Kimi K2.6 vs Muse Spark Open
Kimi K2.6 and Muse Spark Open are closely matched at 43.4 and 44.9 on the LLM Stats Score. Kimi K2.6 is 1.4x cheaper per token.
Moonshot AI · Meta · Updated for 2026
Which is better?
Kimi K2.6 and Muse Spark Open are closely matched on the overall LLM Stats Score at 43.4 and 44.9.
In the 4 individual benchmarks reported for both models, Muse Spark Open wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Kimi K2.6 is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Muse Spark Open 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
- cost matters — it's about 1.4x cheaper per token
Choose Muse Spark Open
- you value its reported benchmark strengths — it wins 3 of 4 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 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 · 17 for Muse Spark Open
Kimi K2.6 outperforms in 1 benchmarks (GPQA), while Muse Spark Open is better at 3 benchmarks (BabyVision, OSWorld-Verified, SciCode).
Muse Spark Open 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 1.7x cheaper than Muse Spark Open ($1.25/1M tokens).
For output processing, Kimi K2.6 ($3.50/1M tokens) is 1.2x cheaper than Muse Spark Open ($4.25/1M tokens).
In conclusion, Muse Spark Open is more expensive than Kimi K2.6.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.6 has 184.0B more parameters than Muse Spark Open, making it 22.5% larger.
Context Window
Maximum input and output token capacity
Muse Spark Open accepts 1,048,576 input tokens compared to Kimi K2.6's 262,144 tokens. Muse Spark Open can generate longer responses up to 1,048,576 tokens, while Kimi K2.6 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both Kimi K2.6 and Muse Spark Open support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Kimi K2.6
Muse Spark Open
License
Usage and distribution terms
Kimi K2.6 is licensed under Modified MIT License, while Muse Spark Open uses Muse Model License.
License differences may affect how you can use these models in commercial or open-source projects.
Modified MIT License
Open weights
Muse Model License
Open weights
Release Timeline
When each model was launched
Kimi K2.6 was released on 2026-04-20, while Muse Spark Open was released on 2026-10-15.
Muse Spark Open is 6 months newer than Kimi K2.6.
Apr 20, 2026
6 months ago
Oct 15, 2026
4 days ago
5mo 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. Muse Spark Open is available from Together.
Kimi K2.6
Muse Spark Open
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.6 and Muse Spark Open side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.6 vs Muse Spark Open.