Kimi K2.5 vs Qwen3.6-35B-A3B
Kimi K2.5 (2026) and Qwen3.6-35B-A3B (2026) are agentic coding models from Moonshot AI and Alibaba. Kimi K2.5 ships a 256k-token context window, while Qwen3.6-35B-A3B ships a 262k-token context window. On MMLU PRO, Kimi K2.5 leads by 1.9 pts. On pricing, Qwen3.6-35B-A3B costs $0.15/1M input tokens versus $0.44/1M 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.
Qwen3.6-35B-A3B is ~193% cheaper at $0.15/1M; pay for Kimi K2.5 only for coding workflow support.
Decision scorecard
Local evidence first| Signal | Kimi K2.5 | Qwen3.6-35B-A3B |
|---|---|---|
| Best for | custom coding agents, code generation, and tool loops | custom coding agents, code generation, and tool loops |
| Decision fit | Coding, RAG, and Agents | Coding, RAG, and Agents |
| Context window | 256k | 262k |
| Cheapest output | $2/1M tokens | $1/1M tokens |
| Provider routes | 10 tracked | 2 tracked |
| Shared benchmarks | MMLU PRO leader | 5 rows |
Decision tradeoffs
- Kimi K2.5 holds a shared-benchmark lead on MMLU PRO, ahead by 1.9 points.
- Kimi K2.5 has broader tracked provider coverage for fallback and procurement flexibility.
- Kimi K2.5 uniquely exposes Structured outputs in local model data.
- Local decision data tags Kimi K2.5 for Coding, RAG, and Agents.
- Qwen3.6-35B-A3B has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Qwen3.6-35B-A3B has the lower cheapest tracked output price at $1/1M tokens.
- Qwen3.6-35B-A3B uniquely exposes 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.
Kimi K2.5
$852
Cheapest tracked route/tier: OpenRouter
Qwen3.6-35B-A3B
$370
Cheapest tracked route/tier: OpenRouter
Estimated monthly gap: $482. Batch, cache, alternate speed tiers, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter and Novita AI; start route-level A/B tests there.
- Qwen3.6-35B-A3B is $1/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- Check replacement coverage for Structured outputs before moving production traffic.
- Qwen3.6-35B-A3B adds Tool use in local capability data.
- Provider overlap exists on OpenRouter and Novita AI; start route-level A/B tests there.
- Kimi K2.5 is $1/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Tool use before moving production traffic.
- Kimi K2.5 adds Structured outputs in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2026-03-15 | 2026-04-16 |
| Context window | 256k | 262k |
| Parameters | 1T (MoE, 384 experts) | 35B |
| Architecture | mixture of experts | moe |
| License | Proprietary | Apache 2.0(OSI) |
| Openness | Proprietary | Open source |
| Commercial use | Commercial use with conditions | Commercial use allowed |
| Knowledge cutoff | - | - |
Pricing and availability
| Pricing attribute | Kimi K2.5 | Qwen3.6-35B-A3B |
|---|---|---|
| Input price | $0.44/1M tokens | $0.15/1M tokens |
| Output price | $2/1M tokens | $1/1M tokens |
| Providers |
Capabilities
| Capability | Kimi K2.5 | Qwen3.6-35B-A3B |
|---|---|---|
| Vision | Yes | Yes |
| Multimodal | Yes | Yes |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | No | Yes |
| Structured outputs | Yes | No |
| Code execution | No | No |
| IDE integration | No | No |
| Computer use | No | No |
| Parallel agents | No | No |
Benchmarks
| Benchmark | Kimi K2.5 | Qwen3.6-35B-A3B |
|---|---|---|
| MMLU PRO | 87.1 | 85.2 |
| SWE-bench Verified | 76.8 | 73.4 |
| Google-Proof Q&A | 87.9 | 86.0 |
| LiveCodeBench | 85.0 | 80.4 |
| MMMU Pro | 78.5 | 75.3 |
Deep dive
On shared benchmark coverage, MMLU PRO has Kimi K2.5 at 87.1 and Qwen3.6-35B-A3B at 85.2, with Kimi K2.5 ahead by 1.9 points; SWE-bench Verified has Kimi K2.5 at 76.8 and Qwen3.6-35B-A3B at 73.4, with Kimi K2.5 ahead by 3.4 points; Google-Proof Q&A has Kimi K2.5 at 87.9 and Qwen3.6-35B-A3B at 86, with Kimi K2.5 ahead by 1.9 points. The largest visible gap is 3.4 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 tool use: Qwen3.6-35B-A3B and structured outputs: Kimi K2.5. Both models share vision, multimodal input, and function calling, 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 K2.5 lists $0.44/1M input and $2/1M 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 $0.50 per million blended tokens. Availability is 10 providers versus 2, so concentration risk also matters.
Choose Kimi K2.5 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, Kimi K2.5 or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B supports 262k tokens, while Kimi K2.5 supports 256k 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 K2.5 or Qwen3.6-35B-A3B?
Qwen3.6-35B-A3B is cheaper on tracked token pricing. Kimi K2.5 costs $0.44/1M input and $2/1M 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 Kimi K2.5 or Qwen3.6-35B-A3B open source?
Kimi K2.5 is listed under Proprietary. 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, Kimi K2.5 or Qwen3.6-35B-A3B?
Both Kimi K2.5 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, Kimi K2.5 or Qwen3.6-35B-A3B?
Both Kimi K2.5 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 Kimi K2.5 and Qwen3.6-35B-A3B?
Kimi K2.5 is available on Cloudflare Workers AI, Fireworks AI, OpenRouter, Together AI, and NVIDIA NIM. 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.