Beam 1.0 vs MiMo-V2.5
Beam 1.0 leads the LLM Stats Score 44.6 to 35.8. MiMo-V2.5 is 2.9x cheaper per token.
Reflection AI · Xiaomi · Updated for 2026
Which is better?
Beam 1.0 leads the overall LLM Stats Score 44.6 to 35.8, ranking #46 overall.
In the 1 individual benchmarks reported for both models, Beam 1.0 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, MiMo-V2.5 is roughly 2.9x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Beam 1.0
- overall performance matters — it scores 44.6 and ranks #46 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- you want the most recent training data — it shipped Oct 2026
Choose MiMo-V2.5
- cost matters — it's about 2.9x cheaper per token
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
22 reported for Beam 1.0 · 14 for MiMo-V2.5
Beam 1.0 outperforms in 1 benchmarks (SWE-Bench Pro), while MiMo-V2.5 is better at 0 benchmarks.
Beam 1.0 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, Beam 1.0 ($0.40/1M tokens) is 2.4x more expensive than MiMo-V2.5 ($0.17/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 3.6x more expensive than MiMo-V2.5 ($0.34/1M tokens).
In conclusion, Beam 1.0 is more expensive than MiMo-V2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Beam 1.0 has 190.5B more parameters than MiMo-V2.5, making it 61.3% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. Both models can generate responses up to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
MiMo-V2.5 supports multimodal inputs, whereas Beam 1.0 does not.
MiMo-V2.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
MiMo-V2.5
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while MiMo-V2.5 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
Beam 1.0 was released on 2026-10-16, while MiMo-V2.5 was released on 2026-04-22.
Beam 1.0 is 6 months newer than MiMo-V2.5.
Oct 16, 2026
3 days ago
5mo newerApr 22, 2026
6 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while MiMo-V2.5's cutoff date is not specified.
We can confirm Beam 1.0's training data extends to 2026-06-30, but cannot make a direct comparison without MiMo-V2.5's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. MiMo-V2.5 is available from Novita, DeepInfra.
Beam 1.0
MiMo-V2.5
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and MiMo-V2.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs MiMo-V2.5.