Kimi K2.6 vs MiniMax M2.7
Kimi K2.6 leads the LLM Stats Score 43.7 to 35.5. MiniMax M2.7 is 2.7x cheaper per token.
Moonshot AI · MiniMax · Updated for 2026
Which is better?
Kimi K2.6 leads the overall LLM Stats Score 43.7 to 35.5, ranking #38 overall.
In the 5 individual benchmarks reported for both models, Kimi K2.6 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, MiniMax M2.7 is roughly 2.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Kimi K2.6 also accepts a larger context window (262,144 input tokens), making it the stronger choice for long documents and large codebases.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Kimi K2.6
- overall performance matters — it scores 43.7 and ranks #38 on LLM Stats
- your work emphasizes reasoning and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 5 of 5 exact shared results
- you process long inputs — it offers a 262,144 token context window
- you want the most recent training data — it shipped Apr 2026
Choose MiniMax M2.7
- cost matters — it's about 2.7x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
28 reported for Kimi K2.6 · 11 for MiniMax M2.7
Kimi K2.6 outperforms in 5 benchmarks (Finance Agent v2, SWE-bench Multilingual, SWE-Bench Pro, Terminal-Bench 2.0, Toolathlon), while MiniMax M2.7 is better at 0 benchmarks.
Kimi K2.6 significantly outperforms across most benchmarks.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Kimi K2.6 ($0.75/1M tokens) is 2.5x more expensive than MiniMax M2.7 ($0.30/1M tokens).
For output processing, Kimi K2.6 ($3.50/1M tokens) is 2.9x more expensive than MiniMax M2.7 ($1.20/1M tokens).
In conclusion, Kimi K2.6 is more expensive than MiniMax M2.7.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Kimi K2.6 accepts 262,144 input tokens compared to MiniMax M2.7's 196,608 tokens. MiniMax M2.7 can generate longer responses up to 196,608 tokens, while Kimi K2.6 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K2.6 supports multimodal inputs, whereas MiniMax M2.7 does not.
Kimi K2.6 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Kimi K2.6
MiniMax M2.7
License
Usage and distribution terms
Kimi K2.6 is licensed under Modified MIT License, while MiniMax M2.7 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Modified MIT License
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Kimi K2.6 was released on 2026-04-20, while MiniMax M2.7 was released on 2026-03-18.
Kimi K2.6 is 1 month newer than MiniMax M2.7.
Apr 20, 2026
5 months ago
1mo newerMar 18, 2026
6 months ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Kimi K2.6 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together. MiniMax M2.7 is available from Fireworks, MiniMax, Novita, DeepInfra.
Kimi K2.6
MiniMax M2.7
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.6 and MiniMax M2.7 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.6 vs MiniMax M2.7.