Last verified 2025-09-22 (left) · 2026-04-21 (right)

Claude Sonnet 3.7 (Legacy) vs Kimi K2.6 — Pricing & Capability Comparison

Claude Sonnet 3.7 (Legacy) charges $3.00 per million input tokens and $15.00 per million output tokens. Kimi K2.6 comes in at $0.95 / $4.00. Context windows span 200K vs 262K tokens respectively.

TL;DR — Quick Comparison

✓Kimi K2.6 is cheaper overall: $4.95 per 1M tokens (in+out) vs $18.00 for Claude Sonnet 3.7 (Legacy) — saves $13.05 per 1M tokens
✓Input pricing: Claude Sonnet 3.7 (Legacy) $3.00/1M vs Kimi K2.6 $0.95/1M
✓Output pricing: Claude Sonnet 3.7 (Legacy) $15.00/1M vs Kimi K2.6 $4.00/1M
✓Context window: Kimi K2.6 offers more (262K vs 200K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

Claude Sonnet 3.7 (Legacy)

$3.00

Kimi K2.6

$0.95

Kimi K2.6 leads here

Output price (per 1M)

Claude Sonnet 3.7 (Legacy)

$15.00

Kimi K2.6

$4.00

Kimi K2.6 leads here

Context window

Claude Sonnet 3.7 (Legacy)

200,000 tokens

Kimi K2.6

262,144 tokens

Kimi K2.6 leads here

Cached input

Claude Sonnet 3.7 (Legacy)

Not published

Kimi K2.6

$0.160

Kimi K2.6 leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between Claude Sonnet 3.7 (Legacy) and Kimi K2.6.

Choose Kimi K2.6 if input tokens dominate your bill

Kimi K2.6 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 Kimi K2.6 if you generate long answers

Kimi K2.6 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	Claude Sonnet 3.7 (Legacy)	Kimi K2.6	Kimi K2.6 cached
Balanced conversation 50% input · 50% output	$0.0900	$0.0248	$0.0208
Input-heavy workflow 80% input · 20% output	$0.0540	$0.0156	$0.0093
Generation heavy 30% input · 70% output	$0.114	$0.0309	$0.0285
Cached system prompt 90% cached input · 10% fresh output	$0.0420	$0.0125	$0.0054

Frequently asked questions

Which is cheaper: Claude Sonnet 3.7 (Legacy) or Kimi K2.6?

Kimi K2.6 is cheaper for input tokens at $0.95 per 1M tokens compared to $3.00. For output, Kimi K2.6 costs $4.00 per 1M tokens versus $15.00 for Claude Sonnet 3.7 (Legacy).

What is the cost per 1M tokens for Claude Sonnet 3.7 (Legacy)?

Claude Sonnet 3.7 (Legacy) pricing: $3.00 per 1M input tokens and $15.00 per 1M output tokens. Context window: 200,000 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: Claude Sonnet 3.7 (Legacy) costs $0.0030 input / $0.0150 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 200K for Claude Sonnet 3.7 (Legacy).

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: Claude Sonnet 3.7 (Legacy) would cost approximately $60.00, while Kimi K2.6 would cost $17.50. Kimi K2.6 is more economical for this usage pattern.

Do these models support prompt caching?

Claude Sonnet 3.7 (Legacy) does not publish cached pricing. Kimi K2.6 supports caching at $0.160 per 1M tokens, saving up to 83%.

Which model is best for my use case?

Choose Kimi K2.6 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.