DeepSeek V4.1 Flash hands-on tests and comparisons

A user reports that OpenCode's free tier, which runs V4.1 Flash 1017 through DeepSeek Harness, has seen its speed drop sharply.

A user warns V4.1 Flash 1017 underperforms below DeepSeek Harness 0.2.1-rc.1, while npx still installs 0.2.0-rc.2, so build from the official repo to try it fastest.

GGUF quants are out: Q2_K ~265GB, Q3_K_M ~350GB, Q4_K_M ~445GB, with Q3 fitting on four DGX Sparks; text-only for now and requires the llama.cpp V4.1 PR branch, plus a ~115GB single-Spark VQ build in progress.

The author plugged Claude Opus 5.5 into Palmier Pro to generate a video and says its visual taste clearly beats GPT; also recommends DeepSeek V4.1 Flash + WorkBuddy for Chinese users.

The post compares model-card numbers: 77.0 vs 74.0 on DeepSWE, 35.4 vs 28.6 on Agents' Last Exam and 60.5 vs 50.3 on Automation-Bench ahead of Opus 5, but 42.3 vs 51.8 on Terminal-Bench 4.0 and 42.6 vs 56.3 on HLE behind it, with 763B parameters under MIT.

The author contrasts DeepSeek V4.1-Flash's wish-style coding with Nvidia Nemotron 3.5's distillation, arguing both aim to make intelligence cheaper.

A user has Opus 5.5 build the procedure while DeepSeek V4.1 Flash-1017 handles routine runs plus Codex image generation, noting Flash feels sluggish but keeps progressing autonomously.

DeepSeek released open-source agent harness DSH 0.2.1 and says the same-day V4.1-Flash-1017 was optimized for DSH from the training stage to cut execution time and token cost.

A test claims DeepSeek V4.1 Flash-1017 High is over 85% cheaper and twice as fast as Gemini 4 Argon High at under 0.3 cents per API run, with close quality.

The poster argues V4.1-Flash-1017 was post-trained for DeepSeek Harness, so dropping its API into Codex and other existing harnesses frequently underperforms.

DeepSeek V4.1-Flash-1017's API now includes web Search and Fetch endpoints for real-time content in agents and RAG, with DeepSeek Harness updated to 0.2.1-rc.2.

The author runs V4.1 Flash 1017 on a Spider-Man-riding-a-bicycle prompt instead of the usual pelican test, shares the output, and asks Claude and GPT users to post the same test for comparison.