DeepSeek V3.1 vs Qwen3.5-397B-A17B
DeepSeek V3.1 (2025) and Qwen3.5-397B-A17B (2026) are frontier reasoning models from DeepSeek and Alibaba. DeepSeek V3.1 ships a 64k-token context window, while Qwen3.5-397B-A17B ships a 262k-token context window. On MMLU PRO, Qwen3.5-397B-A17B leads by 4.5 pts. On pricing, DeepSeek V3.1 costs $0.27/1M input tokens versus $0.39/1M for the alternative. This comparison covers specs, pricing, API access, capabilities, benchmarks, input and output token costs, and production fit for coding and agent workloads.
DeepSeek V3.1 is ~44% cheaper at $0.27/1M; pay for Qwen3.5-397B-A17B only for reasoning depth.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.1 | Qwen3.5-397B-A17B |
|---|---|---|
| Best for | multimodal apps and provider-routed production | reasoning-heavy apps, multimodal apps, and tool-calling agents |
| Decision fit | Coding, Agents, and Vision | Coding, RAG, and Agents |
| Context window | 64k | 262k |
| Cheapest output | $1/1M tokens | $2.34/1M tokens |
| Provider routes | 8 tracked | 4 tracked |
| Shared benchmarks | 2 rows | MMLU PRO leader |
Decision tradeoffs
- DeepSeek V3.1 has the lower cheapest tracked output price at $1/1M tokens.
- DeepSeek V3.1 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek V3.1 uniquely exposes Code execution in local model data.
- Local decision data tags DeepSeek V3.1 for Coding, Agents, and Vision.
- Qwen3.5-397B-A17B holds a shared-benchmark lead on MMLU PRO, ahead by 4.5 points.
- Qwen3.5-397B-A17B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.5-397B-A17B uniquely exposes Reasoning, Function calling, and Tool use in local model data.
- Local decision data tags Qwen3.5-397B-A17B for Coding, RAG, and Agents.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output route or tier on this page.
DeepSeek V3.1
$466
Cheapest tracked route/tier: Novita AI
Qwen3.5-397B-A17B
$897
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $431. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Together AI and Novita AI; start route-level A/B tests there.
- Qwen3.5-397B-A17B is $1.34/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Code execution before moving production traffic.
- Qwen3.5-397B-A17B adds Reasoning, Function calling, and Tool use in local capability data.
- Provider overlap exists on Together AI and Novita AI; start route-level A/B tests there.
- DeepSeek V3.1 is $1.34/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning, Function calling, and Tool use before moving production traffic.
- DeepSeek V3.1 adds Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-21 | 2026-02-16 |
| Context window | 64k | 262k |
| Parameters | 671B total, 37B active (MoE) | 397B |
| Architecture | mixture of experts | MoE |
| License | MIT(OSI) | Apache 2.0(OSI) |
| Openness | Open source | Open source |
| Commercial use | Commercial use allowed | Commercial use allowed |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | DeepSeek V3.1 | Qwen3.5-397B-A17B |
|---|---|---|
| Input price | $0.27/1M tokens | $0.39/1M tokens |
| Output price | $1/1M tokens | $2.34/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3.1 | Qwen3.5-397B-A17B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | Yes |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V3.1 | Qwen3.5-397B-A17B |
|---|---|---|
| MMLU PRO | 83.3 | 87.8 |
| SWE-bench Verified | 66.0 | 76.2 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V3.1 at 83.3 and Qwen3.5-397B-A17B at 87.8, with Qwen3.5-397B-A17B ahead by 4.5 points; SWE-bench Verified has DeepSeek V3.1 at 66 and Qwen3.5-397B-A17B at 76.2, with Qwen3.5-397B-A17B ahead by 10.2 points. The largest visible gap is 10.2 points on SWE-bench Verified, 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 reasoning mode: Qwen3.5-397B-A17B, function calling: Qwen3.5-397B-A17B, tool use: Qwen3.5-397B-A17B, and code execution: DeepSeek V3.1. Both models share vision, multimodal input, and 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 V3.1 lists $0.27/1M input and $1/1M output tokens on the cheapest tracked provider, 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 V3.1 lower by about $0.49 per million blended tokens. Availability is 8 providers versus 4, so concentration risk also matters.
Choose DeepSeek V3.1 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3.5-397B-A17B when reasoning depth 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 V3.1 or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B supports 262k tokens, while DeepSeek V3.1 supports 64k 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 V3.1 or Qwen3.5-397B-A17B?
DeepSeek V3.1 is cheaper on tracked token pricing. DeepSeek V3.1 costs $0.27/1M input and $1/1M 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 V3.1 or Qwen3.5-397B-A17B open source?
DeepSeek V3.1 is listed under MIT. 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 vision, DeepSeek V3.1 or Qwen3.5-397B-A17B?
Both DeepSeek V3.1 and Qwen3.5-397B-A17B expose vision. 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.
Which is better for multimodal input, DeepSeek V3.1 or Qwen3.5-397B-A17B?
Both DeepSeek V3.1 and Qwen3.5-397B-A17B expose multimodal input. 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 V3.1 and Qwen3.5-397B-A17B?
DeepSeek V3.1 is available on Microsoft Foundry, Fireworks AI, NVIDIA NIM, Together AI, and AWS Bedrock. Qwen3.5-397B-A17B is available on OpenRouter, Together AI, Alibaba Cloud PAI-EAS, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-06-04. Data sourced from public model cards and provider documentation.