DeepSeek V4.1 Flash hands-on tests and comparisons

The post says DeepSeek V4.1-Flash-1017 scores 77.0 on DeepSWE v1.1 versus Claude Opus 5's 74.0, and opens Search & Fetch APIs at ¥5 per 1,000 calls, free until Nov 1.

Per DeepSeek's own benchmarks it leads Claude Opus 5 on DeepSWE (77.0 vs 74.0) at no price increase, though Opus 5 keeps the edge on Terminal-Bench.

Cost comparison: V4.1 Flash 1017 charges $0.15/$0.60 off-peak and $0.30/$1.20 peak per million tokens, and its coding scores beat GPT-5.6 Sol and Opus 5 on some tests.

A user asserts that fans of DeepSeek V4.1 Flash-1017 are simply enjoying a GPT model at a lower price, without offering evidence or comparison data.

The post summarizes the release as faster runs, tighter outputs and stronger coding and agent results with unchanged API pricing and MIT open weights, while closed labs still lead on a few hard tests.

A third-party October 2026 tier list puts DeepSeek V4.1-Flash-1017 in A tier alongside GPT-6.1 Sol and Sonnet 5.5, below S+ models Opus 5.5, Gemini 4 Argon and Fable 5, and lists its $0.30/$1.20 pricing.

The roundup cites a self-reported 77% on DeepSWE at under $0.20 per task and a search API at $0.75 per 1,000 calls, alongside Anthropic's safety bill pitch and Replit's OpenAI switch.

An article claims DeepSeek's small V4.1 Flash model beats larger closed models at roughly a tenth of the cost, noting China tightened export rules on such models the day before.

A user notes DeepSeek now sells search too: open weights for V4.1-Flash-1017, with Search and Fetch APIs at $0.75 per 1000 calls and no charge when nothing is found.

A user finds V4.1 Flash 1017 markedly better at frontend interaction and presentation, close to the top tier, and so fast they may swap out Opus for rapid iteration.

Roundup lists DeepSeek V4.1 Flash 1017 at 763.2B params, 77.0% DeepSWE at $0.20/task, a $0.10 low setting above 72%, and Search and Fetch APIs at $0.75 per 1k calls.

DeepSeek-V4.1-Flash-1017 can be pulled via Ollama cloud and plugged into Claude Code with two commands; Ollama says it does well on agent tasks at lower cost per task, without specifics.