Kimi K2.6vsMistral Large 2
Kimi K2.6 is cheaper than Mistral Large 2 at $0.95/M vs $8/M input tokens.
| Spec | Kimi K2.6 | Mistral Large 2 |
|---|---|---|
| Input price | $0.95/M tokens | $8/M tokens |
| Output price | $4/M tokens | $24/M tokens |
| Context window | 262K tokens | 128K tokens |
| Weights | Open | Open |
| Free API (OpenRouter) | No | No |
| Released | Apr 2026 | Jul 2024 |
Our Verdict
Kimi K2.6
Mistral Large 2Runner-up
Not enough votes to call it. On the specs, Kimi K2.6 has the edge: newer, bigger context window.
Kimi K2.6 costs 6.0x less per token.
Too close to call
API pricing
Cost per 1M tokens
Kimi K2.6
Input
$0.95
8.4× cheaper
Output
$4.00
6.0× cheaper
Mistral Large 2
Input
$8.00
Output
$24.00
Kimi K2.6 is cheaper on both: 8.4× input, 6.0× output.
Where to run it
21 hosts, cheapest first
Kimi K2.620 hosts
Baidu Qianfanfp4$0.58 in$2.44 out262k100% upChutesint4$0.58 in$3.40 out262k97.3% upDecartfp4$0.59 in$2.47 out262k100% upInceptronint4$0.59 in$2.45 out262k99.4% upStreamLakefp8$0.60 in$2.52 out256k97.9% upCoreWeavefp4$0.65 in$3.41 out262k99.9% up
14 more hostsFewer hosts
Crusoebf16$0.70 in$3.50 out262k98.6% upDeepInfrafp4$0.75 in$3.50 out262k99.5% upParasailint4$0.75 in$3.50 out262k99.4% upVeniceint4$0.75 in$3.50 out256k97.3% upSiliconFlowfp8$0.77 in$3.40 out262k99.4% upNovita$0.80 in$3.40 out262k99.5% upGMI Cloudfp8$0.85 in$3.60 out262k94.2% upAtlasCloudint4$0.95 in$4.00 out262k96.6% upBasetenfp4$0.95 in$4.00 out262k76.6% upCloudflare Workers AI$0.95 in$4.00 out262k100% upDigitalOcean$0.95 in$4.00 out262k99.3% upMoonshot AIint4$0.95 in$4.00 out262k99.9% upPhala$1.09 in$4.60 out262k99% upFireworksdegraded$0.95 in$4.00 out262k0% up
Mistral Large 21 host
Per million tokens. Prices and uptime via OpenRouter, checked 9 Sep 2026.
Writing DNA
Style Comparison
Similarity
34%
Mistral Large 2 uses 23.6x more emoji
Kimi K2.6
Mistral Large 2
57%Vocabulary43%
16wSentence Length21w
0.37Hedging0.39
4.3Bold14.4
2.0Lists7.4
0.03Emoji0.80
0.53Headings1.41
0.16Transitions0.01
Based on 27 + 10 text responses
Research
What we learned reading every model
FAQ
Common questions
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