---
title: "DeepSeek V4.1 Flash tops agentic-coding benchmark at 74.2%"
slug: "deepseek-v41-flash-tops-agentic-coding-benchmark-at-742"
published: "2026-10-07"
beat: "Research"
tags: ["Research", "Launches"]
creator: "Agentry Newsroom"
editor: "Susanne Sperling, Editor — Human in the Loop"
tools: ["Claude (Anthropic)", "Perplexity Sonar"]
creativeWorkStatus: "verified"
dateReviewed: "2026-10-07"
aiActArticle50: "compliant"
humanView: "https://agentry.news/launches/deepseek-v41-flash-tops-agentic-coding-benchmark-at-742"
agentView: "https://agentry.news/agent/deepseek-v41-flash-tops-agentic-coding-benchmark-at-742"
---# DeepSeek V4.1 Flash tops agentic-coding benchmark at 74.2%

> DeepSeek's V4.1 Flash model achieved 74.2% on the DeepSWE v1.1 benchmark, according to technical documentation released September 10, narrowly surpassing reported scores for Anthropic Opus 5 and OpenA

*Drafted by an AI agent. Verified by Susanne Sperling, Editor — Human in the Loop. [AI policy](/ai-policy).*

DeepSeek's V4.1 Flash model reported a 74.2% score on the DeepSWE v1.1 benchmark for agentic software engineering tasks, according to [DeepSeek technical documentation](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blame/df42c109f1defefcbfcedbe7d905718a12266e40/README.md) released September 10, 2026. The result was conducted using DeepSeek's own evaluation harness with a 1-million-token context window.

## Benchmark Results and Context

The reported score places DeepSeek V4.1 Flash ahead of comparison figures cited for Anthropic Opus 5 at 74.0% and OpenAI GPT-5.6 Sol at 73.0% on the same DeepSWE benchmark. However, the available primary sources do not independently verify the Anthropic and OpenAI comparison scores, which appear only in secondary materials rather than official model documentation or regulatory filings.

DeepSWE v1.1 is a benchmark designed to evaluate agent performance on real-world software engineering workflows. The test represents a concrete capability measure relevant to developers building agentic systems for code generation, debugging, and task automation.

## Independent Validation Status

The evaluation results come directly from DeepSeek's technical harness and have not been reported as independently validated by a third party, regulator, or academic institution in the available sources. This distinction is material: benchmark claims released by model providers themselves require independent replication to establish credibility across the industry.

## Product Shipping Status

V4.1 Flash is available as a shipped product. The model is accessible via [Hugging Face](https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash/blame/main/README.md), [NVIDIA Build](https://build.nvidia.com/deepseek-ai/deepseek-v4.1-flash/modelcard), and integration platforms including [Baseten's model APIs](https://www.unite.ai/baseten-adds-deepseek-v4-1-flash-to-model-apis-with-1m-token-context/), with documented support for 1-million-token context windows.

## Relevance to Agent Developers

For teams building autonomous coding agents—a core use case in the agent economy—model performance on software engineering benchmarks directly impacts task completion rates and reliability. DeepSeek's entry into this capability space, particularly with a "Flash" variant optimized for inference speed, represents a tangible product development in the competitive agentic-reasoning market.

The absence of independent benchmark validation, however, underscores an ongoing industry challenge: comparing agent capabilities across models from different providers requires standardized, third-party testing protocols that remain underdeveloped at scale.