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

GPT-4.1 nano vs Kimi K2.6 — Pricing & Capability Comparison

GPT-4.1 nano charges $0.10 per million input tokens and $0.40 per million output tokens. Kimi K2.6 comes in at $0.95 / $4.00. Context windows span 128K vs 262K tokens respectively.

TL;DR — Quick Comparison

✓GPT-4.1 nano is cheaper overall: $0.50 per 1M tokens (in+out) vs $4.95 for Kimi K2.6 — saves $4.45 per 1M tokens
✓Input pricing: GPT-4.1 nano $0.10/1M vs Kimi K2.6 $0.95/1M
✓Output pricing: GPT-4.1 nano $0.40/1M vs Kimi K2.6 $4.00/1M
✓Context window: Kimi K2.6 offers more (262K vs 128K)
✓Use our calculator below to estimate costs for your specific usage pattern

Input price (per 1M)

GPT-4.1 nano

$0.10

Kimi K2.6

$0.95

GPT-4.1 nano leads here

Output price (per 1M)

GPT-4.1 nano

$0.40

Kimi K2.6

$4.00

GPT-4.1 nano leads here

Context window

GPT-4.1 nano

128,000 tokens

Kimi K2.6

262,144 tokens

Kimi K2.6 leads here

Cached input

GPT-4.1 nano

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 GPT-4.1 nano and Kimi K2.6.

Choose GPT-4.1 nano if input tokens dominate your bill

GPT-4.1 nano 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 GPT-4.1 nano if you generate long answers

GPT-4.1 nano 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	GPT-4.1 nano	Kimi K2.6	Kimi K2.6 cached
Balanced conversation 50% input · 50% output	$0.0025	$0.0248	$0.0208
Input-heavy workflow 80% input · 20% output	$0.0016	$0.0156	$0.0093
Generation heavy 30% input · 70% output	$0.0031	$0.0309	$0.0285
Cached system prompt 90% cached input · 10% fresh output	$0.0013	$0.0125	$0.0054

Frequently asked questions

Which is cheaper: GPT-4.1 nano or Kimi K2.6?

GPT-4.1 nano is cheaper for input tokens at $0.10 per 1M tokens compared to $0.95. For output, GPT-4.1 nano costs $0.40 per 1M tokens versus $4.00 for Kimi K2.6.

What is the cost per 1M tokens for GPT-4.1 nano?

GPT-4.1 nano pricing: $0.10 per 1M input tokens and $0.40 per 1M output tokens. Context window: 128,000 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: GPT-4.1 nano costs $0.0001 input / $0.0004 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 128K for GPT-4.1 nano.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: GPT-4.1 nano would cost approximately $1.80, while Kimi K2.6 would cost $17.50. GPT-4.1 nano is more economical for this usage pattern.

Do these models support prompt caching?

GPT-4.1 nano 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 GPT-4.1 nano 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.