Kimi K2.5 vs Qwen3-Max
Kimi K2.5 (2026) and Qwen3-Max (2026) are agentic coding models from Moonshot AI and Alibaba. Kimi K2.5 ships a 256K-token context window, while Qwen3-Max ships a 128K-token context window. On MultiChallenge, Kimi K2.5 leads by 20.2 pts. On pricing, Kimi K2.5 costs $0.38/1M input tokens versus $0.78/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
Kimi K2.5 is ~104% cheaper at $0.38/1M; pay for Qwen3-Max only for vision-heavy evaluation.
Specs
| Released | 2026-03-15 | 2026-01-15 |
| Context window | 256K | 128K |
| Parameters | 1T (MoE, 384 experts) | — |
| Architecture | mixture of experts | decoder only |
| License | MIT | Proprietary |
| Knowledge cutoff | - | 2025-12 |
Pricing and availability
| Kimi K2.5 | Qwen3-Max | |
|---|---|---|
| Input price | $0.38/1M tokens | $0.78/1M tokens |
| Output price | $1.72/1M tokens | $3.9/1M tokens |
| Providers |
Capabilities
| Kimi K2.5 | Qwen3-Max | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | Kimi K2.5 | Qwen3-Max |
|---|---|---|
| MultiChallenge | 61.4 | 41.2 |
| τ-bench | 74.2 | 76.8 |
Deep dive
On shared benchmark coverage, MultiChallenge has Kimi K2.5 at 61.4 and Qwen3-Max at 41.2, with Kimi K2.5 ahead by 20.2 points; τ-bench has Kimi K2.5 at 74.2 and Qwen3-Max at 76.8, with Qwen3-Max ahead by 2.6 points. The largest visible gap is 20.2 points on MultiChallenge, 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 vision: Qwen3-Max, multimodal input: Qwen3-Max, and tool use: Qwen3-Max. 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 K2.5 lists $0.38/1M input and $1.72/1M output tokens, while Qwen3-Max lists $0.78/1M input and $3.9/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts Kimi K2.5 lower by about $0.93 per million blended tokens. Availability is 7 providers versus 1, so concentration risk also matters.
Choose Kimi K2.5 when coding workflow support, larger context windows, and lower input-token cost are central to the workload. Choose Qwen3-Max when vision-heavy evaluation 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-Max?
Kimi K2.5 supports 256K tokens, while Qwen3-Max supports 128K 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-Max?
Kimi K2.5 is cheaper on tracked token pricing. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Qwen3-Max costs $0.78/1M input and $3.9/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is Kimi K2.5 or Qwen3-Max open source?
Kimi K2.5 is listed under MIT. Qwen3-Max is listed under Proprietary. 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-Max?
Qwen3-Max has the clearer documented vision signal in this comparison. If vision is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ. 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-Max?
Qwen3-Max 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.
Where can I run Kimi K2.5 and Qwen3-Max?
Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. Qwen3-Max is available on OpenRouter. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-04-27. Data sourced from public model cards and provider documentation.