DeepSeek V4 Flash Exits Preview: Pricing, Benchmarks

DeepSeek moved its V4 Flash model out of preview on July 31, 2026, shipping it as build DeepSeek-V4-Flash-0731 through the same deepseek-v4-flash API string. Pricing holds at $0.14 per million input tokens ($0.0028 with a cache hit) and $0.28 per million output tokens — unchanged from the preview — while the model posts sharply higher agent-benchmark scores after a full post-training redo.

What actually changed

DeepSeek's own changelog is explicit that this is not a new model: "DeepSeek-V4-Flash-0731 keeps the same model architecture and size as DeepSeek-V4-Flash-Preview, and was only re-post-trained." The 284-billion-parameter Mixture-of-Experts architecture, its ~13-billion active parameters per token, and the 1-million-token context window all carry over unchanged. What DeepSeek retrained is tool-calling behavior, coding workflows and agentic reasoning — the parts of a model that determine how well it functions inside an autonomous coding loop, not how big it is.

Two API-level additions ship alongside the retrain: native support for the Responses API format, and adaptations specifically for running the model inside Codex-style agent harnesses. Existing integrations that already call deepseek-v4-flash pick up the new behavior automatically — DeepSeek did not introduce a new model identifier, so there is no migration step for teams already on the preview build.

Pricing stays flat, for now

DeepSeek's pricing documentation also discloses a planned 2x multiplier during Beijing-time peak hours (09:00–12:00 and 14:00–18:00, UTC+8), but the effective date is "subject to official announcement" — it is not active yet. Teams billing against DeepSeek's API today should budget for the flat rate above and watch the changelog for when peak pricing goes live, since it would meaningfully change cost math for workloads that run during Chinese business hours.

For context on where that leaves DeepSeek relative to other vendors, see CodingSalt's AI Model API Pricing Comparison: Gemini 3.5 Flash-Lite was the previous cheapest tier tracked there at $0.30 input / $2.50 output. DeepSeek V4 Flash's $0.14 / $0.28 undercuts that on both legs, and beats it by roughly 9x on output specifically.

Benchmark scores (vendor-reported)

DeepSeek published nine agent-focused benchmark results for the 0731 build. These numbers come directly from DeepSeek's own changelog — they are vendor-reported, not independently reproduced, so treat them as directional rather than final. CodingSalt's guide to reading LLM benchmarks covers why vendor tables in general need outside verification before they drive a purchasing decision.

DeepSeek says these results "far exceed" DeepSeek-V4-Pro-Preview, the larger model in the same family — an unusual claim worth noting on its own, since it means the smaller, cheaper Flash tier now reportedly outperforms its own larger sibling on agentic tasks pending a matching Pro retrain.

What this means for developers

• No code changes required. If your integration already targets deepseek-v4-flash, you're running 0731 already; there's no new endpoint or schema to adopt.
• Re-run your own evals before switching workloads onto it. Vendor benchmarks moved a lot in one retrain; whether that translates to your specific coding-agent or tool-use workload is only knowable by testing against it directly, the same caution that applies to any vendor-reported benchmark table.
• Budget headroom for peak pricing. The documented but not-yet-active 2x peak multiplier is a cost variable worth tracking if you route meaningful volume through this API, especially for workloads that would otherwise overlap Beijing business hours.
• Don't expect open weights yet. Teams that need to self-host should keep using the existing April preview weights on Hugging Face until DeepSeek publishes a 0731 checkpoint, if it does at all — API-only releases before a weights drop aren't unusual for this family.

A simple request against the API is unchanged from the preview build:

curl https://api.deepseek.com/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $DEEPSEEK_API_KEY" \
  -d '{
    "model": "deepseek-v4-flash",
    "messages": [{"role": "user", "content": "List three risks in this diff."}]
  }'

For teams already comparing open-weight MoE options, DeepSeek V4 Flash's retrain lands in the same window as Moonshot AI's Kimi K3, another large open-weight MoE model aimed at agentic coding — worth a side-by-side eval if cost per completed task, not just cost per token, is the deciding factor.