output-speed value, so it cannot be called faster or slower on that measure.

Developers should test complete workflows rather than isolated prompts. The relevant unit is the application path: input formatting, model response, tool call, validation, retry behavior, and final user-visible result. The cited official DeepSeek page confirms Tool Calls and JSON Output, but the supplied OpenAI model page does not clearly confirm those capabilities specifically for GPT-5 nano (DeepSeek pricing and model documentation).

Evidence is also missing for community coding experience, stability, response quirks, and independent evaluations with disclosed methods. No reliable conclusion can be drawn on those dimensions. A private acceptance test is necessary before either model becomes a critical dependency.

GPT-5 nano (high) is the cheaper model on every supplied standard price comparison, but DeepSeek V4 Pro (Reasoning, Max Effort) may be cheaper in practice if it avoids retries or produces more usable first responses. The chart below already shows the price values, so the decision should focus on what drives total application cost.

The supplied blended price is $0.1375 per 1M tokens for GPT-5 nano and $0.54375 for DeepSeek V4 Pro. GPT-5 nano also has lower listed input pricing at $0.05 per 1M tokens and lower listed output pricing at $0.4 per 1M tokens. Those values make GPT-5 nano the obvious candidate for high-volume traffic, provided the model can be called reliably under the required identifier.

DeepSeek’s listed standard input price is $0.435 per 1M tokens and its output price is $0.87 per 1M tokens. Its official page separately lists cached input at $0.003625 per 1M tokens and uncached input at $0.435 per 1M tokens (DeepSeek pricing and model documentation). Cache behavior can materially change an application’s economics when prompts repeat large system instructions or stable context.

The cheaper token price is not automatically the cheaper product. A model that needs more retries, stricter repair passes, additional validation calls, or human review can consume the savings. The supplied research does not provide retry rates, failure rates, quality-adjusted cost, or token usage distributions for either model. Those omissions prevent a reliable total-cost-of-ownership comparison.

GPT-5 nano’s current official price is also unresolved. The cited OpenAI pricing page does not list gpt-5-nano; it lists gpt-5.4-nano at $0.20 input, $0.02 cached input, and $1.25 output per 1M tokens, but those are not GPT-5 nano prices (OpenAI API pricing). Developers must not substitute that newer model’s price for the compared model.

DeepSeek adds commercial uncertainty by warning that API prices may rise substantially. GPT-5 nano adds product uncertainty because the current model directory and pricing page do not confirm a dedicated listing. The price winner is therefore conditional: choose GPT-5 nano for cost-sensitive traffic only after endpoint, quota, and billing verification.

DeepSeek V4 Pro (Reasoning, Max Effort) is the recommended default for developers who need a documented API surface and broader measured intelligence. GPT-5 nano (high) is the better candidate for cost-sensitive or math-focused systems only after its production availability is verified.

Choose DeepSeek when the application needs a clearly documented model identifier, Tool Calls, JSON Output, Anthropic API compatibility, or OpenAI-format API compatibility. The official DeepSeek documentation also lists a 1M-token context length and a 384K-token maximum output (DeepSeek pricing and model documentation). Those properties reduce integration ambiguity for teams building around long context, structured responses, or existing API adapters.

Choose GPT-5 nano when the application can tolerate uncertainty during validation and the primary objective is lower listed token cost. Its supplied blended price is $0.1375 per 1M tokens, and its reported Math Index is 83.7. That combination is compelling for workloads where mathematical quality matters and the endpoint has been confirmed in the target account.

Do not choose GPT-5 nano solely because it appears in the supplied data brief. The current OpenAI model directory does not list the model, and the current pricing page does not list its dedicated price (OpenAI models, OpenAI API pricing). The research cannot confirm whether the name is a stable public alias, a historical entry, or an environment-specific model label.

Do not choose DeepSeek solely because its intelligence score is higher. The data does not contain a DeepSeek Math Index, a GPT-5 nano Coding Index, independent evaluation methods, or production failure rates. The result is a selection recommendation under incomplete evidence, not a universal capability claim.

A sensible validation sequence is straightforward. First, confirm that each model identifier is callable in the intended region and account. Next, run representative prompts for reasoning, math, coding, structured output, tool calls, and long-context tasks. Then measure usable answer rate, retries, latency, output speed, and actual billed tokens. Finally, reassess the choice if DeepSeek changes pricing or if OpenAI publishes a confirmed GPT-5 nano listing.

The final recommendation is risk-weighted. DeepSeek is the stronger documented production starting point. GPT-5 nano deserves a focused pilot where low cost and math performance are more important than current documentation certainty.

DeepSeek V4 Pro (Reasoning, Max Effort) is easier to validate from official documentation, while GPT-5 nano (high) requires an availability check before architecture decisions. The following questions address the gaps that the supplied benchmark rows cannot resolve.