DeepSeek V4 Pro in OffloopReason through code and large source sets.
Choose DeepSeek Pro in Offloop for text-heavy engineering and research tasks.
A brief to try · Example
Solve a repository issue
Reproduce this issue, locate its owning code and propose a focused patch with a validation plan.
Choose in your agent settings: DeepSeek Pro
Where DeepSeek V4 Pro fits
Our pick for code and document investigations where the source material is primarily text.
01
Solve a repository issue
The model card reports both verified and more demanding repository benchmarks.
02
Research across a document set
Long-context evaluations support testing retrieval and synthesis over large inputs.
03
Investigate a technical question
Reasoning results make it a candidate for stepwise, verifiable analysis.
Our editorial assessment · The benchmark card below covers the original V4 Pro Max evaluation. The video covers the later API release; they are separate snapshots, not a single comparable test.
Read the capability source ↗Watch a real evaluation
A hands-on review of the later DeepSeek V4 Pro API release. Do not equate its results with the original model-card benchmark snapshot.
Bijan Bowen · Independent creator · External setup, not Offloop
Published evaluations
Selected results for DeepSeek V4 Pro. Source checked November 5, 2026; these are not Offloop measurements.
SWE Verified · Max
80.6%
Original model-card resolved rate.
SWE Pro · Max
55.4%
Original model-card resolved rate; a different task set from SWE Verified.
CorpusQA 1M · Max
62.0%
Accuracy in the original long-context evaluation.
Different task sets, model snapshots and harnesses are not directly comparable. Scores are evidence for a trial, not a guarantee of your result.
Before you start
Give your agent a real task.
Choose DeepSeek Pro, connect the tools you need, and define what a useful result looks like.