DeepSeek V4 Flash 0731 (Reasoning, Max Effort) vs GPT-5 (high): The Ultimate Performance & Pricing Comparison

DeepSeek V4 Flash 0731 is the stronger default for cost-sensitive coding agents, while GPT-5 (high) remains the safer specialist choice for mathematics and image-aware workflows. The data brief gives DeepSeek a 69.1 coding index and GPT-5 (high) a 37.8 coding index. DeepSeek also leads the intelligence index, with 49.9 versus 34.7. GPT-5 (high) is the only model with a reported mathematics index, at 94.3, so the overall choice depends on whether software execution or mathematical assurance dominates the workload.\n\nThe comparison is complicated by lifecycle differences. DeepSeek V4 Flash 0731 remains listed as a current callable model under the stable alias deepseek-v4-flash in the DeepSeek Models & Pricing documentation. OpenAI still lists gpt-5, but the fixed snapshot gpt-5-2025-08-07 is marked Deprecated in the GPT-5 model documentation. That makes GPT-5 (high) a capable option with greater migration risk than the benchmark table alone suggests.

DeepSeek V4 Flash 0731 offers the better value and stronger measured coding result, while GPT-5 (high) preserves a narrower advantage in the evidence available for mathematics.\n\n| Decision factor | Better choice | Why it matters |\n| --- | --- | --- |\n| Coding-oriented agents | DeepSeek V4 Flash 0731 | The coding index is 69.1 versus 37.8, a material gap for code generation and repository tasks. |\n| General intelligence signal | DeepSeek V4 Flash 0731 | The intelligence index is 49.9 versus 34.7. |\n| Mathematics | GPT-5 (high) | GPT-5 (high) has a reported mathematics index of 94.3; the data brief provides no corresponding DeepSeek value. |\n| Blended token economics | DeepSeek V4 Flash 0731 | The blended price is $0.17500000000000002 versus $3.4375 per 1M tokens. |\n| Reported generation speed | DeepSeek V4 Flash 0731 | DeepSeek reports 102.212 median output tokens per second; GPT-5 (high) has no reported value. |\n| Measured request latency | Tie | Both models report 0.3 seconds in the data brief. |\n\nThe strongest selection signal is not a single benchmark. It is the interaction between task mix, output volume, tool protocol, and model lifecycle. DeepSeek supports thinking and non-thinking modes, JSON output, tool calls, Anthropic-compatible access, and a Responses API through its official model and pricing documentation. Its Responses API guide documents an OpenAI-compatible endpoint. GPT-5 supports structured outputs, function calling, streaming, custom tools, and reasoning controls according to GPT-5 for developers and the GPT-5 model documentation.\n\nThe evidence does not answer which model produces fewer regressions in a specific production repository. Developers should treat the benchmark lead as a routing signal, then validate with representative tasks, accepted patches, tool-call recovery, and review effort.

DeepSeek V4 Flash 0731 is the better-supported choice for coding throughput, but GPT-5 (high) may still win tasks where mathematical reasoning is the dominant acceptance criterion.\n\nThe coding-index gap is large enough to change architecture decisions. A coding agent that repeatedly inspects files, proposes patches, runs tests, and revises failed changes benefits from a higher probability of useful intermediate actions. DeepSeek’s 69.1 coding index suggests a stronger starting point for this loop than GPT-5 (high) at 37.8. It does not prove that DeepSeek will edit a particular repository correctly, because neither the data brief nor the cited materials provide a controlled, repository-specific success rate.\n\nGPT-5 (high) has the clearer evidence for mathematical work. Its reported mathematics index is 94.3, while no DeepSeek mathematics value appears in the data brief. That asymmetry matters for theorem-oriented code, numerical validation, symbolic reasoning, and applications where an incorrect intermediate result can invalidate an otherwise clean implementation. It does not establish that GPT-5 is better at every reasoning task.\n\nThe speed result also needs careful interpretation. DeepSeek reports 102.212 median output tokens per second, while GPT-5 (high) has no corresponding value. Both models report 0.3 seconds of latency. Output speed affects long responses and agent traces, but latency affects the time before useful work begins. A fast generator can still feel slow if it spends more tokens revising poor patches.\n\nCommunity evidence is directional rather than conclusive. One DeepSeek user report describes sustained debugging of an unfinished website and observations near 74 tokens per second, but gives no reproducible protocol. A separate local deployment report records 12.5 tokens per second under specific hardware and quantization conditions. A GPT-5 user report describes faster small bug fixes but possible hallucinations or incorrect changes in complex repositories. These reports should inform test design, not replace it.

