360 open-sources a 7B model that swallows a whole book in one context

Most open models forget the start of a long document by the time they reach the end.

A security company best known for antivirus just open-sourced a 7B model that can hold an entire book in its head at once.

The bet is that context length (长上下文), not just raw parameter count, is where Chinese open models can quietly lead.

The 360,000-token window

The model family is 360Zhinao (360智脑), from Qihoo 360 (三六零), the Beijing/Shanghai security group formally named 360 Security Technology Inc. (三六零安全科技股份有限公司).

Its headline number is a 360,000-token context (360K tokens). In plain terms, that is roughly half a million Chinese characters — close to a short monograph, a full legal contract stack, or a year of chat logs in a single prompt.

• The long window first appeared with 360Zhinao-7B v1.0 in April 2024, offered at 4K, 32K, and 360K lengths.
• At launch, the team claimed 360K was the longest context among domestic open models of its time.
• The newest generation keeps that ceiling while improving the base.

What you actually get

In April 2025 the lab released 360Zhinao3 (360智脑3), a refresh of the 7B line under the Apache-2.0 license, free for commercial use.

• 360Zhinao3-7B — the base, continued pretraining on 700B high-quality tokens atop the previous 360Zhinao2-7B.
• 360Zhinao3-7B-Instruct — the chat-tuned version.
• 360Zhinao3-7B-O1.5 — a long chain-of-thought (长思维链) model for multi-step reasoning.

The O1.5 variant targets exactly the problems small models usually flunk: competition math and multi-hop science. Third-party model libraries report it at AIME24 54.2 and GPQA Diamond 40, though those are repository-listed figures rather than our own runs.

Two smaller siblings also topped Chinese retrieval leaderboards: 360Zhinao-search and 360Zhinao-1.8B-Reranking ranked first on C-MTEB Retrieval and Reranking in May 2024. For a company whose business is sifting threats from noise, search and ranking are the natural home turf.

Built like a lab rat, not a flagship

360Zhinao is deliberately mid-sized. The training recipe reads like efficient engineering rather than a show of force.

• 360Zhinao2-7B was trained on about 10.1 trillion tokens (10.1T tokens) across two stages, then 360Zhinao3 added 700B more.
• The 7B size means it runs on a single workstation-class GPU, not a data-center pod.
• The O1.5 long-CoT variant is the "think longer" answer to reasoning models, but at a fraction of the cost of frontier systems.

Qihoo 360's AI lineage goes back further: its 360GPT-S2-V8 was, by the company's account, the first domestic model to pass the China Academy of Information and Communications Technology (中国信通院, CAICT) capability appraisal, back in June 2023.

Why a security firm is shipping models

This is the part that surprises outsiders. Zhou Hongyi (周鸿祎), the company's chairman, has pushed 360 to treat large models (大模型) as the next platform, not just a feature.

The logic is defensive. A security company lives on:

• ingesting massive, mixed-format logs and documents;
• retrieving the one relevant signal from noise;
• reasoning about intent over long sequences.

A long-context, open, cheaply runnable model fits that workload better than a closed API with a short window and per-token billing. Open-sourcing also seeds an ecosystem that, in 360's calculus, strengthens the domestic stack it depends on.

The parent is publicly listed, ticker 601360 on the Shanghai exchange. Its late-September 2026 market value was about RMB 61.4 billion (roughly US$8.6 billion / HK$66.9 billion) — a security incumbent, not a model pure-play, bankrolling open research.

Where the long window earns its keep

A 360K context is not a party trick. It is what lets the model treat a whole document set as a single object rather than a chopped-up prompt.

• A security team can drop a quarter of threat logs and ask for the thread running through all of them, not just the last page.
• A legal or compliance user can question an entire contract pack without splitting it into chunks that quietly lose cross-references.
• A developer can paste a large code slice and get answers that respect relationships across files.

That is the wedge a security-first company cares about: reasoning over long, messy, real-world sequences instead of tidy demo prompts. It is also why 360 pairs the long window with retrieval models — 360Zhinao-search and the 1.8B-Reranking model that each hit first place on the C-MTEB leaderboard in May 2024 handle the "find the needle" step, while the 360K context handles the "now read the whole haystack" step.

What readers can do now

• Test the 360K window directly: paste a long report and ask for cross-references near the start and end; most 8K models will silently drop the opening.
• Use the O1.5 variant for reasoning, not the base: the long chain-of-thought model is the one built for math and science chains.
• Check the Apache-2.0 terms before shipping: free commercial use is allowed, but validate on your own data and keep the license headers.

Honest limitations

• The 360K context is officially supported, but very long prompts cost memory and latency; real throughput varies by hardware.
• AIME24 and GPQA scores for O1.5 come from a model-library listing, not an independent benchmark we ran.
• "Longest domestic open context at launch" was a 2024 claim; the field moves fast and others have since pushed further.
• A 7B model, even with a long window, will trail larger systems on breadth and factual density — context is not a substitute for capacity.