DeepSeek V4.1 Flash hands-on tests and comparisons

The deepseek-flash call name and pricing are unchanged so no code changes are needed; Terminal-Bench 4.0 rose from 31.2 to 42.3, coding gains are largest, and outputs are shorter, saving tokens.

A user says the 1017 update is stronger and cheaper, producing games and 3D work (Forbidden City 3D, a racing game, a Portal 2 remake) from scratch, plus a search API at 5 yuan per thousand calls and weights on HuggingFace.

Commentary on the surprise Saturday launch: doubts that it beats Opus 5.5, questions why it is compared to Sol 5.6 or Opus 5 when the prior V4.1 Flash scored higher, and asks when t3code will add DeepSeek Harness.

The poster points a local DeepSeek V4.1 running on two Sparks at an app holding personal decisions and finances, says it surfaced a blind spot and eased stress, and just learned of the 1017 upgrade.

The poster says the new V4.1 Flash costs just over one yuan per DeepSWE task and produced a 3D Forbidden City and a racing game from single prompts, then asks whether people would pick pricey flagships or this cheap strong Flash.

A user running V4.1 Flash with DSH on local documents reports it first searches all local files then the web for unknown concepts, and feels faster and smarter.

The user reports Terminal-Bench rising from 31.2 to 42.3, faster tasks at the same price, and clearly better one-shot 3D scenes than the old version, while noting these are official numbers pending own tests.

The post claims V4.1 Flash 1017 scores 42.8 on a terminal benchmark and reaches SOTA on DeepSWE, ahead of GPT-5.6 Sol, Kimi K3 and GLM 5.3, with low pricing, shorter outputs, 1M context and unchanged API rates.

Per DeepSeek's own chart, Flash costs about $0.20 per task and scores 77.0 on DeepSWE vs Opus 5's 74.0, though Opus still leads Terminal-Bench 51.8 to 42.3.

The post says it edges past Opus 5 on DeepSWE, ships MIT open weights, is live on OpenRouter, keeps the prior Flash price, and has a surprisingly low cache price.

DeepSeek calls it a Pareto improvement on performance and cost: DeepSWE 77.0 above Opus 5's 74.0 and GPT 5.6-Sol's 73.0, about $0.2 per task at max tier, with Harness and CodeBuddy adaptations.

DeepSeek's new Flash model is live across web, app and API with open weights, hitting 77% on DeepSWE at about 1.3 yuan per task and improving all 11 benchmarks.