Inkling vs Kimi K2.5
Inkling and Kimi K2.5 are closely matched at 37.5 and 38.9 on the LLM Stats Score. Kimi K2.5 is 1.4x cheaper per token.
Thinking Machines Lab · Moonshot AI · Updated for 2026
Which is better?
Inkling and Kimi K2.5 are closely matched on the overall LLM Stats Score at 37.5 and 38.9.
In the 3 individual benchmarks reported for both models, Inkling wins 2; this is a narrower head-to-head signal than the composite indexes.
On price, Kimi K2.5 is roughly 1.4x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Inkling also accepts a larger context window (524,288 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
- you value its reported benchmark strengths — it wins 2 of 3 exact shared results
- you process long inputs — it offers a 524,288 token context window
- you want the most recent training data — it shipped Jul 2026
Choose Kimi K2.5
- cost matters — it's about 1.4x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
16 reported for Inkling · 40 for Kimi K2.5
Inkling outperforms in 2 benchmarks (CharXiv-R, SWE-Bench Verified), while Kimi K2.5 is better at 1 benchmark (MMMU-Pro).
Inkling 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, Inkling ($0.95/1M tokens) is 1.6x more expensive than Kimi K2.5 ($0.60/1M tokens).
For output processing, Inkling ($4.05/1M tokens) is 1.3x more expensive than Kimi K2.5 ($3.00/1M tokens).
In conclusion, Inkling is more expensive than Kimi K2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Kimi K2.5 has 25.0B more parameters than Inkling, making it 2.6% larger.
Context Window
Maximum input and output token capacity
Inkling accepts 524,288 input tokens compared to Kimi K2.5's 262,100 tokens. Inkling can generate longer responses up to 524,288 tokens, while Kimi K2.5 is limited to 262,100 tokens.
Input capabilities
Documented input modalities across available providers
Both Inkling and Kimi K2.5 support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Inkling
Kimi K2.5
License
Usage and distribution terms
Inkling is licensed under Apache 2.0, while Kimi K2.5 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Inkling was released on 2026-07-21, while Kimi K2.5 was released on 2026-01-27.
Inkling is 6 months newer than Kimi K2.5.
Jul 21, 2026
1 months ago
5mo newerJan 27, 2026
7 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 is available from DeepInfra. Kimi K2.5 is available from Fireworks, Moonshot AI.
Inkling
Kimi K2.5
Outputs Comparison
Judge for yourself.
Run your own prompts against Inkling and Kimi K2.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Inkling vs Kimi K2.5.