Kimi K2 vs Qwen3.5-35B-A3B
Kimi K2 (2025) and Qwen3.5-35B-A3B (2026) are frontier reasoning models from Moonshot AI and Alibaba. Kimi K2 ships a 262K-token context window, while Qwen3.5-35B-A3B ships a 262K-token context window. On pricing, Qwen3.5-35B-A3B costs $0.16/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Qwen3.5-35B-A3B is ~208% cheaper at $0.16/1M; pay for Kimi K2 only for provider fit.
Decision scorecard
Local evidence first| Signal | Kimi K2 | Qwen3.5-35B-A3B |
|---|---|---|
| Decision fit | RAG, Agents, and Long context | RAG, Agents, and Long context |
| Context window | 262K | 262K |
| Cheapest output | $2/1M tokens | $1.3/1M tokens |
| Provider routes | 3 tracked | 1 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- Kimi K2 has broader tracked provider coverage for fallback and procurement flexibility.
- Local decision data tags Kimi K2 for RAG, Agents, and Long context.
- Qwen3.5-35B-A3B has the lower cheapest tracked output price at $1.3/1M tokens.
- Qwen3.5-35B-A3B uniquely exposes Reasoning and Tool use in local model data.
- Local decision data tags Qwen3.5-35B-A3B 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
$900
Cheapest tracked route: AWS Bedrock
Qwen3.5-35B-A3B
$455
Cheapest tracked route: OpenRouter
Estimated monthly gap: $445. 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.
- Qwen3.5-35B-A3B is $0.7/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Qwen3.5-35B-A3B adds Reasoning and Tool use in local capability data.
- Provider overlap exists on OpenRouter; start route-level A/B tests there.
- Kimi K2 is $0.7/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Reasoning and Tool use before moving production traffic.
Specs
| Specification | ||
|---|---|---|
| Released | 2025-07-11 | 2026-02-24 |
| Context window | 262K | 262K |
| Parameters | 1K | 35B |
| Architecture | - | mixture of experts |
| License | Proprietary | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Kimi K2 | Qwen3.5-35B-A3B |
|---|---|---|
| Input price | $0.5/1M tokens | $0.16/1M tokens |
| Output price | $2/1M tokens | $1.3/1M tokens |
| Providers |
Capabilities
| Capability | Kimi K2 | Qwen3.5-35B-A3B |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | Yes |
| Function calling | Yes | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on reasoning mode: Qwen3.5-35B-A3B and tool use: Qwen3.5-35B-A3B. Both models share function calling 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, Kimi K2 lists $0.5/1M input and $2/1M output tokens, while Qwen3.5-35B-A3B lists $0.16/1M input and $1.3/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Qwen3.5-35B-A3B lower by about $0.45 per million blended tokens. Availability is 3 providers versus 1, so concentration risk also matters.
Choose Kimi K2 when provider fit and broader provider choice are central to the workload. Choose Qwen3.5-35B-A3B when reasoning depth 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, Kimi K2 or Qwen3.5-35B-A3B?
Kimi K2 supports 262K tokens, while Qwen3.5-35B-A3B 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 or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B is cheaper on tracked token pricing. Kimi K2 costs $0.5/1M input and $2/1M output tokens. Qwen3.5-35B-A3B costs $0.16/1M input and $1.3/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2 or Qwen3.5-35B-A3B open source?
Kimi K2 is listed under Proprietary. Qwen3.5-35B-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 reasoning mode, Kimi K2 or Qwen3.5-35B-A3B?
Qwen3.5-35B-A3B has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Which is better for function calling, Kimi K2 or Qwen3.5-35B-A3B?
Both Kimi K2 and Qwen3.5-35B-A3B expose function calling. 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 and Qwen3.5-35B-A3B?
Kimi K2 is available on OpenRouter, AWS Bedrock, and GCP Vertex AI. Qwen3.5-35B-A3B is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options. Use this as a quick comparison signal, then confirm the provider-specific limits before committing to production.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.