Qwen3.5-9B vs Qwen3.6-27B
Qwen3.5-9B (2026) and Qwen3.6-27B (2026) compare a standalone API model against a coding-specialized model. Qwen3.5-9B ships a 262k-token context window, while Qwen3.6-27B ships a 262k-token context window. On MMLU PRO, Qwen3.6-27B leads by 3.7 pts. On pricing, Qwen3.5-9B costs $0.10/1M input tokens versus $0.32/1M 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: Qwen3.5-9B is standalone API model, while Qwen3.6-27B is coding-specialized model. Choose based on workflow fit before reading any benchmark or price row as decisive.
Decision scorecard
Local evidence first| Signal | Qwen3.5-9B | Qwen3.6-27B |
|---|---|---|
| Product type | Standalone API model | Coding-specialized model |
| Best for | multimodal apps, tool-calling agents, and provider-routed production | custom coding agents, code generation, and tool loops |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 262k | 262k |
| Cheapest output | $0.15/1M tokens | $3.20/1M tokens |
| Provider routes | 3 tracked | 4 tracked |
| Shared benchmarks | 3 shared | MMLU PRO leader |
Decision tradeoffs
- Qwen3.5-9B has the lower cheapest tracked output price at $0.15/1M tokens.
- Qwen3.5-9B uniquely exposes Structured outputs in local model data.
- Local decision data tags Qwen3.5-9B for Coding, RAG, and Agents.
- Qwen3.6-27B holds a shared-benchmark lead on MMLU PRO, ahead by 3.7 points.
- Qwen3.6-27B has broader tracked provider coverage for fallback and procurement flexibility.
- Qwen3.6-27B uniquely exposes Reasoning in local model data.
- Local decision data tags Qwen3.6-27B 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.
Qwen3.5-9B
$118
Cheapest tracked route/tier: Together AI
Qwen3.6-27B
$1,056
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $939. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Alibaba Cloud PAI-EAS; start route-level A/B tests there.
- Qwen3.6-27B is $3.05/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Structured outputs before moving production traffic.
- Qwen3.6-27B adds Reasoning in local capability data.
- Provider overlap exists on OpenRouter and Alibaba Cloud PAI-EAS; start route-level A/B tests there.
- Qwen3.5-9B is $3.05/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Reasoning before moving production traffic.
- Qwen3.5-9B adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-02 | 2026-04-27 |
| Context window | 262k | 262k |
| Parameters | 9B | 27B |
| Architecture | Decoder Only | Decoder Only |
| License | Apache 2.0OSI-approved | Apache 2.0OSI-approved |
| Openness | Open source | Open source |
| Weights | Unknown | Available |
| Code | Unknown | Unknown |
| Commercial use | Commercial use: permitted | Commercial use: permitted |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Qwen3.5-9B | Qwen3.6-27B |
|---|---|---|
| Input price | $0.10/1M tokens | $0.32/1M tokens |
| Output price | $0.15/1M tokens | $3.20/1M tokens |
| Providers |
Capabilities
| Capability | Qwen3.5-9B | Qwen3.6-27B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | Yes |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Qwen3.5-9B | Qwen3.6-27B |
|---|---|---|
| MMLU PRO | 82.5 | 86.2 |
| Google-Proof Q&A | 81.7 | 87.8 |
| LiveCodeBench | 65.6 | 83.9 |
Deep dive
On shared benchmark coverage, MMLU PRO has Qwen3.5-9B at 82.5 and Qwen3.6-27B at 86.2, with Qwen3.6-27B ahead by 3.7 points; Google-Proof Q&A has Qwen3.5-9B at 81.7 and Qwen3.6-27B at 87.8, with Qwen3.6-27B ahead by 6.1 points; LiveCodeBench has Qwen3.5-9B at 65.6 and Qwen3.6-27B at 83.9, with Qwen3.6-27B ahead by 18.3 points. The largest visible gap is 18.3 points on LiveCodeBench, 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: Qwen3.6-27B and structured outputs: Qwen3.5-9B. Both models share vision, multimodal input, function calling, and tool use, 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, Qwen3.5-9B lists $0.10/1M input and $0.15/1M output tokens on the cheapest tracked provider, while Qwen3.6-27B lists $0.32/1M input and $3.20/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-9B lower by about $1.07 per million blended tokens. Availability is 3 providers versus 4, so concentration risk also matters.
Choose Qwen3.5-9B when vision-heavy evaluation and lower input-token cost are central to the workload. Choose Qwen3.6-27B when coding workflow support and broader provider choice 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, Qwen3.5-9B or Qwen3.6-27B?
Qwen3.5-9B supports 262k tokens, while Qwen3.6-27B supports 262k 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, Qwen3.5-9B or Qwen3.6-27B?
Qwen3.5-9B is cheaper on tracked token pricing. Qwen3.5-9B costs $0.10/1M input and $0.15/1M output tokens. Qwen3.6-27B costs $0.32/1M input and $3.20/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Qwen3.5-9B or Qwen3.6-27B open source?
Qwen3.5-9B is listed under Apache 2.0. Qwen3.6-27B 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, Qwen3.5-9B or Qwen3.6-27B?
Both Qwen3.5-9B and Qwen3.6-27B 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, Qwen3.5-9B or Qwen3.6-27B?
Both Qwen3.5-9B and Qwen3.6-27B 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 Qwen3.5-9B and Qwen3.6-27B?
Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Qwen3.6-27B is available on OpenRouter, Alibaba Cloud PAI-EAS, Vercel AI Gateway, and Novita AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-06-29. Data sourced from public model cards and provider documentation.