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

DeepSeek V4 Pro (Reasoning, High Effort) is the stronger default for developers, while GPT-5 nano (high) is the lower-cost option with a notable math result.

The available benchmark evidence favors DeepSeek on general intelligence. DeepSeek records an Artificial Analysis Intelligence Index of 43.1, compared with 19.9 for GPT-5 nano (high). The data also reports a coding index of 58.7 for DeepSeek, but no corresponding GPT-5 nano coding score. That means the evidence supports DeepSeek as the better-supported choice for coding-oriented reasoning, but it does not establish a complete head-to-head coding win.

GPT-5 nano (high) costs $0.1375 per 1M blended tokens, compared with $0.54375 for DeepSeek. That makes GPT-5 nano substantially more attractive for high-volume workloads, assuming the exact model remains callable in the intended environment.

The central selection risk is model identity. DeepSeek's official pricing page lists deepseek-v4-pro, while the current OpenAI model directory does not list GPT-5 nano or gpt-5-nano. Data provided by Artificial Analysis identifies the compared model as gpt-5-nano, but the official OpenAI pages do not confirm its current public status.

DeepSeek V4 Pro (Reasoning, High Effort) offers the clearest evidence for developer use, whereas GPT-5 nano (high) offers the clearest price advantage.

DeepSeek has three practical strengths in the supplied evidence. Its general intelligence score is higher, its coding score is available, and its median output speed is 69.83 tokens per second. Its measured latency is 0.3 seconds, matching GPT-5 nano's reported latency. These results make DeepSeek easier to justify for software tasks where answer quality and sustained generation matter.

GPT-5 nano has a different profile. Its blended price is $0.1375 per 1M tokens, its input price is $0.05 per 1M tokens, and its output price is $0.4 per 1M tokens. Its reported math index is 83.7, which is the strongest specialized result in the comparison. However, the supplied evidence does not include a DeepSeek math score, so the math result cannot prove a winner.

Official documentation introduces uncertainty for both names. DeepSeek documents a stable deepseek-v4-pro identifier and a 1M-token context length, but it does not document a separate deepseek-v4-pro-high API alias. OpenAI's current model and pricing pages do not list GPT-5 nano. Developers should therefore separate benchmark preference from deployment certainty.

DeepSeek V4 Pro (Reasoning, High Effort) has the stronger measured profile for general developer work, but the comparison lacks enough evidence to rank both models across every coding task.

The most important result is not the raw score gap alone. DeepSeek's Intelligence Index is 43.1, while GPT-5 nano's is 19.9. That gap suggests a meaningful advantage for tasks that require broader reasoning, instruction following, decomposition, or recovery from ambiguous requirements. The benchmark cannot tell us how that advantage appears in a specific repository, so production evaluation remains necessary.

DeepSeek also has a reported coding index of 58.7. GPT-5 nano has no coding index in the supplied data. This asymmetry matters. A developer choosing between the models can reasonably treat DeepSeek as the better-supported coding candidate, but cannot claim that DeepSeek beats GPT-5 nano on coding itself. The missing GPT-5 nano coding result is a direct evidence gap.

Speed creates a narrower conclusion. DeepSeek's median output rate is 69.83 tokens per second. GPT-5 nano's corresponding value is unavailable, so no speed winner can be declared. Both models have a reported latency of 0.3 seconds, but equal latency does not imply equal time to finish a long response. Output rate, response length, queueing, streaming behavior, and workload mix can change the user experience.

API features may also affect implementation effort. DeepSeek's documentation lists JSON Output, Tool Calls, Anthropic API support, and Chat Prefix Completion in beta. The same page says Responses API support is not available for deepseek-v4-pro at the stated time, with support planned for early August 2026. The provided materials do not confirm whether GPT-5 nano has a stable current alias or dedicated API limits. That uncertainty prevents a clean capability comparison.

GPT-5 nano (high) is the clear cost choice, but its lower price only matters if the model can be reliably provisioned and performs well enough on the target workload.

The blended price is $0.1375 per 1M tokens for GPT-5 nano, versus $0.54375 for DeepSeek V4 Pro. GPT-5 nano is also listed at $0.05 per 1M input tokens and $0.4 per 1M output tokens. DeepSeek is listed at $0.435 for input and $0.87 for output. The pricing chart captures the direct difference, but the operational meaning depends on how many requests fail, require retries, or need human correction.

A cheaper model can become more expensive when weaker answers trigger additional calls. This is especially relevant for code generation, tool use, structured extraction, and agent loops. The supplied evidence does not provide task-level failure rates, retry rates, or correction costs, so it cannot determine the total cost of ownership. Developers should test cost per accepted result, not only cost per token.

DeepSeek's official page adds another pricing risk. DeepSeek states that API prices may increase substantially in the future, with the final approach subject to an official announcement. The page lists a concurrency limit of 500. A workload near that limit may need throttling or queuing, which can add delay even when the listed token price remains attractive.

OpenAI's current pricing page lists gpt-5.4-nano at $0.20 per 1M input tokens, $0.02 for cached input, and $1.25 for output, but that is not GPT-5 nano. The OpenAI pricing page therefore cannot validate the compared model's current price. The supplied $0.1375 blended figure remains useful for the comparison, but its deployment status requires verification.

DeepSeek V4 Pro (Reasoning, High Effort) is the safer first choice for coding-heavy evaluation, while GPT-5 nano (high) should be tested as a cost-focused alternative only after availability is confirmed.

Choose DeepSeek when the application needs broad reasoning, code assistance, repository-level analysis, or long generated outputs. The available evidence supports that direction through a 43.1 Intelligence Index and a 58.7 Coding Index. DeepSeek's official documentation also documents a 1M-token context length and a 384K-token maximum output length for deepseek-v4-pro. Those limits are documented for the official model identifier, not explicitly for the high-effort comparison alias.

Choose GPT-5 nano when token economics dominate and the workload is narrow, repetitive, or math-oriented. Its blended price is $0.1375 per 1M tokens, and its math index is 83.7. The evidence does not show whether that math strength transfers to code generation, tool orchestration, or general software reasoning.

Use a verification gate before production deployment. OpenAI's current model directory does not list GPT-5 nano, and its pricing page does not list gpt-5-nano. The provided research also found no reliable community posts that clearly describe GPT-5 nano's coding behavior, speed, or failure patterns.

A practical pilot should compare accepted code changes, test pass rates, tool-call recovery, latency under concurrency, and cost per successful task. Those measurements are not provided in the brief, so this article cannot claim which model wins after application-specific correction costs. The evidence-based default is DeepSeek for capability risk, with GPT-5 nano reserved for validated low-cost paths.

DeepSeek V4 Pro (Reasoning, High Effort) requires an identity check before deployment because the benchmark alias and official API identifier are not identical.

The comparison contains enough evidence for an initial shortlist, but not enough to remove deployment uncertainty. Developers should verify model access, limits, billing behavior, and task-level quality in the exact account and region used by production. The official pages are the right references for current availability, while the supplied Artificial Analysis snapshot provides the comparison metrics.