This article evaluates DeepSeek V4 on the CowAgent platform, focusing on how the model behaves in realistic agent workflows rather than only in chat or isolated coding tasks.
The benchmark centers on deepseek-v4-flash and examines six capability areas: task planning, complex coding, long-term memory, browser automation, knowledge-base construction, and long-context handling.
Test Environment
All tests were run in CowAgent’s web interface with independent sessions for each scenario. The setup used deepseek-v4-flash with deep-thinking mode enabled, a high reasoning effort by default, a fifty-step task limit, twenty turns of retained history, a one-million-token context ceiling, thirteen built-in tools, and more than thirty skills.
Scenario Design
The six scenarios were chosen to reflect practical agent work. They included multi-tool planning, interactive front-end coding under strict constraints, cross-session memory retrieval, Xiaohongshu browser automation with login handling, online research plus knowledge-base organization, and precision retrieval from an extremely long document.
Scenario 1: Planning and Tool Orchestration
The model prepared an internal sharing session on enterprise AI-agent adoption, including research across customer service, marketing, and engineering, an eight-slide presentation, and a knowledge-base summary. It completed the task in 229.5 seconds with 35 tool calls and showed stable multi-tool planning with no loops or skipped steps.
Scenario 2: Complex Interactive Coding
The model built a single-file real-time dashboard called “ATLAS AI Global Operations Center,” including charts, maps, rankings, event streams, and simulated front-end data. The task took 381.7 seconds and 28 tool calls.
A notable strength was self-verification: the model reopened the page and reviewed screenshots without being told to do so. The main weakness was that it still referenced an ECharts CDN even though the prompt required zero external dependencies.
Scenario 3: Long-Term Memory
The benchmark stored fragmented brand information for a coffee shop in one session and then, in a separate new session, asked the model to create a thirty-day operating plan and recommend a store manager. It completed the second phase in 142.4 seconds with only two memory-related tool calls and retrieved detailed facts accurately.
Scenario 4: Browser Automation
The browser task required searching Xiaohongshu for DeepSeek posts, summarizing popular-content patterns, drafting a post locally, and then opening the publishing page. The model paused appropriately when a login QR code was needed and stopped again before the final publish action, demonstrating useful restraint in a live environment.
Scenario 5: Knowledge-Base Construction
The model researched the Model Context Protocol ecosystem and built a structured knowledge base with index pages, concept pages, server pages, client pages, and cross-links. It finished in 210.6 seconds with 26 tool calls and wrote 13 documents.
Scenario 6: Long-Context Processing
For the long-context test, the model downloaded the full English text of War and Peace from Project Gutenberg, then summarized the work, identified major narrative lines, and answered detailed textual questions with chapter references.
Instead of stuffing the full text into context, it saved the file locally, kept only an initial slice in context, searched for relevant keywords through terminal commands, and then loaded precise segments for analysis. The article presents this as the most practical long-document workflow for real agents.
Overall Conclusion
Across all six scenarios, the model completed each task in a single pass without failure or dead loops. The author argues that deepseek-v4-flash is now stable enough to serve as CowAgent’s default model, with especially strong performance in memory and long-context work.
The main remaining limitation is stricter compliance with complex constraints. For harder tasks, the article recommends switching to the Pro version and increasing reasoning effort.