K2.5 is the stronger single model; Qwen3.5 is the stronger platform. Kimi takes peak capability and multimodality. Qwen answers with Apache 2.0 (no attribution thresholds at any scale), 2–3× cheaper serving via 17B active parameters, and a size ladder built for fine-tuning. Product teams chasing capability pick Kimi; platform teams standardizing an ML stack pick Qwen.
Qwen3.5 vs Kimi K2.5 at a glance
| Qwen3.5 | Kimi K2.5 | |
|---|---|---|
| Vendor | Alibaba (Qwen team) (China) | Moonshot AI (China) |
| Open weights | Yes — downloadable | Yes — downloadable |
| License | Apache 2.0 — permissive, patent-granting, free for commercial use | Modified MIT — free commercial use; attribution required above 100M monthly active users or $20M monthly revenue |
| Parameters | 397B (17B active) | 1T (32B active) |
| Context window | 262K tokens (native) | 256K tokens |
| Modalities | text | text, image, video |
| Pricing | Varies by hosting provider (open weights) | $0.60 in / $2.50 out per 1M tokens |
| Released | 2026-02-16 | 2026-01-27 |
Specs and pricing verified July 2026.
About Qwen3.5
Qwen3.5 is Alibaba's February 2026 generation and the efficiency benchmark of the open-weight field: the 397B-A17B flagship activates just 17B parameters per token yet beats Alibaba's own trillion-parameter Qwen3-Max — at 8.6× the throughput at 32K context and 19× at 256K. All open-weight variants ship under Apache 2.0, the most enterprise-friendly license in common use.
The release is a full family, not one model: from the 397B flagship down to a 9B variant with a native 256K context window that runs on consumer hardware. That spread — one architecture, Apache 2.0 everywhere, sizes from phone to datacenter — is why Qwen has become the default base-model family for fine-tuners and the most-adopted open-weight line by download count.
Full specs, benchmarks, and hardware guidance: the Qwen3.5 page.
About Kimi K2.5
Kimi K2.5 is Moonshot AI's trillion-parameter multimodal agent model, released January 27, 2026. Built by continual-pretraining on ~15 trillion mixed visual and text tokens on top of the Kimi-K2 base, it natively understands text, images, and video, activates 32B parameters per token, and ships both instant and thinking modes.
Its defining feature is agentic: K2.5 introduced Agent Swarm, coordinating up to 100 specialized agents on a single task, and posted 76.8% on SWE-bench Verified — frontier-class coding from an open-weight release. The Modified MIT license is effectively free for everyone below 100M monthly users or $20M monthly revenue, at which point attribution (not payment) kicks in.
Full specs, benchmarks, and hardware guidance: the Kimi K2.5 page.
Choosing between them
Choose Qwen3.5 for:
- Enterprise deployments that require Apache 2.0 licensing
- Efficiency-critical serving (17B active parameters, extreme throughput)
- Fine-tuning projects — the broadest size ladder in open weights
Choose Kimi K2.5 for:
- Multimodal agents that need vision and video, not just text
- Frontier coding (76.8% SWE-bench Verified) on open weights
- Multi-agent orchestration workloads (Agent Swarm)
Frequently asked questions
Is Qwen3.5 better than Kimi K2.5?
K2.5 is the stronger single model; Qwen3.5 is the stronger platform. Kimi takes peak capability and multimodality. Qwen answers with Apache 2.0 (no attribution thresholds at any scale), 2–3× cheaper serving via 17B active parameters, and a size ladder built for fine-tuning. Product teams chasing capability pick Kimi; platform teams standardizing an ML stack pick Qwen.
Which is cheaper: Qwen3.5 or Kimi K2.5?
Qwen3.5: Varies by hosting provider (open weights). Kimi K2.5: $0.60 in / $2.50 out per 1M tokens. Both are open-weight, so self-hosting costs depend on your hardware and utilization.
Can I self-host Qwen3.5 and Kimi K2.5?
Qwen3.5: yes — weights are published under Apache 2.0. Kimi K2.5: yes — weights are published under Modified MIT.