DeepSeek V4 Flash Beta: Faster and Cheaper Than Pro

DeepSeek released its V4-Flash-0731 API into public beta on July 31, 2026, scoring 82.7 on Terminal Bench 2.1, up from 61.8 in the preview build and surpassing V4-Pro-Preview's 72.1, while maintaining a price of $0.14 per million input tokens. The model also supports OpenAI's Codex integration and the Responses API format, with V4-Pro Codex support expected in early August. However, data processed through DeepSeek's hosted API goes to servers in China, subject to Chinese law, which may not be suitable for regulated industries.

DeepSeek released its V4-Flash-0731 API into public beta today, July 31, 2026 — and the benchmarks are hard to ignore. On Terminal Bench 2.1, the model scores 82.7 https://api-docs.deepseek.com/updates/ , up from 61.8 in the preview build. That puts it above V4-Pro-Preview at 72.1 — a Flash model outperforming the Pro variant on the benchmark developers use for agentic coding tasks. On DeepSWE, Flash jumped from 7.3 to 54.4, a 7x improvement in a single post-training cycle. Claude Opus 4.8 scores ~85.0 on Terminal Bench, for context. The price remains $0.14 per million input tokens. Flash Beats Pro on Agent Benchmarks — and the Gap Is Not Small DeepSeek’s official API changelog is blunt about what changed: “V4-Flash-0731 keeps the same model architecture and size as V4-Flash-Preview, and was only re-post-trained.” Same weights, different fine-tuning. That post-training was clearly targeted at agentic tasks. Every benchmark published today shows Flash 0731 surpassing V4-Pro-Preview. | Benchmark | Flash Preview | Flash 0731 | Pro Preview | |---|---|---|---| | Terminal Bench 2.1 | 61.8 | 82.7 | 72.1 | | NL2Repo | 39.4 | 54.2 | — | | Cybergym | 38.7 | 76.7 | — | | DeepSWE | 7.3 | 54.4 | — | The Cybergym result deserves a double take — 38.7 to 76.7 is nearly double in one update. DeepSWE going from 7.3 to 54.4 is not a rounding error; it signals that the post-training specifically targeted software engineering agent tasks. When a Flash model beats the Pro on what Pro is supposed to excel at, you have to ask what the Pro is for. The answer right now: wait for the official V4-Pro release, which is coming soon. Codex Integration: DeepSeek V4 Flash Gets There First V4-Flash-0731 is also the only DeepSeek model that currently supports OpenAI’s Codex integration and the Responses API format. V4-Pro Codex support is expected in early August. According to DeepSeek’s Codex integration documentation https://api-docs.deepseek.com/quick start/agent integrations/codex/ , the Responses API offers “more flexible tool calls, more complex multi-round interactions, and more fine-grained output control” compared to standard Chat Completions — which makes Flash the better choice right now for agentic pipelines built on Codex. For developers already on the V4-Flash preview, upgrading to the stable public beta requires exactly one code change: Before V4-Flash preview client.chat.completions.create model="deepseek-v4-flash-preview", ... After V4-Flash public beta — July 31, 2026 client.chat.completions.create model="deepseek-v4-flash", ... Everything else stays the same. No API restructuring, no credential changes. The Cost Math: $0.14 vs. the Field V4-Flash-0731 costs $0.14 per million input tokens and $0.28 per million output tokens. That is 3.1x cheaper than V4-Pro $0.435/$0.87 and 7x cheaper than GPT-5.6 Luna $1.00/$6.00 . Claude Sonnet 5 is $3.00/$15.00 — over 21x the input cost. Even GPT-5.4 Nano, OpenAI’s cheapest model, charges $0.20/$1.25. According to BenchLM.ai’s pricing comparison https://benchlm.ai/deepseek/api-pricing , cache-hit pricing drops V4-Flash to $0.0028 per million input tokens, making it nearly free for repeated system prompts. Output throughput is 81.3 tokens per second, compared to 35.6 tok/s for Pro — 2.3x faster. Consider what this means for high-volume workloads. A team spending $1,000 a month on Claude Sonnet 5 could, in theory, run equivalent V4-Flash-0731 workloads for around $47. That kind of cost delta changes what agentic products are economically viable to build. DeepSeek is not squeezing margins — it is shifting the floor. Two Caveats Worth Knowing Before You Switch First: data processed through DeepSeek’s hosted API goes to servers in China, subject to Chinese law — including the National Intelligence Law, which compels organizations to cooperate with state intelligence requests. For regulated industries GDPR, HIPAA, government , the hosted API is not appropriate. The open-weights release, confirmed by TechNode https://technode.com/2026/07/31/deepseek-puts-v4-flash-api-into-public-beta/ , is available on HuggingFace under MIT license and provides a self-hosting path, but you will need at minimum a single H100 for FP8 inference, or roughly 170 GB VRAM for full weights. Consumer GPU support via llama.cpp and Ollama is not stable yet. Second: this is still public beta. DeepSeek has not published enterprise SLA terms. For workloads where uptime guarantees matter, the API is usable but not certified. The official V4-Pro release, and V4-Pro’s Codex integration, are both expected in August — so the current state is not the final word. Key Takeaways - V4-Flash-0731 outperforms V4-Pro-Preview on every agent benchmark published today — Terminal Bench 2.1 82.7 vs 72.1 , DeepSWE 54.4 vs Flash’s prior 7.3 . A Flash model beating the Pro is the headline. - Codex integration is available now on V4-Flash only; V4-Pro support arrives in early August. Upgrading from preview to public beta requires changing one line of code. - At $0.14/M input, V4-Flash is 3.1x cheaper than V4-Pro, 7x cheaper than GPT-5.6 Luna, and 21x cheaper than Claude Sonnet 5 — while now matching near-frontier agent performance. - The hosted API sends data to China-based servers. Teams with regulated data should self-host via the MIT-licensed HuggingFace weights, which require H100-class hardware. - The official V4-Pro release is forthcoming. Today’s update makes V4-Flash the rational default for agent workloads until then.