Kimi K2.6 vs Laguna S 2.1
Kimi K2.6 and Laguna S 2.1 are closely matched at 43.7 and 40.9 on the LLM Stats Score. Laguna S 2.1 is 11.5x cheaper per token.
Moonshot AI · Poolside · Updated for 2026
Which is better?
Kimi K2.6 and Laguna S 2.1 are closely matched on the overall LLM Stats Score at 43.7 and 40.9.
In the 3 individual benchmarks reported for both models, Laguna S 2.1 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Laguna S 2.1 is roughly 11.5x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Laguna S 2.1 also accepts a larger context window (1,048,576 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
- you want predictable pricing at $0.75/M input and $3.50/M output
Choose Laguna S 2.1
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- cost matters — it's about 11.5x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Jul 2026
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 · 6 for Laguna S 2.1
Kimi K2.6 outperforms in 1 benchmarks (Toolathlon), while Laguna S 2.1 is better at 2 benchmarks (SWE-bench Multilingual, SWE-Bench Pro).
Laguna S 2.1 shows notably better performance in the majority of 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 7.5x more expensive than Laguna S 2.1 ($0.10/1M tokens).
For output processing, Kimi K2.6 ($3.50/1M tokens) is 17.5x more expensive than Laguna S 2.1 ($0.20/1M tokens).
In conclusion, Kimi K2.6 is more expensive than Laguna S 2.1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.6 has 882.0B more parameters than Laguna S 2.1, making it 747.5% larger.
Context Window
Maximum input and output token capacity
Laguna S 2.1 accepts 1,048,576 input tokens compared to Kimi K2.6's 262,144 tokens. Only Kimi K2.6 specifies output context (131,072 tokens).
Input capabilities
Documented input modalities across available providers
Kimi K2.6 supports multimodal inputs, whereas Laguna S 2.1 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
Laguna S 2.1
License
Usage and distribution terms
Kimi K2.6 is licensed under Modified MIT License, while Laguna S 2.1 uses OpenMDW License v1.1.
License differences may affect how you can use these models in commercial or open-source projects.
Modified MIT License
Open weights
OpenMDW License v1.1
Open weights
Release Timeline
When each model was launched
Kimi K2.6 was released on 2026-04-20, while Laguna S 2.1 was released on 2026-07-21.
Laguna S 2.1 is 3 months newer than Kimi K2.6.
Apr 20, 2026
5 months ago
Jul 21, 2026
2 months ago
3mo newerKnowledge 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. Laguna S 2.1 is available from Poolside.
Kimi K2.6
Laguna S 2.1
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.6 and Laguna S 2.1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.6 vs Laguna S 2.1.