As an open-source, neutral Agent framework, CowAgent focuses on how models perform in real Agent workflows (tool calling, long contexts, long-term memory, browser automation, and knowledge organization). It tested DeepSeek V4 in six real-world scenarios, with this evaluation focusing primarily on deepseek-v4-flash.
Its price is roughly one-tenth that of Pro, a few dozen times lower than Claude Sonnet/Opus, and one-third that of MiniMax M2.7, while its response speed is faster.
Model: deepseek-v4-flash; extended thinking enabled, with reasoning_effort set to high by default (max can be used)
Maximum of 50 steps per task; 20 rounds of conversation history retained; context token limit of 1 million
Tools: 13 built-in tools (bash / edit / read / write / web_search / web_fetch / browser, and others)
Skills: 30+ Skills (frontend-engineer / image-generation / video-gen / pptx-creator, and others)
| Scenario | Focus | Time | Tool calls | Result |
|---|---|---|---|---|
| s1 Task planning and skill scheduling | Multi-tool/Skill coordination and long-chain planning | 229.5s | 35 | Successful; the workflow completed in one run with no redundancy |
| s2 Complex interactive programming | Single-file frontend with no dependencies | 381.7s | 28 | Successful; proactively wrote in chunks and reviewed the result with browser screenshots |
| s3 Long-term memory | Cross-session memory retrieval and reasoning | 142.4s | 2 | Successful; 14 memories retrieved precisely |
| s4 Browser automation (Xiaohongshu) | Multi-step operations on a real site and login-state handling | 124.4s | 8 | Successful; QR-code handling was a highlight |
| s5 Automated knowledge-base construction | Online research and knowledge-graph organization | 210.6s | 26 | Successful; 13 documents linked to one another |
| s6 Ultra-long-context processing | Processing the full 560,000-word text of War and Peace | 156.3s | 50 | Successful; saved the text to disk first, then searched to locate the relevant content |
Task planning: 35 tool calls were restrained and precise; decomposition → research → consolidation → PPT → knowledge base completed in one run
Complex programming: Proactively wrote in chunks to avoid token truncation and called the browser to review screenshots (a stability fallback added by the model). Shortcoming: despite the requirement of "zero external dependencies," it still referenced the ECharts CDN
Long-term memory: In a brand-new session, just 2 tool calls retrieved all brand details (visual colors, suppliers, rent, and salaries) accurately, then produced structured recommendations
Browser automation: When it reached a login page, it proactively captured a QR code and paused for the user; before publishing, it stopped at the button and waited for confirmation, showing careful Agent safety details
Knowledge base: It split the material into concept pages, server pages, and client pages and linked them to one another. Each page ended with "Related reading," creating a genuine graph
Ultra-long context: Instead of stuffing the 3.36 MB full text into the context, it first saved it to disk, used grep to locate line numbers, and read it in segments—the long-context capability an Agent needs is knowing "what to put into context and when to use a tool"
Zero failures, zero infinite loops: All six scenarios completed successfully in one run, the most significant upgrade from V3 to V4
Fast responses: Every scenario finished in 2–6 minutes, with steps streamed as they ran and low perceived latency
Flash is now stable enough to serve as the default Agent model, and its long-term memory and long-context performance exceeded expectations
Weakness: It occasionally relaxes complex constraints (for example, still pulling in a CDN despite "zero external dependencies"); Pro is probably more reliable in complex scenarios
DeepSeek V4 Flash
