DeepSeek V4 Pro (Reasoning, Max Effort) vs o3: The Ultimate Performance & Pricing Comparison

DeepSeek V4 Pro is the stronger default for cost-sensitive developer workloads, while o3 remains the faster choice with a reported math score that DeepSeek lacks. The measured data gives DeepSeek V4 Pro an Artificial Analysis Intelligence Index of 44.3, compared with 30.4 for o3. The same dataset reports median output speed of 59.583 tokens per second for DeepSeek V4 Pro and 128.056 for o3. Both models show 0.3 seconds of reported latency. Data provided by https://artificialanalysis.ai/. The comparison should still be treated as a procurement decision under incomplete evidence. DeepSeek has current official documentation for deepseek-v4-pro, including API formats, context limits, pricing, and a concurrency limit of 500 (DeepSeek pricing and model documentation). OpenAI's current model directory does not list o3, and its current pricing page does not provide an o3 price (OpenAI models, OpenAI API pricing).

DeepSeek V4 Pro offers the clearer current integration story, while o3 offers better reported speed and a specialized math signal. DeepSeek V4 Pro is documented as deepseek-v4-pro, with a 1M-token context length and a 384K-token maximum output. The official page also lists JSON Output, Tool Calls, Anthropic API access, and OpenAI-format API access (DeepSeek pricing and model documentation). Responses API support is not currently available for DeepSeek V4 Pro according to that page, although DeepSeek says support is planned for early August 2026. The same official page lists a concurrency limit of 500.\n\nThe measured quality picture is not symmetrical. DeepSeek V4 Pro has a reported Intelligence Index of 44.3 and Coding Index of 59.4. o3 has a reported Intelligence Index of 30.4 and Math Index of 88.3. No comparable o3 coding score or DeepSeek math score is supplied, so the evidence cannot establish a complete quality ranking across coding and mathematics.\n\nThe commercial difference is substantial in the supplied data. DeepSeek V4 Pro costs $0.54375 per 1M blended tokens, while o3 costs $3.5. DeepSeek also has lower listed input and output prices. However, the official DeepSeek page warns that API prices may increase significantly, so a long-lived application should treat today's price advantage as conditional (DeepSeek pricing and model documentation).

o3 is the faster model in the supplied measurements, but DeepSeek V4 Pro has the stronger reported general-intelligence result. o3 produces a median 128.056 output tokens per second, compared with 59.583 for DeepSeek V4 Pro. That difference matters in interactive coding tools, streaming assistants, and workflows where users judge quality while the answer is arriving. Faster output can reduce perceived waiting time after generation begins.\n\nThe reported latency is 0.3 seconds for each model, so the speed advantage should not be described as an across-the-board responsiveness advantage. The supplied measurements suggest similar latency and different generation throughput. A developer building a chat interface may therefore see the largest practical difference in long responses, tool-result explanations, or code generation rather than in initial request handling.\n\nDeepSeek V4 Pro's Intelligence Index is 44.3, compared with 30.4 for o3. The dataset also gives DeepSeek V4 Pro a Coding Index of 59.4, but it provides no comparable o3 coding value. o3 has a Math Index of 88.3, but no comparable DeepSeek math value is supplied. The evidence supports a conditional conclusion: DeepSeek looks stronger on the measured general-intelligence dimension, while o3 looks stronger on measured output speed and has a distinct math signal.\n\nThe research brief does not provide reliable community testing for coding reliability, response behavior, stability, or model quirks for either model. It also does not provide official benchmark results for DeepSeek V4 Pro, and the supplied OpenAI sources do not provide o3 benchmark results. Developers should run representative repository tasks before treating either index as a production quality guarantee.

DeepSeek V4 Pro is the cheaper model by a wide margin in the supplied pricing snapshot, but its advantage depends on future price stability and workload shape. Its blended price is $0.54375 per 1M tokens, compared with $3.5 for o3. DeepSeek's listed input price is $0.435 per 1M tokens, while o3's supplied input price is $2. DeepSeek's output price is $0.87 per 1M tokens, while o3's is $8.\n\nThe practical implication is strongest for applications that generate large volumes of text, code, evaluations, or agent traces. Output-heavy systems benefit especially from the reported output-price difference. A cheaper token is not automatically a cheaper product if the model needs repeated retries, additional validation, or more tool calls to complete the same task. The supplied research does not include task-success rates, retry rates, or total workflow costs, so it cannot answer that question directly.\n\nDeepSeek also lists cached-input pricing of $0.003625 per 1M tokens. That may matter for workloads that repeatedly send stable prompts or long shared context. The official DeepSeek documentation warns that prices may increase significantly in the near term, with details deferred to later official notices (DeepSeek pricing and model documentation).\n\nOpenAI's current pricing page does not list an o3 price in the provided material (OpenAI API pricing). The data snapshot supplies $3.5, $2, and $8 for comparison, but developers should verify the live commercial terms before signing off on a budget. The cost winner is therefore DeepSeek V4 Pro for the snapshot, not necessarily for every future contract or workflow.

DeepSeek V4 Pro is the better first choice for high-volume general development workflows that can use its documented APIs and tolerate some platform uncertainty. Its measured Intelligence Index is 44.3, its measured Coding Index is 59.4, and its blended price is $0.54375 per 1M tokens. The official documentation supports JSON Output, Tool Calls, Anthropic API access, and OpenAI-format API access (DeepSeek pricing and model documentation). Those capabilities make it a practical candidate for coding assistants, structured extraction, tool-driven agents, and cost-controlled experimentation.\n\no3 is the better candidate when generation speed or mathematics is the deciding factor. Its median output speed is 128.056 tokens per second, and its Math Index is 88.3. The provided evidence does not establish whether that math result translates into better performance on a particular codebase, production debugging task, or agent workflow. It also does not establish current o3 availability, a stable alias, or a supported endpoint. OpenAI's current model directory lists newer frontier models but does not list o3 (OpenAI models).\n\nTeams selecting DeepSeek should validate Responses API requirements, concurrency behavior, and price-change exposure. The documented concurrency limit is 500, and Responses API support is currently marked as unavailable for deepseek-v4-pro (DeepSeek pricing and model documentation). Teams selecting o3 should first confirm that the intended account and endpoint can call the model under current terms.\n\nThe safest selection process is a small task-based bake-off. Use production-like coding tasks, math tasks, tool calls, long-context prompts, and failure recovery. Record successful task completion, retries, total tokens, latency, and developer acceptance. The current brief does not provide those measurements, so no source-backed recommendation can replace testing against the target workload.

DeepSeek V4 Pro has the more complete current public documentation, while o3 has the larger evidence gap around present availability. DeepSeek's official page identifies the model, documents supported API formats, gives context and output limits, and lists pricing (DeepSeek pricing and model documentation). The supplied OpenAI model directory and pricing page do not list the same details for o3 (OpenAI models, OpenAI API pricing).\n\nThe evidence does not show whether either model is more reliable in real repositories, whether either model has a distinctive failure pattern, or whether one produces better tool-call trajectories. No verified community discussions or independent evaluations with reproducible methods were found for either model. Those omissions matter because developer model selection is often governed by retries, correctness under project context, and operational stability rather than one aggregate score.\n\nThe data also does not permit a complete benchmark comparison. DeepSeek V4 Pro has a Coding Index of 59.4, but o3 has no supplied coding value. o3 has a Math Index of 88.3, but DeepSeek V4 Pro has no supplied math value. The missing cross-model measurements are evidence gaps, not victories for either side. Data provided by https://artificialanalysis.ai/.