DEEPSEEK

DeepSeek v3.2

Максимум логики и анализа

What it's the best tool for

Massive 128K context — whole repos and long PDFs
DSA sparse attention: fast and cheap on long inputs
Strong code and math, on par with V3.1-Terminus
Native tool-use for agent pipelines
Open weights — no vendor lock-in, low token cost

Limitations

When to reach for something else

Content filters tuned for Chinese policy
No native web search
Text only — no image or video generation
Not a reasoning model — use the R-series for deep chain-of-thought
Quality drops on low-resource languages vs GPT-5

Sample output

How DeepSeek v3.2 responds

Prompt

Review this 100K-token repo and list the top 10 spots where O(n²) logic can move to O(n log n). Explain why each is critical.

DeepSeek v3.2

Scanned 47 files. 1) utils/search.py:42 — linear scan in a hot loop; switch to a hash set. 2) services/matcher.py:118 — nested for over users; index it. 3) analytics/agg.py:201 — O(n²) sort; replace with heapq.nlargest. Patches and microbenchmarks attached below.

Where teams use it

Four scenarios where it pays for itself

Reviewing large repos

128K context — no chunking

Long documents

Whole PDFs, reports, contracts in one pass

Agent tool chains

Native tool-use in a single model

Code migrations

Frontier-adjacent quality at open-source pricing

About model

More about DeepSeek v3.2

DeepSeek v3.2 Free Online — Open-Source LLM for Code and Reasoning

DeepSeek v3.2-Exp is a flagship open-source MoE model with 685B total parameters and 37B activated per token, released in September 2025. Try it on NetRoom — straight in the browser, no VPN, no foreign card.

What is new in DeepSeek v3.2

The headline feature is DeepSeek Sparse Attention (DSA). A lightning indexer picks 2048 key tokens per query, dramatically cutting compute on long inputs while keeping output quality on par with V3.1-Terminus. In practice that means cheaper and faster runs on the workloads that hurt your budget — long PDFs, full repos, multi-turn agent chains.

The context window is 128K tokens — enough for an entire mid-size codebase, a long legal contract, or a quarter of support logs in a single prompt.

Strengths

On standard benchmarks V3.2 stays level with V3.1-Terminus, and on long-context evaluations like Fiction.liveBench it pulls ahead. Strong code and math, native tool-use for agent pipelines, fully open weights — no vendor lock-in if you want to self-host later.

Who DeepSeek v3.2 is for

Engineers running PR reviews, framework migrations, and large-module refactors that previously required chunking. Analysts and ops teams parsing long reports and contracts. Teams hitting cost ceilings on GPT-5 or Claude Sonnet who want frontier-adjacent quality at open-source pricing.

Why use DeepSeek via NetRoom

Hosting DeepSeek yourself takes serious GPU memory; the official API can mean payment friction for some regions. NetRoom gives you DeepSeek v3.2 free online through a single chat interface — local payment methods, no foreign cards, no Cloudflare gymnastics. Open the page, drop your prompt, ship.