DeepSeek V4 Flash public beta released with agent performance exceeding V4 Pro Preview

DeepSeek launched V4.1-Flash today, the smallest model in a new architecture family with native multimodal vision. The new design targets higher ceiling, faster inference, and larger throughput, and is meant to scale to bigger models. V4.1-Flash scores 90.9 on GPQA Diamond, 3471 Codeforces rating, and 36.8 on HLE. Set model name to deepseek-flash in the API; old V4 Flash and V4 Flash Vision Exp are offline and requests are temporarily routed to V4.1-Flash. DeepSeek also claims V4.1-Flash beats V4 Pro on performance, cost, and speed, so V4 Pro requests will be routed to V4.1-Flash starting Sep 14 and billed at Flash rates. API pricing is cut, but the post doesn't list the new numbers—check the pricing page.

DeepSeek updated V4 Flash API on July 31, keeping the 284B-total / 13B-active MoE architecture and applying re-post-training only. Artificial Analysis measured an Intelligence Index of 50, up 10 points from Preview, placing it alongside Gemini 3.6 Flash and GPT-5.6 Luna in the Nano/lightweight tier. Cache-miss input costs $0.14/1M tokens, dropping to $0.0028 on long-context cache hits, with a blended ~$0.06 under typical workloads—genuinely the lowest price band. Three deployment concerns stand out: the self-reported DeepSWE score of 54.4 uses an undisclosed custom harness and cannot be compared directly to Opus 4.8's 58 under standard blind evaluation; hallucination rate remains at 84% with max verbosity, and tool calls frequently emit null optional fields, escaped strings, and markdown-link-wrapped paths; real agent economics hinge on cost per accepted task—open-ended tasks risk multi-turn token burn, while deterministic pipelines with hard validation rules benefit from the low unit price. The post recommends adding a tool-calling repair layer, capping output length, and using a flagship model as controller to dispatch sub-tasks to Flash.

DeepSeek released V4-Flash-0731 on Product Hunt, the official version of V4-Flash. It claims better agentic performance than V4-Pro Preview, native Responses API support, and full adaptation for Codex CLI. The post doesn't disclose benchmark scores or exact pricing, only the headline 'frontier agent intelligence at Flash prices.' I'd wait for third-party evals and API cost details before drawing conclusions.

DeepSeek opened V4-Flash API for public beta. The post claims agent benchmark scores now far exceed V4-Pro-Preview, with native Responses API support and full Codex integration. The body only shows a title and a performance chart—no specific scores, pricing, or latency numbers are disclosed, so I'd hold off on the 'huge leap' claim until real-world tests appear.

DeepSeek opened V4 Flash to public beta. Call it with model name deepseek-v4-flash, same API. Only Flash was updated; V4 Pro and App/Web models are unchanged. Agent scores are a big leap over V4 Pro Preview: Terminal Bench 2.1 hit 82.7, Cybergym 76.7, DSBench-FullStack 68.7. Same architecture and size as Flash Preview, only re-post-trained. It natively supports the Responses API format and is adapted for Codex. V4 Pro is promised “soon” with no date given. I'd discount the internal DSBench scores until third parties replicate them—the post doesn't disclose difficulty or representativeness.

DeepSeek released the V4 Flash production API for public testing today. Only post-training changed; model architecture and size stayed the same. Agent scores jumped—Terminal Bench 2.1 hit 82.7, DeepSWE 54.4, which the team says far exceeds the V4 Pro preview. Flash now natively supports the Responses API format and is tuned for Codex. The V4 Pro production version is still “coming soon.” Only the API endpoint was upgraded; the app and web versions remain unchanged.