Kimi K2 Instruct vs Mercury 3
Mercury 3 leads the LLM Stats Score 36.1 to 21.4. Mercury 3 is 3.1x cheaper per token.
Moonshot AI · Inception · Updated for 2026
Which is better?
Mercury 3 leads the overall LLM Stats Score 36.1 to 21.4, ranking #98 overall.
In the 2 individual benchmarks reported for both models, Mercury 3 wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Mercury 3 is roughly 3.1x 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 Instruct
- you need open weights you can self-host or fine-tune
Choose Mercury 3
- overall performance matters — it scores 36.1 and ranks #98 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 2 of 2 exact shared results
- cost matters — it's about 3.1x 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
38 reported for Kimi K2 Instruct · 12 for Mercury 3
Kimi K2 Instruct outperforms in 0 benchmarks, while Mercury 3 is better at 2 benchmarks (GPQA, Multi-Challenge).
Mercury 3 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 Instruct ($0.50/1M tokens) is 5.0x more expensive than Mercury 3 ($0.10/1M tokens).
For output processing, Kimi K2 Instruct ($0.50/1M tokens) is 1.4x more expensive than Mercury 3 ($0.35/1M tokens).
In conclusion, Kimi K2 Instruct 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 Instruct's 200,000 tokens. Kimi K2 Instruct can generate longer responses up to 200,000 tokens, while Mercury 3 is limited to 131,072 tokens.
License
Usage and distribution terms
Kimi K2 Instruct is licensed under MIT, while Mercury 3 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Kimi K2 Instruct was released on 2025-07-11, while Mercury 3 was released on 2026-10-19.
Mercury 3 is 16 months newer than Kimi K2 Instruct.
Jul 11, 2025
1.3 years ago
Oct 19, 2026
0 days ago
1.3yr 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 Instruct is available from Fireworks, Novita. Mercury 3 is available from Inception.
Kimi K2 Instruct
Mercury 3
Outputs Comparison
Judge for yourself.
Run your own prompts against Kimi K2 Instruct and Mercury 3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Kimi K2 Instruct vs Mercury 3.