DeepSeek-V4-Flash-0731 is the official release of V4-Flash, featuring a massive leap in agentic capabilities. It outperforms V4-Pro (Preview) on key benchmarks, natively supports the Responses API, and is fully adapted for Codex CLI.
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DeepSeek-V4-Flash-0731 is the official release of the V4-Flash model line, marking a significant upgrade over its preview predecessor. It shares the same model architecture as DeepSeek-V4-Flash-DSpark, including an attached speculative decoding module designed to accelerate inference. The model delivers a massive leap in agentic capabilities, outperforming DeepSeek-V4-Pro (Preview) across key benchmarks despite its considerably smaller activated parameter count. It natively supports the Responses API and is fully adapted for Codex CLI, making it a versatile choice for both autonomous agent workflows and direct coding assistance.
DeepSeek-V4-Flash-0731 posts substantially higher scores than both the V4-Flash preview and V4-Pro (Preview) on agent-focused evaluations. On Terminal Bench 2.1 it reaches 82.7, on Cybergym 76.7, and on DeepSWE 54.4 — figures that place it broadly competitive with the strongest proprietary models available today.
The model ships with an attached speculative decoding component, inherited from the DSpark variant. This design choice targets lower latency during generation, which is especially valuable in interactive agent loops where quick turnarounds matter.
DeepSeek-V4-Flash-0731 natively supports the Responses API, simplifying integration for developers who want a structured, modern interface for building agent applications. This removes the need for custom adapters or workarounds.
The model is fully adapted for Codex CLI, meaning developers can plug it into their existing Codex-based workflows without friction. Combined with its strong performance on coding benchmarks like NL2Repo (54.2) and DSBench-Hard (59.6), it's a credible option for repository-level coding tasks.
DeepSeek-V4-Flash-0731 delivers near-frontier agentic performance at a fraction of the activated parameter count.
The efficiency story is the headline here. This model beats its own Pro-tier sibling on nearly every benchmark while being smaller and faster to run. For teams that previously had to choose between capability and cost, this release closes that gap — you get competitive agentic behavior, strong coding scores, and a model that's practical to self-host or serve through standard inference stacks.
You're evaluating models for agentic coding assistants, tool-use pipelines, or autonomous task automation, and you want something that outperforms larger alternatives without the infrastructure overhead. If you already use Codex CLI or the Responses API, the native support removes integration friction. And if you prefer to run models on your own hardware, the documented paths for Transformers, vLLM, SGLang, and Docker Model Runner make deployment straightforward. For teams that benchmark heavily on agent tasks, the numbers here speak for themselves — this is a model that punches well above its weight class.
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