DeepSeek V3.2 vs Qwen3.5-397B-A17B
DeepSeek V3.2 (2025) and Qwen3.5-397B-A17B (2026) are general-purpose language models from DeepSeek and Alibaba. DeepSeek V3.2 ships a 160K-token context window, while Qwen3.5-397B-A17B ships a 262K-token context window. On Google-Proof Q&A, Qwen3.5-397B-A17B leads by 5.3 pts. On pricing, DeepSeek V3.2 costs $0.26/1M input tokens versus $0.39/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
DeepSeek V3.2 is ~51% cheaper at $0.26/1M; pay for Qwen3.5-397B-A17B only for long-context analysis.
Specs
| Released | 2025-01-01 | 2026-02-16 |
| Context window | 160K | 262K |
| Parameters | 671B | 397B |
| Architecture | decoder only | MoE |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| DeepSeek V3.2 | Qwen3.5-397B-A17B | |
|---|---|---|
| Input price | $0.26/1M tokens | $0.39/1M tokens |
| Output price | $0.42/1M tokens | $2.34/1M tokens |
| Providers |
Capabilities
| DeepSeek V3.2 | Qwen3.5-397B-A17B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | DeepSeek V3.2 | Qwen3.5-397B-A17B |
|---|---|---|
| Google-Proof Q&A | 84.0 | 89.3 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has DeepSeek V3.2 at 84 and Qwen3.5-397B-A17B at 89.3, with Qwen3.5-397B-A17B ahead by 5.3 points. The largest visible gap is 5.3 points on Google-Proof Q&A, which matters most when that benchmark mirrors your workload. Treat isolated benchmark wins as directional, because provider routing, prompt style, and tool access can move real application results.
The capability footprint differs most on multimodal input: Qwen3.5-397B-A17B and code execution: DeepSeek V3.2. Both models share structured outputs, so the practical split is not just feature count. Use those differences to decide whether the page is about raw model quality, agentic coding support, multimodal ingestion, or predictable structured API behavior.
For cost, DeepSeek V3.2 lists $0.26/1M input and $0.42/1M output tokens, while Qwen3.5-397B-A17B lists $0.39/1M input and $2.34/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek V3.2 lower by about $0.67 per million blended tokens. Availability is 4 providers versus 1, so concentration risk also matters.
Choose DeepSeek V3.2 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3.5-397B-A17B when long-context analysis and larger context windows are more important. For production, rerun your own prompts through the exact provider, region, and tool stack you plan to ship.
FAQ
Which has a larger context window, DeepSeek V3.2 or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B supports 262K tokens, while DeepSeek V3.2 supports 160K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Which is cheaper, DeepSeek V3.2 or Qwen3.5-397B-A17B?
DeepSeek V3.2 is cheaper on tracked token pricing. DeepSeek V3.2 costs $0.26/1M input and $0.42/1M output tokens. Qwen3.5-397B-A17B costs $0.39/1M input and $2.34/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.2 or Qwen3.5-397B-A17B open source?
DeepSeek V3.2 is listed under Open Source. Qwen3.5-397B-A17B is listed under Apache 2.0. License labels affect whether you can self-host, redistribute weights, or rely only on hosted APIs, so confirm the upstream license before deployment.
Which is better for multimodal input, DeepSeek V3.2 or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B has the clearer documented multimodal input signal in this comparison. If multimodal input is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for structured outputs, DeepSeek V3.2 or Qwen3.5-397B-A17B?
Both DeepSeek V3.2 and Qwen3.5-397B-A17B expose structured outputs. The better choice depends on benchmark fit, context budget, pricing, and whether your provider route exposes the same capability surface. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Where can I run DeepSeek V3.2 and Qwen3.5-397B-A17B?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. Qwen3.5-397B-A17B is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-24. Data sourced from public model cards and provider documentation.