DeepSeek V3 vs Kimi K2
DeepSeek V3 (2024) and Kimi K2 (2025) are compact production models from DeepSeek and Moonshot AI. DeepSeek V3 ships a 64k-token context window, while Kimi K2 ships a 262K-token context window. On pricing, DeepSeek V3 costs $0.1/1M input tokens versus $0.5/1M for the alternative. This comparison covers specs, pricing, capabilities, benchmarks, provider availability, and production fit. It focuses on practical selection signals rather than broad model-family marketing.
DeepSeek V3 is ~400% cheaper at $0.1/1M; pay for Kimi K2 only for long-context analysis.
Decision scorecard
Local evidence first| Signal | DeepSeek V3 | Kimi K2 |
|---|---|---|
| Decision fit | Coding, Agents, and Classification | RAG, Agents, and Long context |
| Context window | 64k | 262K |
| Cheapest output | $0.3/1M tokens | $2/1M tokens |
| Provider routes | 12 tracked | 3 tracked |
| Shared benchmarks | 0 rows | 0 rows |
Decision tradeoffs
- DeepSeek V3 has the lower cheapest tracked output price at $0.3/1M tokens.
- DeepSeek V3 has broader tracked provider coverage for fallback and procurement flexibility.
- DeepSeek V3 uniquely exposes Tool use in local model data.
- Local decision data tags DeepSeek V3 for Coding, Agents, and Classification.
- Kimi K2 has the larger context window for long prompts, retrieval packs, or transcript analysis.
- Local decision data tags Kimi K2 for RAG, Agents, and Long context.
Monthly cost at traffic
Estimate token spend from the cheapest tracked input and output prices on this page.
DeepSeek V3
$155
Cheapest tracked route: Bitdeer AI
Kimi K2
$900
Cheapest tracked route: AWS Bedrock
Estimated monthly gap: $745. Batch, cache, and negotiated pricing are excluded from this local estimate.
Switch friction
- Provider overlap exists on OpenRouter, AWS Bedrock, and GCP Vertex AI; start route-level A/B tests there.
- Kimi K2 is $1.7/1M tokens higher on cheapest tracked output pricing, so quality gains need to justify the spend.
- Check replacement coverage for Tool use before moving production traffic.
- Provider overlap exists on OpenRouter, AWS Bedrock, and GCP Vertex AI; start route-level A/B tests there.
- DeepSeek V3 is $1.7/1M tokens lower on cheapest tracked output pricing before cache, batch, or negotiated discounts.
- DeepSeek V3 adds Tool use in local capability data.
Specs
| Specification | ||
|---|---|---|
| Released | 2024-12-26 | 2025-07-11 |
| Context window | 64k | 262K |
| Parameters | 671B | 1K |
| Architecture | mixture of experts | - |
| License | Open Source | Proprietary |
| Knowledge cutoff | 2024-04 | - |
Pricing and availability
| Pricing attribute | DeepSeek V3 | Kimi K2 |
|---|---|---|
| Input price | $0.1/1M tokens | $0.5/1M tokens |
| Output price | $0.3/1M tokens | $2/1M tokens |
| Providers |
Capabilities
| Capability | DeepSeek V3 | Kimi K2 |
|---|---|---|
| Vision | No | No |
| Multimodal | No | No |
| Reasoning | No | No |
| Function calling | Yes | Yes |
| Tool use | Yes | No |
| Structured outputs | Yes | Yes |
| Code execution | No | No |
Benchmarks
No shared benchmark rows are currently sourced for this pair.
Deep dive
The capability footprint differs most on tool use: DeepSeek V3. 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, DeepSeek V3 lists $0.1/1M input and $0.3/1M output tokens, while Kimi K2 lists $0.5/1M input and $2/1M output tokens on the cheapest tracked provider. A 70/30 input-output blend puts DeepSeek V3 lower by about $0.79 per million blended tokens. Availability is 12 providers versus 3, so concentration risk also matters.
Choose DeepSeek V3 when provider fit, lower input-token cost, and broader provider choice are central to the workload. Choose Kimi K2 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. This keeps the decision grounded in measurable tradeoffs instead of brand-level assumptions. It also helps separate model capability from provider packaging, which can change cost and latency. For teams standardizing a stack, that distinction is often the difference between a benchmark win and a reliable deployment.
FAQ
Which has a larger context window, DeepSeek V3 or Kimi K2?
Kimi K2 supports 262K tokens, while DeepSeek V3 supports 64k 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 or Kimi K2?
DeepSeek V3 is cheaper on tracked token pricing. DeepSeek V3 costs $0.1/1M input and $0.3/1M output tokens. Kimi K2 costs $0.5/1M input and $2/1M output tokens. Provider discounts or batch pricing can still change the final bill.
Is DeepSeek V3 or Kimi K2 open source?
DeepSeek V3 is listed under Open Source. Kimi K2 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 function calling, DeepSeek V3 or Kimi K2?
Both DeepSeek V3 and Kimi K2 expose function calling. 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 tool use, DeepSeek V3 or Kimi K2?
DeepSeek V3 has the clearer documented tool use signal in this comparison. If tool use 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 and Kimi K2?
DeepSeek V3 is available on DeepInfra, Fireworks AI, DeepSeek Platform, Microsoft Foundry, and OpenRouter. Kimi K2 is available on OpenRouter, AWS Bedrock, and GCP Vertex AI. Provider coverage can affect latency, region availability, compliance posture, and fallback options.
Continue comparing
Last reviewed: 2026-05-11. Data sourced from public model cards and provider documentation.