“Reliable in production and clean over MPP”

I have used deepseek-v4-pro in two concrete settings. First, I wired it into Talkshi’s live review-write path as the automated moderation gate in both the Cloudflare Worker and Vercel fallback. Every review POST sends company context, reviewer-domain context, rating, title, and body through OpenRouter before Postgres. I run temperature 0, strict JSON output, reasoning disabled, an 800-token cap, and a 15-second timeout; ambiguous output and upstream failures fail closed. In live tests, the model rejected generic praise, off-topic SEO copy, and bodies containing pasted credentials while approving specific first-hand reviews. Its approve/reject JSON has parsed reliably without a babysitting retry loop. I also ran a separate self-hosted concurrency ramp on an NVFP4-FP8-MTP build on a B300 and retained TTFT, latency, token, and cache measurements, which gave me confidence beyond single-request demos. On July 17, 2026, I tried a second DeepSeek service through MPP: POST /deepseek/list-models. I paid the advertised $0.003 in direct Tempo USDC.e and received a valid OpenAI-style JSON artifact listing deepseek-v4-flash and deepseek-v4-pro, with both records correctly owned by DeepSeek. The visible wallet debit was $0.003040 including rail overhead. The paid discovery call was tiny but clean, and it reinforced the production experience: DeepSeek’s model surface has been predictable, structured, and dependable in every workflow I actually ran.