Compare the listed model data for Qwen3.5 397B-A17B (MoE) by Qwen and DeepSeek-V3.1 by DeepSeek. Benchmark and pricing records are not a controlled test and may change.
Data last updated: 2026-10-16. Verify current model details with each provider.
What the listed data says
Qwen3.5 397B-A17B (MoE): 128K tokens; DeepSeek-V3.1: 128K tokens. The listed context-window values are equal. Qwen3.5 397B-A17B (MoE) is listed for no specific capability flags; DeepSeek-V3.1 is listed for no specific capability flags. Shared catalog benchmark entries: MMLU: Qwen3.5 397B-A17B (MoE) 94.1%; DeepSeek-V3.1 91.7%; AIME 2025: Qwen3.5 397B-A17B (MoE) 88.2%; DeepSeek-V3.1 75.3%; LiveCodeBench: Qwen3.5 397B-A17B (MoE) 73.5%; DeepSeek-V3.1 68.2%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
Catalog source links
Use these model and provider links to verify current specifications, pricing, and availability.
The first listed parameter quantities are 397B total (17B active) and 671B total (37B active via MoE); the larger quantity is 1.69× the smaller. Total and active MoE parameters are not directly equivalent.
A release-date gap cannot be calculated from the catalog values (Qwen3.5 397B-A17B (MoE): 2025; DeepSeek-V3.1: June 2025).
Cost example: 1M input and 1M output tokens
A cost example for 1M input plus 1M output tokens cannot be calculated reliably because separate numeric input and output rates are not listed for both models.
Choose Qwen3.5 397B-A17B (MoE) if…
Choose Qwen3.5 397B-A17B (MoE) when your task aligns with catalog-listed uses or strengths such as Frontier-level AI applications with efficiency constraints, Production deployments of Qwen3.5 quality at scale, Qwen3.5 flagship model — 397B knowledge at 17B inference cost, Near Qwen3 235B quality at more efficient inference.
Choose DeepSeek-V3.1 if…
Choose DeepSeek-V3.1 when your task aligns with catalog-listed uses or strengths such as Enterprise software development requiring high quality at low latency, Document intelligence and knowledge base applications, API providers serving general business customers, DeepSeek's most capable non-reasoning model as of June 2025.
Feature Comparison — Qwen3.5 397B-A17B (MoE) vs DeepSeek-V3.1
Catalog specifications for Qwen3.5 397B-A17B (MoE) and DeepSeek-V3.1
Specification
Qwen3.5 397B-A17B (MoE)
DeepSeek-V3.1
Provider
Qwen
DeepSeek
Parameters
397B total (17B active)
671B total (37B active via MoE)
Context window
128K tokens
128K tokens
Maximum output
Not listed
Not listed
Catalog pricing
Apache 2.0 — free
MIT open source. DeepSeek API: ~$0.27/MTok input, ~$1.10/MTok output
Listed input/output cost per 1M tokens
input $0.8, output $1.5 per 1M tokens
Separate numeric input/output rates not listed
License
Apache 2.0
MIT
Coding support
Not listed
Not listed
Reasoning support
Not listed
Not listed
Vision input
Not listed
Not listed
Multimodal
Not listed
Not listed
Open weights
Yes
Yes
Benchmark: MMLU
94.1%
91.7%
Benchmark: AIME 2025
88.2%
75.3%
Benchmark: LiveCodeBench
73.5%
68.2%
Figures are transcribed from LITHOVEX model records. Benchmark versions and evaluation setups may differ; pricing and specifications can change. Check each provider's current documentation before making a decision.
Frequently Asked Questions
What is different between Qwen3.5 397B-A17B (MoE) and DeepSeek-V3.1?
Qwen3.5 397B-A17B (MoE): 128K tokens; DeepSeek-V3.1: 128K tokens. The listed context-window values are equal. Qwen3.5 397B-A17B (MoE) is listed for no specific capability flags; DeepSeek-V3.1 is listed for no specific capability flags. Shared catalog benchmark entries: MMLU: Qwen3.5 397B-A17B (MoE) 94.1%; DeepSeek-V3.1 91.7%; AIME 2025: Qwen3.5 397B-A17B (MoE) 88.2%; DeepSeek-V3.1 75.3%; LiveCodeBench: Qwen3.5 397B-A17B (MoE) 73.5%; DeepSeek-V3.1 68.2%. These records are reference data, not a controlled head-to-head test; verify current pricing and specifications with each provider.
What benchmark values are listed for Qwen3.5 397B-A17B (MoE) and DeepSeek-V3.1?
MMLU: Qwen3.5 397B-A17B (MoE) 94.1%; DeepSeek-V3.1 91.7%; AIME 2025: Qwen3.5 397B-A17B (MoE) 88.2%; DeepSeek-V3.1 75.3%; LiveCodeBench: Qwen3.5 397B-A17B (MoE) 73.5%; DeepSeek-V3.1 68.2%. The catalog values may use different versions and evaluation setups, so they are reference points rather than a controlled head-to-head result.
Which model has the larger listed context window?
Qwen3.5 397B-A17B (MoE): 128K tokens; DeepSeek-V3.1: 128K tokens. The listed context-window values are equal.
What are the listed costs per million tokens for Qwen3.5 397B-A17B (MoE) and DeepSeek-V3.1?
Qwen3.5 397B-A17B (MoE): input $0.8, output $1.5 per 1M tokens (Apache 2.0 — free). DeepSeek-V3.1: Separate numeric input/output rates not listed (MIT open source. DeepSeek API: ~$0.27/MTok input, ~$1.10/MTok output). Where separate numeric input and output rates are available, the worked example uses 1M input plus 1M output tokens. Confirm current terms with each provider.
How far apart are the listed release dates for Qwen3.5 397B-A17B (MoE) and DeepSeek-V3.1?
The catalog lists Qwen3.5 397B-A17B (MoE) as 2025 and DeepSeek-V3.1 as June 2025; a release-date gap cannot be calculated from both records.
When was the comparison data last updated?
The model catalog records 2026-10-16 as its data update date. Provider specifications and prices may have changed since then.
How should I choose between Qwen3.5 397B-A17B (MoE) and DeepSeek-V3.1?
Compare the listed specifications and capabilities, review the benchmark notes, then test both with the same representative prompt for your task. This page does not claim a universal winner.
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