Inkling vs MiMo-V2.6-Pro
MiMo-V2.6-Pro leads the LLM Stats Score 49.9 to 37.2. MiMo-V2.6-Pro is 3.2x cheaper per token.
Thinking Machines Lab · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Pro leads the overall LLM Stats Score 49.9 to 37.2, ranking #20 overall.
In the 1 individual benchmarks reported for both models, MiMo-V2.6-Pro wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, MiMo-V2.6-Pro is roughly 3.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.6-Pro 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 Inkling
- you want predictable pricing at $0.95/M input and $4.05/M output
Choose MiMo-V2.6-Pro
- overall performance matters — it scores 49.9 and ranks #20 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 3.2x cheaper per token
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Sep 2026
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 · 18 for MiMo-V2.6-Pro
Inkling outperforms in 0 benchmarks, while MiMo-V2.6-Pro is better at 1 benchmark (Terminal-Bench 2.1).
MiMo-V2.6-Pro 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 ($0.95/1M tokens) is 2.2x more expensive than MiMo-V2.6-Pro ($0.43/1M tokens).
For output processing, Inkling ($4.05/1M tokens) is 4.7x more expensive than MiMo-V2.6-Pro ($0.87/1M tokens).
In conclusion, Inkling is more expensive than MiMo-V2.6-Pro.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Pro has 45.0B more parameters than Inkling, making it 4.6% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Pro accepts 1,048,576 input tokens compared to Inkling's 524,288 tokens. Only Inkling specifies output context (524,288 tokens).
Input capabilities
Documented input modalities across available providers
Both Inkling and MiMo-V2.6-Pro support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Inkling
MiMo-V2.6-Pro
License
Usage and distribution terms
Inkling is licensed under Apache 2.0, while MiMo-V2.6-Pro 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 MiMo-V2.6-Pro was released on 2026-09-22.
MiMo-V2.6-Pro is 2 months newer than Inkling.
Jul 21, 2026
2 months ago
Sep 22, 2026
1 weeks ago
2mo newerKnowledge 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. MiMo-V2.6-Pro is available from Xiaomi.
Inkling
MiMo-V2.6-Pro
Outputs Comparison
Judge for yourself.
Run your own prompts against Inkling and MiMo-V2.6-Pro side-by-side, then vote on the output you prefer.
FAQ
Common questions about Inkling vs MiMo-V2.6-Pro.