Qwen3.5 397B on Genosai — multilingual text and code, no subscription

Qwen3.5 397B is a large text MoE model from the Qwen team, available on Genosai with a 230,000-token context. Of its 397 billion parameters, only about 17 billion are active per token, so it stays fast while matching top-tier systems. It excels at multilingual work and code, supports 201 languages, and runs online with no separate account, API keys, or setup.

Updated: July 7, 2026

Qwen3.5 397B

Contents

What is Qwen3.5 397B

Qwen3.5 397B is a large text model from the Qwen team, released in February 2026. It continues the Qwen3.x line and sits at the top of it: the model you reach for when you need strong multilingual output, clean code, and work over a long context. On Genosai it is available online, so you do not need a separate account, API keys, or your own infrastructure.

Its defining feature is the Mixture-of-Experts architecture. The model holds 397 billion parameters and 512 experts in total, but only a small share fires per token — about 17 billion parameters and roughly ten experts. That gives you the quality of a very large model at the speed and cost of a much smaller one. This is exactly what the A17B suffix in the name stands for: "active 17B".

What the MoE design means in practice. The model does not push every request through all 397 billion parameters — a router picks the experts best suited to each token and uses only them. For you that means two things: answers come back faster than from a dense model of the same size, and the cost per request stays low at 2 credits. Yet the breadth of a large model is not lost: knowledge is spread across all the experts, and the right ones activate for the task.

The second reason people choose this model is language. Qwen3.5 397B supports 201 languages and dialects and switches especially confidently between Russian, English, and Chinese. For translation, localization, and multilingual content that clearly simplifies the job: one model covers both the source and the target language without needing separate tools per direction. It stays strong in code too — 76.4 on SWE-bench Verified and 83.6 on LiveCodeBench v6 — and on exam-style benchmarks such as AIME26 for math it reaches 91.3.

In terms of lineage, Qwen3.5 397B is a step up from earlier Qwen3 versions: more experts, a longer working context, and noticeably higher scores on coding and reasoning tasks. On Genosai you get this model with no barrier to entry — no local hosting, no weights to manage, no idle server to pay for.

Capabilities

Qwen3.5 397B covers most of the text scenarios a marketer, writer, translator, or developer runs into. Here is what the model does well in practice.

Multilingual text and translation

The model writes and translates text across dozens of languages while keeping meaning, tone, and terminology intact. It reads the context of a translation well: a marketing slogan sounds natural rather than literal, and a business letter stays a business letter. That is handy for localizing sites, product descriptions, and newsletters when the same text is needed in several languages at once.

Code and data

Qwen3.5 397B writes and reviews code, finds bugs, suggests refactoring, and explains unfamiliar snippets. It helps with SQL queries, regular expressions, and data structures, and it walks through each step in its answer. Strong scores on coding benchmarks confirm the model is fit not only for drafts but for real working tasks.

Reasoning and analysis

The model breaks complex questions into steps, compares options, and reaches conclusions on concrete numbers. A 230,000-token context lets you load a large document whole and ask questions about it without splitting it into pieces. That is useful for analysis, long-text review, and preparing structured answers.

Content and structure

Qwen3.5 397B keeps the structure you ask for: request a five-point list with subheadings and you get exactly five tidy blocks. It offers several variants of the same text for different goals and formats its answer neatly in Markdown — with headings, lists, and code blocks. That is handy when the result goes straight to a document or a publication: less manual format cleanup, more time on the substance. When needed, the model reshapes the same text for another channel — from an article to a post, from a post to an email.

Examples prompts and responses

Below are real Qwen3.5 397B responses produced on Genosai. Prompts and text are shown unchanged.

Prompt: Translate this slogan into Russian and Chinese: 'Freshness in every cup.'

Qwen3.5 397B response:
**Russian:**
Свежесть в каждой чашке.

**Chinese:**
每一杯都新鲜。

_Generated on Genosai.io with Qwen3.5 397B._

The second example shows the model writing working SQL and explaining every line of the query.

Prompt: Write an SQL query: select the top-5 products by total sales from table orders(product, amount).

Qwen3.5 397B response:
Here is the SQL query to select the top 5 products by total sales:

SELECT product, SUM(amount) AS total_sales
FROM orders
GROUP BY product
ORDER BY total_sales DESC
LIMIT 5;

Note: This syntax works for MySQL, PostgreSQL, and SQLite. If you are using SQL Server, replace the last line with SELECT TOP 5 ... If you are using Oracle, use FETCH FIRST 5 ROWS ONLY at the end.

