Kimi K2.6 vs Mercury 3
Kimi K2.6 leads the LLM Stats Score 43.4 to 36.1. Mercury 3 is 8.8x cheaper per token.
Moonshot AI · Inception · Updated for 2026
Which is better?
Kimi K2.6 leads the overall LLM Stats Score 43.4 to 36.1, ranking #54 overall.
In the 2 individual benchmarks reported for both models, Kimi K2.6 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Mercury 3 is roughly 8.8x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Mercury 3 also accepts a larger context window (1,000,000 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 43.4 and ranks #54 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- you need open weights you can self-host or fine-tune
Choose Mercury 3
- cost matters — it's about 8.8x cheaper per token
- you process long inputs — it offers a 1,000,000 token context window
- you want the most recent training data — it shipped Oct 2026
At a glance
The differences that matter most.
Individual benchmarks
28 reported for Kimi K2.6 · 12 for Mercury 3
Kimi K2.6 outperforms in 2 benchmarks (DeepSearchQA, GPQA), while Mercury 3 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 7.5x more expensive than Mercury 3 ($0.10/1M tokens).
For output processing, Kimi K2.6 ($3.50/1M tokens) is 10.0x more expensive than Mercury 3 ($0.35/1M tokens).
In conclusion, Kimi K2.6 is more expensive than Mercury 3.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Mercury 3 accepts 1,000,000 input tokens compared to Kimi K2.6's 262,144 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Kimi K2.6 supports multimodal inputs, whereas Mercury 3 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
Mercury 3
License
Usage and distribution terms
Kimi K2.6 is licensed under Modified MIT License, while Mercury 3 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Modified MIT License
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Kimi K2.6 was released on 2026-04-20, while Mercury 3 was released on 2026-10-19.
Mercury 3 is 6 months newer than Kimi K2.6.
Apr 20, 2026
6 months ago
Oct 19, 2026
1 days ago
6mo 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. Mercury 3 is available from Inception.
Kimi K2.6
Mercury 3
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2.6 and Mercury 3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2.6 vs Mercury 3.