Last verified 2026-03-12 (left) · 2026-04-21 (right)

DeepSeek Reasoner vs Kimi K2.6 — Pricing & Capability Comparison

DeepSeek Reasoner charges $0.55 per million input tokens and $2.19 per million output tokens. Kimi K2.6 comes in at $0.95 / $4.00. Context windows span 64K vs 262K tokens respectively.

TL;DR — Quick Comparison

✓DeepSeek Reasoner is cheaper overall: $2.74 per 1M tokens (in+out) vs $4.95 for Kimi K2.6 — saves $2.21 per 1M tokens
✓Input pricing: DeepSeek Reasoner $0.55/1M vs Kimi K2.6 $0.95/1M
✓Output pricing: DeepSeek Reasoner $2.19/1M vs Kimi K2.6 $4.00/1M
✓Context window: Kimi K2.6 offers more (262K vs 64K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

DeepSeek Reasoner

$0.55

Kimi K2.6

$0.95

DeepSeek Reasoner leads here

Output price (per 1M)

DeepSeek Reasoner

$2.19

Kimi K2.6

$4.00

DeepSeek Reasoner leads here

Context window

DeepSeek Reasoner

64,000 tokens

Kimi K2.6

262,144 tokens

Kimi K2.6 leads here

Cached input

DeepSeek Reasoner

$0.140

Kimi K2.6

$0.160

DeepSeek Reasoner leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between DeepSeek Reasoner and Kimi K2.6.

Choose DeepSeek Reasoner if input tokens dominate your bill

DeepSeek Reasoner has the lower input rate, which usually matters most for chat, RAG, classification, and long-prompt workflows where prompt volume stays much larger than generated output.

Choose DeepSeek Reasoner if you generate long answers

DeepSeek Reasoner is cheaper on output tokens, so it tends to win for report generation, coding assistance, reasoning traces, and any workflow where completions are long.

Choose Kimi K2.6 if context size is the blocker

Kimi K2.6 offers the larger published context window, which is more important than small pricing differences when you need to fit large files, long chats, or multi-document prompts into one request.

Cost comparison for 10K-token workloads

Side-by-side pricing for identical workloads (10,000 total tokens per request) across different distributions.

Scenario	DeepSeek Reasoner	Kimi K2.6	DeepSeek Reasoner cached	Kimi K2.6 cached
Balanced conversation 50% input · 50% output	$0.0137	$0.0248	$0.0117	$0.0208
Input-heavy workflow 80% input · 20% output	$0.0088	$0.0156	$0.0055	$0.0093
Generation heavy 30% input · 70% output	$0.0170	$0.0309	$0.0158	$0.0285
Cached system prompt 90% cached input · 10% fresh output	$0.0071	$0.0125	$0.0034	$0.0054

Frequently asked questions

Which is cheaper: DeepSeek Reasoner or Kimi K2.6?

DeepSeek Reasoner is cheaper for input tokens at $0.55 per 1M tokens compared to $0.95. For output, DeepSeek Reasoner costs $2.19 per 1M tokens versus $4.00 for Kimi K2.6.

What is the cost per 1M tokens for DeepSeek Reasoner?

DeepSeek Reasoner pricing: $0.55 per 1M input tokens and $2.19 per 1M output tokens. Context window: 64,000 tokens.

What is the cost per 1M tokens for Kimi K2.6?

Kimi K2.6 pricing: $0.95 per 1M input tokens and $4.00 per 1M output tokens. Context window: 262,144 tokens.

How much does it cost per 1K tokens?

Per 1K tokens: DeepSeek Reasoner costs $0.0006 input / $0.0022 output. Kimi K2.6 costs $0.0009 input / $0.0040 output. This is useful for calculating small-scale usage costs.

Which model supports a larger context window?

Kimi K2.6 offers 262,144 tokens (262K) versus 64K for DeepSeek Reasoner.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: DeepSeek Reasoner would cost approximately $9.88, while Kimi K2.6 would cost $17.50. DeepSeek Reasoner is more economical for this usage pattern.

Do these models support prompt caching?

DeepSeek Reasoner supports prompt caching at $0.140 per 1M cached tokens, reducing costs for repeated context by up to 75%. Kimi K2.6 supports caching at $0.160 per 1M tokens, saving up to 83%.

Which model is best for my use case?

Choose DeepSeek Reasoner for cost-sensitive applications with high input volume. Choose Kimi K2.6 if you need 262K context for long documents or conversations. Consider prompt caching if you have repeated context. Use our token calculator to model your specific usage pattern.