Last verified 2025-11-26 (left) · 2026-04-21 (right)

GPT-5.1 mini vs Kimi K2.6 — Pricing & Capability Comparison

GPT-5.1 mini charges $0.25 per million input tokens and $2.00 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

✓GPT-5.1 mini is cheaper overall: $2.25 per 1M tokens (in+out) vs $4.95 for Kimi K2.6 — saves $2.70 per 1M tokens
✓Input pricing: GPT-5.1 mini $0.25/1M vs Kimi K2.6 $0.95/1M
✓Output pricing: GPT-5.1 mini $2.00/1M 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)

GPT-5.1 mini

$0.25

Kimi K2.6

$0.95

GPT-5.1 mini leads here

Output price (per 1M)

GPT-5.1 mini

$2.00

Kimi K2.6

$4.00

GPT-5.1 mini leads here

Context window

GPT-5.1 mini

200,000 tokens

Kimi K2.6

262,144 tokens

Kimi K2.6 leads here

Cached input

GPT-5.1 mini

$0.025

Kimi K2.6

$0.160

GPT-5.1 mini leads here

Which one should you choose?

Skip the spreadsheet if you just need the practical takeaway. Use these rules when deciding between GPT-5.1 mini and Kimi K2.6.

Choose GPT-5.1 mini if input tokens dominate your bill

GPT-5.1 mini 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-5.1 mini if you generate long answers

GPT-5.1 mini 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-5.1 mini	Kimi K2.6	GPT-5.1 mini cached	Kimi K2.6 cached
Balanced conversation 50% input · 50% output	$0.0112	$0.0248	$0.0101	$0.0208
Input-heavy workflow 80% input · 20% output	$0.0060	$0.0156	$0.0042	$0.0093
Generation heavy 30% input · 70% output	$0.0148	$0.0309	$0.0141	$0.0285
Cached system prompt 90% cached input · 10% fresh output	$0.0043	$0.0125	$0.0022	$0.0054

Frequently asked questions

Which is cheaper: GPT-5.1 mini or Kimi K2.6?

GPT-5.1 mini is cheaper for input tokens at $0.25 per 1M tokens compared to $0.95. For output, GPT-5.1 mini costs $2.00 per 1M tokens versus $4.00 for Kimi K2.6.

What is the cost per 1M tokens for GPT-5.1 mini?

GPT-5.1 mini pricing: $0.25 per 1M input tokens and $2.00 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: GPT-5.1 mini costs $0.0003 input / $0.0020 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 GPT-5.1 mini.

What is the estimated monthly cost for typical usage?

For a typical workload of 10M input + 2M output tokens per month: GPT-5.1 mini would cost approximately $6.50, while Kimi K2.6 would cost $17.50. GPT-5.1 mini is more economical for this usage pattern.

Do these models support prompt caching?

GPT-5.1 mini supports prompt caching at $0.025 per 1M cached tokens, reducing costs for repeated context by up to 90%. 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-5.1 mini 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.