Ask

Kimi K3 vs DeepSeek V3.2

Detailed pricing comparison and cost analysis.

Updated September 2026

Cost Simulator

Kimi K3 Cost
$6.00
DeepSeek V3.2 Cost
$0.36
DeepSeek V3.2 is 94% cheaper
FeatureKimi K3DeepSeek V3.2
ProviderMoonshot AIDeepSeek
Input Price (1M)$3.00$0.28
Output Price (1M)$15.00$0.42
Context Window1,000,000128,000

Verdict

Kimi K3 costs $3.00 per 1M input tokens and $15.00 per 1M output tokens. DeepSeek V3.2 costs $0.28 per 1M input tokens and $0.42 per 1M output tokens. DeepSeek V3.2 is 91% cheaper on input tokens than Kimi K3. For output tokens, DeepSeek V3.2 is the more affordable option at $0.42/1M vs $15.00.

On context window, Kimi K3 supports 1,000,000 tokens — meaning it can fit more conversation history, documents, or code in a single request. This matters for RAG pipelines, long document analysis, and agentic workflows where context builds up over many turns.

When to choose Kimi K3

  • ✓ You need a larger context window (1,000,000 tokens)
  • ✓ You are already integrated with Moonshot AI

When to choose DeepSeek V3.2

  • ✓ You need the lowest input token cost ($ 0.28/1M)
  • ✓ Your workload is output-heavy — DeepSeek V3.2 generates text cheaper
  • ✓ You are already integrated with DeepSeek

Use the calculator above to simulate your specific workload and find the exact break-even point. For most applications, the cheapest model is the one that minimises your total monthly bill given your input-to-output token ratio.

Frequently Asked Questions

Is Kimi K3 cheaper than DeepSeek V3.2?

DeepSeek V3.2 is cheaper on input tokens at $0.28/1M vs $3.00/1M for Kimi K3 — a 91% saving.

What is the context window of Kimi K3 vs DeepSeek V3.2?

Kimi K3 has a 1,000,000-token context window. DeepSeek V3.2 has a 128,000-token context window. Kimi K3 supports the larger context, suitable for longer documents and agentic workflows.

Which model is better: Kimi K3 or DeepSeek V3.2?

The best choice depends on your use case. For cost efficiency on input tokens, DeepSeek V3.2 is the cheaper option. For maximum context length, Kimi K3 supports 1,000,000 tokens. Use the comparison table above to find the right fit for your workload.