Qwen3.5 397B A17B

◆ Strong

by Qwen Intelligent Open Weight Multimodal Rank #81 of 104

7.8
/ 10

The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...

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Specifications

Specifications for Qwen3.5 397B A17B
AttributeValue
Lab Qwen
Tags Intelligent Open Weight Multimodal
Overall Score 7.8/10
Release Date 2026-02
Context Window 262,144 tokens
Input Price / 1M $0.39
Output Price / 1M $2.34
Input Modalities Text, Image, Video
Output Modalities Text

Strengths

Weaknesses

Best For

In Depth: Qwen3.5 397B A17B

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Benchmark Performance

[Lead paragraph — ~60 words. Anchor Qwen3.5 397B A17B's overall score of 7.8/10 against the headline benchmarks it actually competes on (MMLU, HumanEval, MATH, GSM8K, LMSYS Arena). Name the closest peers above and below it on this leaderboard so the reader instantly understands the tier.]

[Detail paragraph — ~80 words. Walk through 2–3 specific benchmark numbers with citations: e.g. "scores X on MMLU vs. Y for the next-best model in its class," "Arena ELO of Z places it between A and B." Mention where the model over- or under-performs its overall rank — a 9.0 model that's a 9.5 on coding but a 8.2 on math is the kind of nuance that wins long-tail queries like "Qwen3.5 397B A17B coding benchmark" or "Qwen3.5 397B A17B vs [peer]".]

Pricing & Value

[Lead paragraph — ~50 words. State the input/output prices in plain English ("$0.39 in, $2.34 out per million tokens") and convert to something tangible — cost of a 10k-token analysis, cost of a 1M-token agentic run, cost vs. the cheapest model on the leaderboard.]

[Detail paragraph — ~90 words. Compare Qwen3.5 397B A17B's price-per-point-of-score against 2–3 named peers. Call out whether you're paying for raw intelligence, long context (262,144 tokens here), low latency, or brand premium. Flag any tier discounts, batch pricing, or caching that materially change the effective cost. If this model is overpriced for its score, say so plainly — that honesty is what ranks.]

Who Should Use This

[Lead paragraph — ~50 words. One sentence per persona: the developer building X, the analyst doing Y, the team that already runs Z. Each should resolve to a concrete decision: "pick Qwen3.5 397B A17B if…" and "skip it if…".]

  • [Persona 1 — e.g. "Backend engineers wiring up production agents." One sentence on why this model fits, one on the tradeoff they accept.]
  • [Persona 2 — e.g. "Solo founders prototyping fast." Same structure: why it fits, what they give up.]
  • [Persona 3 — e.g. "Enterprise teams that need a long-context workhorse." Why it fits, the constraint.]
  • [Anti-persona — "Skip Qwen3.5 397B A17B if you're optimizing for X or Y." Be specific; this is the most-cited line in a review.]

Release & Version History

[Lead paragraph — ~50 words. Anchor the 2026-02 release in context: what it replaced inside Qwen's lineup, what the lab claimed it improved, and how those claims held up against independent benchmarks in the weeks after launch.]

[Detail paragraph — ~90 words. Walk the version trail: previous generation, this model, any planned successor or sibling (mini/flash/opus tier). Note pricing or context-window changes vs. the predecessor. Mention deprecation timelines if the lab has announced any — readers searching "Qwen3.5 397B A17B deprecated" or "Qwen3.5 397B A17B successor" land directly here. Close with the editorial verdict: is this the version to standardize on for the next 6 months, or a stopgap?]

Sources & Further Reading

Related Models

Scores are aggregated from public benchmarks (MMLU, HumanEval, MATH, GSM8K, LMSYS) and normalized to a 1–10 scale. Methodology →