DeepSeek V4 Flash 0731 is dramatically cheaper per token, but GPT-5 (high) can become economically preferable if it reduces correction, review, or rerun work.\n\nThe blended price is $0.17500000000000002 for DeepSeek and $3.4375 for GPT-5 (high). DeepSeek’s input price is $0.14, compared with $1.25 for GPT-5 (high), while output is $0.28 versus $10. These differences favor DeepSeek for high-volume coding agents, repeated repository context, exploratory generation, and workflows that produce substantial output before a human reviews the result.\n\nToken price alone does not measure task cost. A model that produces a plausible but incorrect patch may trigger another prompt, another tool cycle, a test run, and human inspection. GPT-5 (high) can justify its higher price when its mathematical strength or tool behavior prevents expensive downstream correction. The available evidence does not provide comparative patch acceptance rates, review time, retry counts, or total cost per completed task, so no exact break-even point can be calculated.\n\nCaching can also change the practical result. DeepSeek documents separate cached and uncached input pricing in its current pricing page, and its thinking-mode behavior is documented in Thinking Mode. Developers should model cache hit rates, reasoning output volume, tool-call retries, and human review together.\n\nGPT-5 (high) may be the cheaper operational choice for a narrow, high-consequence task if one successful response replaces several weaker attempts. DeepSeek is the cheaper infrastructure choice for most broad coding workloads, but the claim that it is cheaper per completed feature remains unproven without workload-specific telemetry.

DeepSeek V4 Flash 0731 should be the default router target for coding-heavy workloads, while GPT-5 (high) should be reserved for mathematics, image input, or cases where its ecosystem fit offsets its cost and lifecycle risk.\n\nChoose DeepSeek for code review drafts, bug localization, test generation, repository navigation, autonomous patch loops, and large-volume agent activity. Its measured coding index of 69.1, reported output speed of 102.212 median output tokens per second, and blended price of $0.17500000000000002 support that default. The model supports thinking modes including max, and the Thinking Mode documentation explains the required handling of reasoning_content during tool calls.\n\nChoose GPT-5 (high) for mathematical validation, image-grounded development tasks, and integrations built around OpenAI’s structured tool ecosystem. GPT-5 supports image input, while the DeepSeek materials do not clearly document image input. GPT-5 also has a reported mathematics index of 94.3. Its official developer material describes function calling, structured outputs, streaming, custom tools, and reasoning controls in GPT-5 for developers.\n\nUse a fallback or routing policy if the application needs both profiles. Send ordinary coding tasks to DeepSeek, escalate mathematical or image-dependent tasks to GPT-5, and record accepted patches rather than relying only on model scores. DeepSeek documents an account concurrency limit of 2,500 and HTTP 429 behavior in Rate Limit & Isolation.\n\nThe largest unresolved issue is lifecycle stability. DeepSeek is currently listed under its stable alias, while the GPT-5 fixed snapshot is Deprecated. The data does not show how long either callable alias will remain unchanged. Pin versions where supported, monitor provider notices, and keep a migration test set before committing the application to either model.

DeepSeek V4 Flash 0731 is the practical first candidate, but deployment should proceed only after testing the failure modes that the published comparison cannot resolve.\n\nThe key unknowns are repository-specific patch quality, tool-call recovery, mathematical error rates, cache behavior, and migration impact. Official documentation establishes API capabilities and constraints, while community posts provide individual experiences. Neither source class supplies a controlled comparison across the same prompts, repositories, tools, and acceptance criteria.\n\nA useful pilot should compare completed tasks, not just generated text. Track whether tests pass, how often the agent retries, how much human correction is required, and whether the provider’s API contract fits the existing orchestration layer. Treat the published indices as prioritization evidence and the pilot as the final selection gate.