_Generated on Genosai.io with Qwen3.5 397B._

How to use on Genosai

  1. Sign in to your Genosai account and open the text studio.
  2. In the model list, pick Qwen3.5 397B in the Qwen section.
  3. State the output language and format in your prompt — this matters most for translation and localization.
  4. Enter your task: the more specific the input, the more precise the answer.
  5. Send the request and, if needed, ask for edits — shorter, a different tone, an added example.
  6. Copy the finished text or keep the conversation going, building on previous messages.

Prompts

Translate the text below into English and Chinese, keep the marketing tone: [paste text]
You are an editor. Localize this product description for the [country] market, adapt examples and units: [paste text]
Write an SQL query for table [table and fields] that [task], and explain each line.
Review this code snippet, find the bugs, and propose a fix with comments: [paste code]
Draft an outline for an article about [topic]: 6 sections with subheadings and a short thesis for each.
Summarize the long text below: key points, numbers, and a conclusion: [paste text]
Compare two approaches [A] and [B] point by point and give a recommendation with reasoning.

Generation cost

On Genosai Qwen3.5 397B is billed by tokens — you pay for the actual size of the prompt and the answer, so a short question is cheaper than a long analysis. A typical request costs roughly 2 credits. The final amount depends on prompt length and answer size.

Starter credits after sign-up let you try Qwen3.5 397B for free, and top-ups work with local cards without a VPN. See current rates and balance on the Pricing page.

How it compares

Qwen3.5 397B is a strong multilingual all-rounder that leans into code. If you want a comparable model from another developer with a long context, look at DeepSeek V3.2. For agentic and creative tasks there is MiniMax M2.7, for a balance of accuracy and reasoning GPT-5.4, and when speed matters most Gemini 3 Flash.

ModelContext on GenosaiCost per requestStrength
Qwen3.5 397B230,0002 creditsMultilingual work and code
DeepSeek V3.2see the model page—Reasoning and long context
MiniMax M2.7125,0000.5 creditsAgentic and creative tasks
GPT-5.4380,0003 creditsBalance of accuracy and reasoning

All models live in a single Genosai interface, so you can compare them on your own task and pick the best fit for price and quality.

Limitations and tips

Qwen3.5 397B is strong but not all-powerful. On Genosai it works with text only — there is no image or file processing here, so pick another catalog model for picture tasks. There is no built-in web search either: if you need fresh data from the web, add it to your prompt yourself. Niche facts and figures are worth double-checking against the original source.

To get the best result, phrase the task concretely: for translation, name the target language and tone; for code, the language, database, and output format. The model handles a long context well, so do not hesitate to feed it large inputs whole — paste the document or thread as one block rather than paraphrasing it. If an answer comes out generic, ask it to rewrite with specifics and examples; if the format drifts, restate the structure you need in your next message. For translation into rarer languages, adding a style sample or a couple of terms helps the model keep the right wording.

Finally, keep price in mind. Qwen3.5 397B costs 2 credits per request, which is reasonable for complex multilingual and coding tasks. For high-volume, simple generations you can switch to the cheaper MiniMax M2.7 to save credits, and heavy analytical prompts can be cross-checked against GPT-5.4 if you like.

FAQ

What is Qwen3.5 397B and who built it?

Qwen3.5 397B is a large text model from the Qwen team, released in February 2026. It uses a Mixture-of-Experts design with 397 billion parameters, of which about 17 billion are active per token. On Genosai it is available online with no separate subscription.

What context length does Qwen3.5 397B support on Genosai?

On Genosai the model works with a 230,000-token context. That is enough for long documents, large briefs, and extended conversations. The whole text fits in one window without splitting it into chunks.

Which languages does Qwen3.5 397B handle?

The model supports 201 languages and dialects, including Russian, English, and Chinese. It translates confidently between them and generates text while keeping meaning and tone. Multilingual work is one of its strongest sides.

How much does a Qwen3.5 397B request cost?

A single Qwen3.5 397B request on Genosai costs 2 credits. You pay for actual generations with no monthly subscription. Credits can be topped up whenever you need them.

Can Qwen3.5 397B work with images on Genosai?

No. On Genosai, Qwen3.5 397B is available as a text model and processes text only. For image tasks, pick a model with file support from the catalog.

Is Qwen3.5 397B good for code?

Yes. It scores 76.4 on SWE-bench Verified and 83.6 on LiveCodeBench v6. It writes functions, debugs errors, helps with SQL queries, and explains unfamiliar code snippets.

Try Qwen3.5 397B on Genosai