Beam 1.0 vs MiniMax M2.5
Beam 1.0 and MiniMax M2.5 are closely matched at 44.6 and 36.6 on the LLM Stats Score. MiniMax M2.5 is 1.1x cheaper per token.
Reflection AI · MiniMax · Updated for 2026
Which is better?
Beam 1.0 and MiniMax M2.5 are closely matched on the overall LLM Stats Score at 44.6 and 36.6.
In the 3 individual benchmarks reported for both models, Beam 1.0 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, MiniMax M2.5 is roughly 1.1x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Beam 1.0 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 Beam 1.0
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Oct 2026
Choose MiniMax M2.5
- cost matters — it's about 1.1x cheaper per token
At a glance
The differences that matter most.
Individual benchmarks
22 reported for Beam 1.0 · 8 for MiniMax M2.5
Beam 1.0 outperforms in 3 benchmarks (BrowseComp, SWE-Bench Pro, SWE-Bench Verified), while MiniMax M2.5 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 1.3x more expensive than MiniMax M2.5 ($0.30/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) costs the same as MiniMax M2.5 ($1.20/1M tokens).
In conclusion, Beam 1.0 is more expensive than MiniMax M2.5.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Beam 1.0 has 271.3B more parameters than MiniMax M2.5, making it 118.0% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to MiniMax M2.5's 1,000,000 tokens. MiniMax M2.5 can generate longer responses up to 1,000,000 tokens, while Beam 1.0 is limited to 131,072 tokens.
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while MiniMax M2.5 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 MiniMax M2.5 was released on 2026-02-12.
Beam 1.0 is 8 months newer than MiniMax M2.5.
Oct 16, 2026
3 days ago
8mo newerFeb 12, 2026
8 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while MiniMax M2.5'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 MiniMax M2.5's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. MiniMax M2.5 is available from MiniMax.
Beam 1.0
MiniMax M2.5
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and MiniMax M2.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs MiniMax M2.5.