Inkling-Small vs Kimi K2.6
Kimi K2.6 leads the LLM Stats Score 43.6 to 38.3. Inkling-Small is 2.7x cheaper per token.
Thinking Machines Lab · Moonshot AI · Updated for 2026
Which is better?
Kimi K2.6 leads the overall LLM Stats Score 43.6 to 38.3, ranking #46 overall.
In the 10 individual benchmarks reported for both models, Kimi K2.6 wins 9; this is a narrower head-to-head signal than the composite indexes.
On price, Inkling-Small 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 Inkling-Small
- cost matters — it's about 2.7x cheaper per token
- you want the most recent training data — it shipped Jul 2026
Choose Kimi K2.6
- overall performance matters — it scores 43.6 and ranks #46 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 9 of 10 exact shared results
- you process long inputs — it offers a 262,144 token context window
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
24 reported for Inkling-Small · 28 for Kimi K2.6
Inkling-Small outperforms in 1 benchmarks (Toolathlon), while Kimi K2.6 is better at 8 benchmarks (AIME 2026, BrowseComp, CharXiv-R, GPQA, Humanity's Last Exam, MMMU-Pro, SciCode, SWE-Bench Pro).
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, Inkling-Small ($0.30/1M tokens) is 2.5x cheaper than Kimi K2.6 ($0.75/1M tokens).
For output processing, Inkling-Small ($1.20/1M tokens) is 2.9x cheaper than Kimi K2.6 ($3.50/1M tokens).
In conclusion, Kimi K2.6 is more expensive than Inkling-Small.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.6 has 724.0B more parameters than Inkling-Small, making it 262.3% larger.
Context Window
Maximum input and output token capacity
Kimi K2.6 accepts 262,144 input tokens compared to Inkling-Small's 256,000 tokens. Inkling-Small can generate longer responses up to 256,000 tokens, while Kimi K2.6 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both Inkling-Small and Kimi K2.6 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Inkling-Small
Kimi K2.6
License
Usage and distribution terms
Inkling-Small is licensed under Apache 2.0, while Kimi K2.6 uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
Inkling-Small was released on 2026-07-30, while Kimi K2.6 was released on 2026-04-20.
Inkling-Small is 3 months newer than Kimi K2.6.
Jul 30, 2026
2 months ago
3mo newerApr 20, 2026
5 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
Inkling-Small is available from Thinking Machines Lab, DeepInfra. Kimi K2.6 is available from DeepInfra, Fireworks, Moonshot AI, Novita, Together.
Inkling-Small
Kimi K2.6
Outputs Comparison
Judge for yourself.
Run your own prompts against Inkling-Small and Kimi K2.6 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Inkling-Small vs Kimi K2.6.