> ## Documentation Index
> Fetch the complete documentation index at: https://docs.tess.im/llms.txt
> Use this file to discover all available pages before exploring further.

# DeepSeek V4.1 Flash

> Explore the architecture, agentic benchmarks, and best use cases for DeepSeek V4.1 Flash.

DeepSeek V4.1 Flash is a multimodal model from DeepSeek built for **agents, coding, and workloads with large volumes of context**. It combines a 552-billion-parameter Mixture-of-Experts (MoE) architecture with efficient processing: only 8B parameters are activated per token while reading context and 16B during generation.

| **Provider model**<br /><br />`deepseek-ai/DeepSeek-V4.1-Flash`                              | **Context**<br /><br />1M tokens  | **Provider**<br /><br />DeepSeek | **Architecture**<br /><br />MoE · 552B                   |
| :------------------------------------------------------------------------------------------- | :-------------------------------- | :------------------------------- | :------------------------------------------------------- |
| **Capabilities**<br /><br /><Icon icon="brain" /><Icon icon="image" /><Icon icon="wrench" /> | **Input**<br /><br />Text + image | **Output**<br /><br />Text       | **Reasoning**<br /><br />Continuous effort from 1 to 100 |

## Why this Flash model stands out

The core advantage of V4.1 Flash is its ability to **perform more agentic work without loading the entire architecture for every token**. Its combination of Causal Encoder-Decoder and Compressed Sparse Attention 2 reduces the computational cost of long contexts and leaves more room for agents that need to read, plan, use tools, and continue working through many steps.

<Info>
  The model accepts text and images, supports structured JSON output and function calling, and offers up to 1M tokens of context. In the provider implementation, `reasoning_effort` can range from 1 to 100.
</Info>

## What changed from DeepSeek V4 Flash

|                                | DeepSeek V4 Flash           | DeepSeek V4.1 Flash                              |
| ------------------------------ | --------------------------- | ------------------------------------------------ |
| Total parameters               | 284B                        | **552B**                                         |
| Parameters activated per token | 13B                         | **8B while reading / 16B during generation**     |
| Global KV cache                | Reference                   | **Approximately 4× smaller**                     |
| Context                        | —                           | **Up to 1M tokens**                              |
| Focus                          | General-purpose Flash model | **Coding, automation, and context-heavy agents** |
| Multimodal                     | —                           | **Text + image**                                 |

The KV cache reduction is particularly relevant for input-heavy tasks: repositories, long documents, tool histories, and projects that accumulate context throughout execution.

## What the benchmarks show

The results published by DeepSeek were measured at **maximum effort** (`reasoning_effort=100`). They indicate a significant leap in agentic tasks, but do not mean the model leads every evaluation.

| Benchmark          | V4 Flash | V4.1 Flash |                 Market reference |
| ------------------ | -------: | ---------: | -------------------------------: |
| Terminal-Bench 2.1 |     82.7 |   **90.6** | Opus 5: 89.1 · GPT-5.6 Sol: 88.8 |
| DeepSWE v1.1       |     54.4 |   **74.2** | Opus 5: 74.0 · GPT-5.6 Sol: 73.0 |
| AutomationBench    |     37.7 |   **54.8** | Opus 5: 50.3 · GPT-5.6 Sol: 45.8 |
| Agent’s Last Exam  |     25.2 |   **31.8** | Opus 5: 28.6 · GPT-5.6 Sol: 26.7 |
| HLE with tools     |     51.5 |   **63.9** |                     Opus 5: 63.6 |
| CyberGym           |     76.7 |   **88.1** |                GPT-5.6 Sol: 84.5 |

<Tip>
  **The most important signal:** V4.1 Flash does more than improve on V4 Flash. In DeepSWE, Terminal-Bench 2.1, and AutomationBench, it matches or outperforms larger frontier models within the tested configuration.
</Tip>

### Where it still does not lead

The gains are not uniform. In Terminal-Bench 3.0 and 4.0, V4.1 Flash remains behind Claude Opus 5 and GPT-5.6 Sol. It also does not rank first in general reasoning evaluations such as HLE without tools.

This makes the choice clearer: **use V4.1 Flash when the work requires context, tools, and execution; compare other options when the task depends only on pure reasoning or maximum consistency in frontier coding.**

## How to apply it in Tess

The best test is not an isolated question. Give the agent an objective, the required sources, access to tools, and an objective definition of done.

<Steps>
  <Step title="Choose a workflow with real context">
    Use a repository, a document set, or an operation that requires cross-referencing several sources. The architectural advantage becomes visible when there is substantial information to process.
  </Step>

  <Step title="Provide tools and boundaries">
    Enable only the required tools, define what may be changed, and require confirmation for irreversible actions.
  </Step>

  <Step title="Define how to validate">
    Specify tests, required fields, sources that must be cited, or conditions that must be satisfied before completion.
  </Step>

  <Step title="Match effort to difficulty">
    Start with intermediate effort and increase it when the agent needs to investigate more paths, maintain long tool loops, or recover from failures.
  </Step>
</Steps>

## Three playbooks to get started

### 1. Software change in a large repository

**Input:** repository, expected behavior, project standards, and test commands.<br />**Tools:** code, terminal, and test suite.

```text theme={null}
Map the responsible flow before editing. Make the smallest change capable of
solving the problem, add a test that proves the behavior, and run the relevant
suite. If a test fails, investigate and fix it before finishing. Deliver a
summary of the diff, the tests run, and any remaining risks.
```

**Done when:** the change is implemented, the behavior is covered, and the relevant tests pass.

### 2. Document audit with visual evidence

**Input:** contracts, reports, spreadsheets, diagrams, and images.<br />**Tools:** file search and document generation.

```text theme={null}
Cross-reference the documents and visual evidence. Identify discrepancies,
risks, and missing information. For every conclusion, cite the source and state
which passage or visual element supports it. Generate a findings matrix and an
executive summary without filling gaps through assumptions.
```

**Done when:** every finding can be traced to its source and all gaps are explicit.

### 3. Operational automation with multiple tools

**Input:** objective, internal policies, system data, and approval criteria.<br />**Tools:** connectors required to query and update the systems involved.

```text theme={null}
Plan the execution, consult the required sources, and proceed step by step.
Record decisions and validate each tool result before continuing. Recover from
reversible errors and request approval before any high-impact action. Finish
only when every criterion has been verified.
```

**Done when:** systems are updated, exceptions are recorded, and the outcome is auditable.

<Note>
  Benchmarks were published by the provider and depend on the harness, tools, parameters, and effort used. Validate the model in your workflow before replacing a production configuration.
</Note>

## Best practices

* Use V4.1 Flash for coding agents, automations, and tasks with large input volumes.
* Do not treat 1M context as a target: send only the content required for the decision.
* Require sources and validation in legal, financial, or factual tasks.
* Compare it with stronger pure-reasoning models when the workflow does not use tools.
* Keep human confirmation for irreversible or high-impact actions.

See also: [Models and Costs](/en/models-and-cost) · [DeepSeek V4.1 Flash (DeepInfra)](https://deepinfra.com/deepseek-ai/DeepSeek-V4.1-Flash).