DeepSeek V3.1 vs Kimi K2.6
DeepSeek V3.1 (2026) and Kimi K2.6 (2026) are agentic coding models from DeepSeek and Moonshot AI. DeepSeek V3.1 ships a 64K-token context window, while Kimi K2.6 ships a 262K-token context window. On SWE-bench Verified, Kimi K2.6 leads by 14.2 pts. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
Kimi K2.6 fits 4x more tokens; pick it for long-context work and DeepSeek V3.1 for tighter calls.
Specs
| Released | 2026-03-01 | 2026-04-13 |
| Context window | 64K | 262K |
| Parameters | — | 1T (MoE, 32B active) |
| Architecture | mixture of experts | Mixture of Experts (MoE) |
| License | Open Source | Proprietary |
| Knowledge cutoff | - | - |
Pricing and availability
| DeepSeek V3.1 | Kimi K2.6 | |
|---|---|---|
| Input price | $0.56/1M tokens | - |
| Output price | $1.68/1M tokens | - |
| Providers |
Capabilities
| DeepSeek V3.1 | Kimi K2.6 | |
|---|---|---|
| Vision | ||
| Multimodal | ||
| Reasoning | ||
| Function calling | ||
| Tool use | ||
| Structured outputs | ||
| Code execution |
Benchmarks
| Benchmark | DeepSeek V3.1 | Kimi K2.6 |
|---|---|---|
| SWE-bench Verified | 66.0 | 80.2 |
Deep dive
On shared benchmark coverage, SWE-bench Verified has DeepSeek V3.1 at 66 and Kimi K2.6 at 80.2, with Kimi K2.6 ahead by 14.2 points. The largest visible gap is 14.2 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 reasoning mode: Kimi K2.6, function calling: Kimi K2.6, structured outputs: DeepSeek V3.1, and code execution: DeepSeek V3.1. Both models share vision and multimodal input, 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.
Pricing coverage is uneven: DeepSeek V3.1 has $0.56/1M input tokens and Kimi K2.6 has no token price sourced yet. Provider availability is 6 tracked routes versus 2. Treat unknown pricing as an integration gap, then verify the route you will actually call before estimating production spend.
Choose DeepSeek V3.1 when coding workflow support and broader provider choice are central to the workload. Choose Kimi K2.6 when coding workflow support 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, DeepSeek V3.1 or Kimi K2.6?
Kimi K2.6 supports 262K tokens, while DeepSeek V3.1 supports 64K tokens. That gap matters most for long documents, large codebases, retrieval-heavy agents, and conversations where earlier context must remain visible.
Is DeepSeek V3.1 or Kimi K2.6 open source?
DeepSeek V3.1 is listed under Open Source. Kimi K2.6 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, DeepSeek V3.1 or Kimi K2.6?
Both DeepSeek V3.1 and Kimi K2.6 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, DeepSeek V3.1 or Kimi K2.6?
Both DeepSeek V3.1 and Kimi K2.6 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.
Which is better for reasoning mode, DeepSeek V3.1 or Kimi K2.6?
Kimi K2.6 has the clearer documented reasoning mode signal in this comparison. If reasoning mode is mission-critical, validate it against the provider endpoint because model-level support and API-level exposure can differ.
Where can I run DeepSeek V3.1 and Kimi K2.6?
DeepSeek V3.1 is available on Microsoft Foundry, Fireworks AI, NVIDIA NIM, Together AI, and AWS Bedrock. Kimi K2.6 is available on NVIDIA NIM and Moonshot AI Kimi. 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.