V4.1 Flash: DSH Minimal, Standard, or PTC
V4.1 Flash: DSH Minimal vs Standard vs PTC
- Author
- DeepSeekAgent.io Editorial Team
- Published
- Updated
The DeepSeek V4.1 Flash model card compares the same model across several Agent scaffolds, including DSH Minimal, DSH Standard, and DSH PTC. It demonstrates an important operational point: changing the Harness mode can materially change task success even when the model stays fixed.
This guide combines DeepSeek's official scaffold benchmark, DSH architecture documentation, and early community reports. The table below comes from DeepSeek's official model card, while community experiences are labeled separately.
Official results: Minimal leads on both reported tasks
DeepSeek used V4.1 Flash at maximum reasoning effort with temperature=1.0, top_p=0.95, a one-million-token context, and up to 500 Agent steps. DeepSWE v1.1 used eight samples per task; Terminal-Bench 2.1 used three and disabled network access.
| Official benchmark | DSH Minimal | DSH Standard | DSH PTC |
|---|---|---|---|
| DeepSWE v1.1 (Resolved) | 72.6 | 70.5 | 67.6 |
| Terminal-Bench 2.1 (Pass@1) | 90.6 | 85.8 | 85.8 |
The official result gives a clear choice for these workloads: start with Minimal for DeepSWE- and Terminal-Bench-style coding tasks. Everyday work also involves attachments, Web access, plugins, and interaction design, so mode choice still follows the task.
What each mode is good for
Minimal: a narrow surface for reproduction and coding
Minimal reduces the visible tool set and surrounding structure, giving the Agent a smaller execution surface. It fits:
- coding fixes, terminal work, and benchmark reproduction;
- tasks where fewer tool choices can reduce branching;
- experiments that need clear command, edit, and result records;
- work isolated in a container or disposable checkout.
The smaller surface also asks the Agent to compose some conveniences through the shell, giving it a higher operating threshold than Standard.
Standard: the complete everyday product experience
Standard exposes a broader set of default tools, workspace context, and product features. It fits:
- everyday file browsing, images, Web tools, and plugins;
- interactive work with several input types and visible feedback;
- users who want a useful default before learning Profile internals.
Additional tools create more choices and constraints for the model. If a run repeatedly chooses the wrong tool, adds unnecessary turns, or expands its plan, compare the same task in Minimal.
PTC: programmatic orchestration for batch operations
PTC lets the model generate code that composes multiple tool operations, reducing round trips for suitable work. It fits:
- bulk reading, filtering, or transformation across many files;
- independent checks that can run in parallel;
- repeated tool sequences that can be expressed as one program;
- operators who can review the generated execution logic and outputs.
PTC does not lead DeepSeek's two reported benchmark rows. Its advantage is batch orchestration and fewer tool round trips; quality depends on whether the work decomposes cleanly into programmatic steps.
What early community reports suggest
Initial V4.1 Flash discussions contain opposing experiences. Some users report weaker persistence and completion on real projects; others say switching from Standard to Minimal or PTC improved speed and token use. Tasks, DSH versions, endpoints, quantization, and measurement methods vary; the mode-dependent difference is the part worth testing.
The official table and community reports point in the same direction: V4.1 Flash performance changes materially with Harness structure. When a result is unstable, hold the model, effort, and task constant while changing only the mode to isolate model, tool, and prompt effects.
A simple choice for everyday users
Start with Standard to learn the workspace, attachments, and tools. If the task is mostly coding or terminal work and Standard takes obvious detours, repeat it in Minimal. Try PTC when the work contains many repeated or parallelizable operations and you understand the execution boundary.
A useful sequence is:
- Run Standard once to confirm the requirement and available capabilities.
- Repeat the same input in Minimal and compare turns and detours.
- Test PTC only when the task can be expressed as batch tool operations.
- Choose using final quality, manual rework, runtime, and cost together—not tokens alone.
Reproduce the comparison on your workload
Prepare at least three task types: a single-file fix, a cross-file feature, and a repository task with repeated checks. Keep the initial checkout, prompt, model ID, reasoning effort, and acceptance tests identical across modes.
| Dimension | What to record |
|---|---|
| Completion quality | Automated tests, acceptance items, and manual rework |
| Execution efficiency | Total runtime, Agent steps, and tool calls |
| Cost | Input, output, cache tokens, and billed amount |
| Stability | Stalls, loops, retries, and human takeovers |
| Auditability | Whether the trajectory explains the critical changes |
Run every task and mode at least three times, and retain failures and the complete configuration so one sample does not dominate the conclusion.
Current recommendation
- Pure coding and benchmark reproduction: start with Minimal.
- Everyday work and the complete tool experience: start with Standard.
- Batch, parallel, programmatic tool work: test PTC.
- Critical production workflows: benchmark all three on your own tasks.
Related reading
- DeepSeek V4.1 Flash overview
- Configure V4.1 Flash in DeepSeek Harness
- Reproduce DeepSWE with DSH Minimal
- V4 Pro now routes to V4.1 Flash
FAQ
Which DSH mode scores highest in DeepSeek's benchmark?
DSH Minimal reports 72.6 on DeepSWE v1.1 and 90.6 on Terminal-Bench 2.1, ahead of Standard and PTC in the same official table.
Should a new user immediately choose Minimal?
Standard is a practical starting point for learning the full product. Compare Minimal when the task mainly uses code and terminal operations or Standard takes unnecessary detours.
Does PTC always use fewer tokens?
It can reduce tool-call round trips on batch work, but total tokens and cost also depend on generated code, tool output, retries, and task structure. Measure the same workload.