Kimi K2.6 vs Qwen3.5-9B
Kimi K2.6 (2026) and Qwen3.5-9B (2026) are agentic coding models from Moonshot AI and Alibaba. Kimi K2.6 ships a 262K-token context window, while Qwen3.5-9B ships a 262K-token context window. On MMLU PRO, Kimi K2.6 leads by 2.1 pts. On pricing, Qwen3.5-9B costs $0.1/1M input tokens versus $0.75/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Qwen3.5-9B is ~650% cheaper at $0.1/1M; pay for Kimi K2.6 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | Kimi K2.6 | Qwen3.5-9B |
|---|---|---|
| Decision fit | Coding, RAG, and Agents | RAG, Agents, and Long context |
| Context window | 262K | 262K |
| Cheapest output | $3.5/1M tokens | $0.15/1M tokens |
| Provider routes | 5 tracked | 3 tracked |
| Shared benchmarks | MMLU PRO leader | 2 rows |
Decision tradeoffs
- Kimi K2.6 leads the largest shared benchmark signal on MMLU PRO by 2.1 points.
- Kimi K2.6 has broader tracked provider coverage for fallback and procurement flexibility.
- Kimi K2.6 uniquely exposes Reasoning in local model data.
- Local decision data tags Kimi K2.6 for Coding, RAG, and Agents.
- 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 RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
Kimi K2.6
$1,475
Cheapest tracked route: OpenRouter
Qwen3.5-9B
$118
Cheapest tracked route: Together AI
Estimated monthly gap: $1,358. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Qwen3.5-9B is $3.35/1M 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.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Kimi K2.6 is $3.35/1M 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.
- Kimi K2.6 adds Reasoning in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-04-20 | 2026-03-02 |
| Context window | 262K | 262K |
| Parameters | 1T | 9B |
| Architecture | Mixture of Experts (MoE) | decoder only |
| License | Open Source | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Kimi K2.6 | Qwen3.5-9B |
|---|---|---|
| Input price | $0.75/1M tokens | $0.1/1M tokens |
| Output price | $3.5/1M tokens | $0.15/1M tokens |
| Providers |
Capabilities
| Capability | Kimi K2.6 | Qwen3.5-9B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | Yes | No |
| Function calling | Yes | Yes |
| Tool use | Yes | Yes |
| Structured outputs | No | Yes |
| Code execution | No | No |
Benchmarks
| Benchmark | Kimi K2.6 | Qwen3.5-9B |
|---|---|---|
| MMLU PRO | 84.6 | 82.5 |
| Google-Proof Q&A | 90.5 | 81.7 |
Deep dive
On shared benchmark coverage, MMLU PRO has Kimi K2.6 at 84.6 and Qwen3.5-9B at 82.5, with Kimi K2.6 ahead by 2.1 points; Google-Proof Q&A has Kimi K2.6 at 90.5 and Qwen3.5-9B at 81.7, with Kimi K2.6 ahead by 8.8 points. The largest visible gap is 8.8 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 reasoning mode: Kimi K2.6 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, Kimi K2.6 lists $0.75/1M input and $3.5/1M output tokens, while Qwen3.5-9B lists $0.1/1M input and $0.15/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-9B lower by about $1.46 per million blended tokens. Availability is 5 providers versus 3, so concentration risk also matters.
Choose Kimi K2.6 when coding workflow support and broader provider choice are central to the workload. Choose Qwen3.5-9B when vision-heavy evaluation 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, Kimi K2.6 or Qwen3.5-9B?
Kimi K2.6 supports 262K tokens, while Qwen3.5-9B 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, Kimi K2.6 or Qwen3.5-9B?
Qwen3.5-9B is cheaper on tracked token pricing. Kimi K2.6 costs $0.75/1M input and $3.5/1M output tokens. Qwen3.5-9B costs $0.1/1M input and $0.15/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.6 or Qwen3.5-9B open source?
Kimi K2.6 is listed under Open Source. Qwen3.5-9B 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, Kimi K2.6 or Qwen3.5-9B?
Both Kimi K2.6 and Qwen3.5-9B 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, Kimi K2.6 or Qwen3.5-9B?
Both Kimi K2.6 and Qwen3.5-9B 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 Kimi K2.6 and Qwen3.5-9B?
Kimi K2.6 is available on NVIDIA NIM, Moonshot AI Kimi, Fireworks AI, OpenRouter, and Microsoft Foundry. Qwen3.5-9B is available on Together AI, OpenRouter, and Alibaba Cloud PAI-EAS. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-16. Data sourced from public model cards and provider documentation.