DeepSeek V3.2 vs Kimi K2.5
DeepSeek V3.2 (2025) and Kimi K2.5 (2026) are agentic coding models from DeepSeek and Moonshot AI. DeepSeek V3.2 ships a 160K-token context window, while Kimi K2.5 ships a 256K-token context window. On Google-Proof Q&A, Kimi K2.5 leads by 3.9 pts. On pricing, DeepSeek V3.2 costs $0.26/1M input tokens versus $0.38/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit.
DeepSeek V3.2 is ~48% cheaper at $0.26/1M; pay for Kimi K2.5 only for coding workflow support.
Specs
| Released | 2025-01-01 | 2026-03-15 |
| Context window | 160K | 256K |
| Parameters | 671B | 1T (MoE, 384 experts) |
| Architecture | decoder only | mixture of experts |
| License | Open Source | MIT |
| Knowledge cutoff | - | - |
Pricing and availability
| DeepSeek V3.2 | Kimi K2.5 | |
|---|---|---|
| Input price | $0.26/1M tokens | $0.38/1M tokens |
| Output price | $0.42/1M tokens | $1.72/1M tokens |
| Providers |
Capabilities
| DeepSeek V3.2 | Kimi K2.5 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | DeepSeek V3.2 | Kimi K2.5 |
|---|---|---|
| Google-Proof Q&A | 84.0 | 87.9 |
Deep dive
On shared benchmark coverage, Google-Proof Q&A has DeepSeek V3.2 at 84 and Kimi K2.5 at 87.9, with Kimi K2.5 ahead by 3.9 points. The largest visible gap is 3.9 points on Google-Proof Q&A, 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 function calling: Kimi K2.5 and code execution: DeepSeek V3.2. 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, DeepSeek V3.2 lists $0.26/1M input and $0.42/1M output tokens, while Kimi K2.5 lists $0.38/1M input and $1.72/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek V3.2 lower by about $0.48 per million blended tokens. Availability is 4 providers versus 7, so concentration risk also matters.
Choose DeepSeek V3.2 when coding workflow support and lower input-token cost are central to the workload. Choose Kimi K2.5 when coding workflow support, larger context windows, and broader provider choice 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, DeepSeek V3.2 or Kimi K2.5?
Kimi K2.5 supports 256K tokens, while DeepSeek V3.2 supports 160K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Which is cheaper, DeepSeek V3.2 or Kimi K2.5?
DeepSeek V3.2 is cheaper on tracked token pricing. DeepSeek V3.2 costs $0.26/1M input and $0.42/1M output tokens. Kimi K2.5 costs $0.38/1M input and $1.72/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3.2 or Kimi K2.5 open source?
DeepSeek V3.2 is listed under Open Source. Kimi K2.5 is listed under MIT. 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 function calling, DeepSeek V3.2 or Kimi K2.5?
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.
Which is better for structured outputs, DeepSeek V3.2 or Kimi K2.5?
Both DeepSeek V3.2 and Kimi K2.5 expose structured outputs. 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 DeepSeek V3.2 and Kimi K2.5?
DeepSeek V3.2 is available on Fireworks AI, NVIDIA NIM, AWS Bedrock, and OpenRouter. Kimi K2.5 is available on Fireworks AI, OpenRouter, Together AI, Fireworks AI, and NVIDIA NIM. 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.