DeepSeek V4 comparison: verify findings before counting the winner

Record the setup and the question being tested

The original title compares DeepSeek V4, ChatGPT 5.5 and Claude Opus 4.7. The description specifies DeepSeek v4 Pro and v4 Flash, a real landing-page redesign and bug hunting. It reports that Claude’s redesign looked better, while DeepSeek was slower and crashed when routed for latency. These observations concern this recorded setup, not every deployment of those models.

1. Follow cost and caching at 2:10, then the real-code setup at 8:35.
2. Record the exact model variant, routing choice and starting repository state before each attempt.
3. Keep the redesign request and the bug-hunting request distinct so visual preference does not substitute for correctness.

Check what the bug report actually establishes

The source says Claude and ChatGPT each caught key issues and DeepSeek reported the most bugs: 13, including one critical. It also notes false positives in the overall synthesis. A larger list therefore needs individual verification before it supports a quality judgement.

1. Use the bug-hunt process at 12:20 and the report comparison at 17:41 as the viewing route.
2. For each finding, record the affected behaviour, expected rule and evidence that reproduces the problem.
3. Separate confirmed defects, duplicate reports and false positives rather than comparing only the headline totals.
4. After a repair, repeat the affected behaviour and keep untested claims marked as unresolved.

Compare the cost of a useful completed task

The description reports DeepSeek models as about ten times cheaper and discusses data and cost tradeoffs. Treat the ratio as a historical account of this run. Caching, routing, failed attempts and follow-up work can change the practical comparison; a low quoted model price does not establish the lowest cost to finish a task.

1. Keep usage, delays, crashes and revisions beside the accepted result in your comparison notes.
2. Inspect the data-handling route before supplying sensitive repository material.
3. Judge the landing page by its actual visitor task as well as appearance, then choose a model for the next specific job.

The takeaway

Prefer verified results and the cost of reaching them over a raw finding count or a general benchmark claim.