Kimi K2.5 vs Qwen3.5-397B-A17B
Kimi K2.5 (2026) and Qwen3.5-397B-A17B (2026) are agentic coding models from Moonshot AI and Alibaba. Kimi K2.5 ships a 256K-token context window, while Qwen3.5-397B-A17B ships a 262K-token context window. On MMLU PRO, Qwen3.5-397B-A17B leads by a hair. On pricing, Kimi K2.5 costs $0.38/1M input tokens versus $0.39/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Kimi K2.5 is safer overall; choose Qwen3.5-397B-A17B when long-context analysis matters.
Specs
| Released | 2026-03-15 | 2026-02-16 |
| Context window | 256K | 262K |
| Parameters | 1T (MoE, 384 experts) | 397B |
| Architecture | mixture of experts | MoE |
| License | MIT | Apache 2.0 |
| Knowledge cutoff | - | - |
Pricing and availability
| Kimi K2.5 | Qwen3.5-397B-A17B | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.39/1M tokens |
| Output price | $1.72/1M tokens | $2.34/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | Qwen3.5-397B-A17B | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Kimi K2.5 | Qwen3.5-397B-A17B |
|---|---|---|
| MMLU PRO | 87.1 | 87.8 |
| Google-Proof Q&A | 87.9 | 89.3 |
| BFCL | 68.3 | 72.9 |
| Instruction-Following Evaluation | 93.9 | 92.6 |
| Massive Multi-discipline Multimodal Understanding | 84.3 | 85.0 |
Deep dive
On shared benchmark coverage, MMLU PRO has Kimi K2.5 at 87.1 and Qwen3.5-397B-A17B at 87.8, with Qwen3.5-397B-A17B ahead by 0.7 points; Google-Proof Q&A has Kimi K2.5 at 87.9 and Qwen3.5-397B-A17B at 89.3, with Qwen3.5-397B-A17B ahead by 1.4 points; BFCL has Kimi K2.5 at 68.3 and Qwen3.5-397B-A17B at 72.9, with Qwen3.5-397B-A17B ahead by 4.6 points. The largest visible gap is 4.6 points on BFCL, 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 multimodal input: Qwen3.5-397B-A17B and function calling: Kimi K2.5. Both models share 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 K2.5 lists $0.38/1M input and $1.72/1M output tokens, while Qwen3.5-397B-A17B lists $0.39/1M input and $2.34/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $0.19 per million blended tokens. Availability is 7 providers versus 1, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support, lower input-token cost, and broader provider choice are central to the workload. Choose Qwen3.5-397B-A17B when long-context analysis and larger context windows 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 Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B 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 Qwen3.5-397B-A17B?
Kimi K2.5 is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Qwen3.5-397B-A17B costs $0.39/1M input and $2.34/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Qwen3.5-397B-A17B open source?
Kimi K2.5 is listed under MIT. Qwen3.5-397B-A17B 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 multimodal input, Kimi K2.5 or Qwen3.5-397B-A17B?
Qwen3.5-397B-A17B has the clearer documented multimodal input signal in this comparison. If multimodal input 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 K2.5 or Qwen3.5-397B-A17B?
Kimi K2.5 has the clearer documented function calling signal in this comparison. If function calling is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run Kimi K2.5 and Qwen3.5-397B-A17B?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Qwen3.5-397B-A17B is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
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Last reviewed: 2026-04-27. Data sourced from public model cards and provider documentation.