Llama 3.2 90B Instruct vs MiMo-V2.6-Flash
MiMo-V2.6-Flash leads the LLM Stats Score 45.6 to 5.2. MiMo-V2.6-Flash is 2.1x cheaper per token.
Meta · Xiaomi · Updated for 2026
Which is better?
MiMo-V2.6-Flash leads the overall LLM Stats Score 45.6 to 5.2, ranking #29 overall.
On price, MiMo-V2.6-Flash is roughly 2.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
MiMo-V2.6-Flash 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 Llama 3.2 90B Instruct
- you want predictable pricing at $0.35/M input and $0.40/M output
Choose MiMo-V2.6-Flash
- overall performance matters — it scores 45.6 and ranks #29 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 2.1x 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.
Individual benchmarks
13 reported for Llama 3.2 90B Instruct · 16 for MiMo-V2.6-Flash
Llama 3.2 90B Instruct and MiMo-V2.6-Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, Llama 3.2 90B Instruct ($0.35/1M tokens) is 2.5x more expensive than MiMo-V2.6-Flash ($0.14/1M tokens).
For output processing, Llama 3.2 90B Instruct ($0.40/1M tokens) is 1.4x more expensive than MiMo-V2.6-Flash ($0.28/1M tokens).
In conclusion, Llama 3.2 90B Instruct is more expensive than MiMo-V2.6-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiMo-V2.6-Flash has 219.0B more parameters than Llama 3.2 90B Instruct, making it 243.3% larger.
Context Window
Maximum input and output token capacity
MiMo-V2.6-Flash accepts 1,048,576 input tokens compared to Llama 3.2 90B Instruct's 128,000 tokens. Only Llama 3.2 90B Instruct specifies output context (128,000 tokens).
Input capabilities
Documented input modalities across available providers
Both Llama 3.2 90B Instruct and MiMo-V2.6-Flash support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Llama 3.2 90B Instruct
MiMo-V2.6-Flash
License
Usage and distribution terms
Llama 3.2 90B Instruct is licensed under Llama 3.2, while MiMo-V2.6-Flash uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Llama 3.2
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Llama 3.2 90B Instruct was released on 2024-09-25, while MiMo-V2.6-Flash was released on 2026-09-22.
MiMo-V2.6-Flash is 24 months newer than Llama 3.2 90B Instruct.
Sep 25, 2024
2.0 years ago
Sep 22, 2026
0 days ago
2.0yr newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
Llama 3.2 90B Instruct is available from DeepInfra, Bedrock, Fireworks, Together, Hyperbolic. MiMo-V2.6-Flash is available from Xiaomi.
Llama 3.2 90B Instruct
MiMo-V2.6-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against Llama 3.2 90B Instruct and MiMo-V2.6-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about Llama 3.2 90B Instruct vs MiMo-V2.6-Flash.