DeepSeek V3.1 vs Qwen3.6-35B-A3B
DeepSeek V3.1 (2025) and Qwen3.6-35B-A3B (2026) compare a standalone API model against a coding-specialized model. DeepSeek V3.1 ships a 64k-token context window, while Qwen3.6-35B-A3B ships a 262k-token context window. On MMLU PRO, Qwen3.6-35B-A3B leads by 1.9 pts. On pricing, Qwen3.6-35B-A3B costs $0.15/1M input tokens versus $0.27/1M for the alternative. This page treats the result as workflow and deployment fit, not a universal model winner.
Treat this as a product-type comparison: DeepSeek V3.1 is standalone API model, while Qwen3.6-35B-A3B is coding-specialized model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | DeepSeek V3.1 | Qwen3.6-35B-A3B |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | multimodal apps and provider-routed production | custom coding agents, code generation, and tool loops |
| Decision fit | Coding, Agents, and Vision | Coding, RAG, and Agents |
| Context window | 64k | 262k |
| Cheapest output | $1/1M tokens | $1/1M tokens |
| Provider routes | 8 tracked | 2 tracked |
| Shared benchmarks | 2 rows | MMLU PRO leader |
Decision tradeoffs
- DeepSeek V3.1 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek V3.1 uniquely exposes Structured outputs and Code execution in local model data.
- Local decision data tags DeepSeek V3.1 for Coding, Agents, and Vision.
- Qwen3.6-35B-A3B holds a shared-benchmark lead on MMLU PRO, ahead by 1.9 points.
- Qwen3.6-35B-A3B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.6-35B-A3B uniquely exposes Function calling and Tool use in local model data.
- Local decision data tags Qwen3.6-35B-A3B 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
Qwen3.6-35B-A3B
$370
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $96.00. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on Novita AI; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for Structured outputs and Code execution before moving production traffic.
- Qwen3.6-35B-A3B adds Function calling and Tool use in local capability data.
- Provider overlap exists on Novita AI; start route-level A/B tests there.
- Cheapest tracked output pricing is tied, so migration risk shifts to quality, latency, and provider packaging.
- Check replacement coverage for Function calling and Tool use before moving production traffic.
- DeepSeek V3.1 adds Structured outputs and Code execution in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-08-21 | 2026-04-16 |
| Context window | 64k | 262k |
| Parameters | 671B total, 37B active (MoE) | 35B |
| 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 | Qwen3.6-35B-A3B |
|---|---|---|
| Input price | $0.27/1M tokens | $0.15/1M tokens |
| Output price | $1/1M tokens | $1/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3.1 | Qwen3.6-35B-A3B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | No | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | No |
| Code execution | Yes | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | DeepSeek V3.1 | Qwen3.6-35B-A3B |
|---|---|---|
| MMLU PRO | 83.3 | 85.2 |
| SWE-bench Verified | 66.0 | 73.4 |
Deep dive
On shared benchmark coverage, MMLU PRO has DeepSeek V3.1 at 83.3 and Qwen3.6-35B-A3B at 85.2, with Qwen3.6-35B-A3B ahead by 1.9 points; SWE-bench Verified has DeepSeek V3.1 at 66 and Qwen3.6-35B-A3B at 73.4, with Qwen3.6-35B-A3B ahead by 7.4 points. The largest visible gap is 7.4 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 function calling: Qwen3.6-35B-A3B, tool use: Qwen3.6-35B-A3B, structured outputs: DeepSeek V3.1, and code execution: DeepSeek V3.1. Both models share vision and multimodal input, 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 Qwen3.6-35B-A3B lists $0.15/1M input and $1/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.6-35B-A3B lower by about $0.08 per million blended tokens. Availability is 8 providers versus 2, so concentration risk also matters.
Choose DeepSeek V3.1 when coding workflow support and broader provider choice are central to the workload. Choose Qwen3.6-35B-A3B when coding workflow support, larger context windows, and lower input-token cost 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 Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B 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 Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B is cheaper on tracked token pricing. DeepSeek V3.1 costs $0.27/1M input and $1/1M output tokens. Qwen3.6-35B-A3B costs $0.15/1M input and $1/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.1 or Qwen3.6-35B-A3B open source?
DeepSeek V3.1 is listed under MIT. Qwen3.6-35B-A3B 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 Qwen3.6-35B-A3B?
Both DeepSeek V3.1 and Qwen3.6-35B-A3B 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 Qwen3.6-35B-A3B?
Both DeepSeek V3.1 and Qwen3.6-35B-A3B 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 Qwen3.6-35B-A3B?
DeepSeek V3.1 is available on Microsoft Foundry, Fireworks AI, NVIDIA NIM, Together AI, and AWS Bedrock. Qwen3.6-35B-A3B is available on OpenRouter 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.