DeepSeek V4.1 Flash vs DeepSeek V4 Pro 0813 vs. the closest competing models

DeepSeek-V4.1-Flash is the cost-efficient, high-throughput model tier in DeepSeek's V4 family. It's upgraded version of previous Flash generation and replaces experimental endpoints, now it have native multimodal vision capabilities and published under MIT-licensed with open weights.

V4.1-Flash is optimized for input-heavy, iterative workflows rather than pure heavyweight one-shot reasoning:

• Optimized for multi-turn task loops, terminal use, and computer-use automation
• Works well with persistent framework runners (such as Pi, Codex, Claude Code, mini-SWE, opencode and local developer harnesses)
• Handles mixed media input out-of-the-box (e.g., parsing system architecture charts, screenshot-to-code pipelines, UI testing scripts, and dense tabular document scans)
• Supports fine-grained reasoning effort levels: Off, Low, Medium, High, Max

From the model card (all instruct results at reasoning_effort=100):

• AutomationBench & Agent's Last Exam clearest wins: 54.8 vs Opus 5 at 50.3 and GPT-5.6-Sol at 45.8; 31.8 vs Opus 5 at 28.6.
• CyberGym 88.1, ahead of GPT-5.6-Sol and GLM-5.3 at 84.5. No Opus 5 number was reported.
• DeepSWE v1.1 & HLE w/ tools 74.2 and 63.9, statistically tied with Opus 5 (74.0, 63.6); the card treats gaps under 0.3 as equivalent.
• Terminal-Bench 90.6 on 2.1, the top result. On the harder 3.0 and 4.0 it scores 30.0 and 31.2, well behind Opus 5 at 43.3 and 51.8.

Where it trails: HLE without tools (36.8 vs 56.3), ProgramBench, NL2Repo-Bench, and all three visual agent benchmarks.
Scaffold sensitivity: DeepSWE v1.1 ranges 65.5–74.2 for the same weights depending on harness. Treat sub-point margins accordingly.

DeepSeek-V4.1-Flash model card