连夜实测DeepSeek V4 Pro 正式版，低于预期，不推荐接入Codex
Ranking
Overall
71
Content
80
Popularity
N/A
No observed public metrics; popularity remains neutral/archived.
Merged summary
TL;DR - A hands-on review finds DeepSeek V4 Pro inexpensive and operationally robust, but disappointing as a primary Codex model due to limited progress visibility, aggressive reuse of prior context, and weaker-than-expected frontend and writing performance.
- A 41.39M-token API test achieved 100% request success, 52.3K tok/s aggregate throughput, and roughly 96% cache hits for ¥8.5.
- Reasoning comprised 84.5% of output tokens, while sparse user-facing updates made long-running tasks difficult to monitor.
- The model repeatedly reused artifacts and conclusions from earlier sessions, improving efficiency but raising concerns about stale or inappropriate context reuse.
- Latency reached 4.9 seconds at p50 and 12.3 seconds at p99 under the reported workload; frontend generation and writing also trailed expectations.
Sources (1)
连夜实测DeepSeek V4 Pro 正式版，低于预期，不推荐接入Codex
Public signals
N/A
TL;DR - A hands-on review finds DeepSeek V4 Pro inexpensive and operationally robust, but disappointing as a primary Codex model due to limited progress visibility, aggressive reuse of prior context, and weaker-than-expected frontend and writing performance.
- A 41.39M-token API test achieved 100% request success, 52.3K tok/s aggregate throughput, and roughly 96% cache hits for ¥8.5.
- Reasoning comprised 84.5% of output tokens, while sparse user-facing updates made long-running tasks difficult to monitor.
- The model repeatedly reused artifacts and conclusions from earlier sessions, improving efficiency but raising concerns about stale or inappropriate context reuse.
- Latency reached 4.9 seconds at p50 and 12.3 seconds at p99 under the reported workload; frontend generation and writing also trailed expectations.