GPT-4.1 NanovsKimi K2.6
GPT-4.1 Nano is cheaper than Kimi K2.6 at $0.1/M vs $0.95/M input tokens.
| Spec | GPT-4.1 Nano | Kimi K2.6 |
|---|---|---|
| Input price | $0.1/M tokens | $0.95/M tokens |
| Output price | $0.4/M tokens | $4/M tokens |
| Context window | 1.0M tokens | 262K tokens |
| Weights | Closed | Open |
| Free API (OpenRouter) | No | No |
| Released | Apr 2025 | Apr 2026 |
Our Verdict
GPT-4.1 Nano
Kimi K2.6
Not enough votes to call it. On the specs, nothing separates them.
GPT-4.1 Nano costs 10x less per token.
Too close to call
Reviewing agent-written code?See a Brief PR report
API pricing
Cost per 1M tokens
GPT-4.1 Nano
Input
$0.10
9.5× cheaper
Output
$0.40
10× cheaper
Kimi K2.6
Input
$0.95
Output
$4.00
GPT-4.1 Nano is cheaper on both: 9.5× input, 10× output.
Where to run it
22 hosts, cheapest first
GPT-4.1 Nano2 hosts
Kimi K2.620 hosts
Chutesint4$0.58 in$3.40 out262k98.3% upDecartfp4$0.59 in$2.47 out262k99.6% upInceptronint4$0.59 in$2.94 out262k99.6% upStreamLakefp8$0.60 in$2.52 out256k96.8% upCoreWeavefp4$0.65 in$3.41 out262k99.8% upCrusoebf16$0.70 in$3.50 out262k99.5% up
14 more hostsFewer hosts
DeepInfrafp4$0.75 in$3.50 out262k99% upParasailint4$0.75 in$3.50 out262k99.6% upVeniceint4$0.75 in$3.50 out256k97.7% upSiliconFlowfp8$0.77 in$3.40 out262k99.6% upNovita$0.80 in$3.40 out262k99% upGMI Cloudfp8$0.85 in$3.60 out262k98.3% upAtlasCloudint4$0.95 in$4.00 out262k97.4% upBaidu Qianfanfp4$0.95 in$4.00 out262k99.8% upBasetenfp4$0.95 in$4.00 out262k—Cloudflare Workers AI$0.95 in$4.00 out262k99.9% upDigitalOcean$0.95 in$4.00 out262k99.7% upFireworks$0.95 in$4.00 out262k0% upMoonshot AIint4$0.95 in$4.00 out262k99.7% upPhala$1.09 in$4.60 out262k97.5% up
Per million tokens. Prices and uptime via OpenRouter, checked 11 Sep 2026.
Writing DNA
Style Comparison
Similarity
27%
Kimi K2.6 uses 3.4x more emoji
GPT-4.1 Nano
Kimi K2.6
57%Vocabulary57%
25wSentence Length16w
0.72Hedging0.37
5.6Bold4.3
4.9Lists2.0
0.00Emoji0.03
0.71Headings0.53
0.22Transitions0.16
Based on 24 + 27 text responses
Research
What we learned reading every model
FAQ
Common questions
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