Laguna S 2.1 vs MiniMax M3
Laguna S 2.1 and MiniMax M3 are closely matched at 41.4 and 41.9 on the LLM Stats Score. Laguna S 2.1 is 4.2x cheaper per token.
Poolside · MiniMax · Updated for 2026
Which is better?
Laguna S 2.1 and MiniMax M3 are closely matched on the overall LLM Stats Score at 41.4 and 41.9.
In the 3 individual benchmarks reported for both models, Laguna S 2.1 wins 3; this is a narrower head-to-head signal than the composite indexes.
On price, Laguna S 2.1 is roughly 4.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Laguna S 2.1 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 Laguna S 2.1
- you value its reported benchmark strengths — it wins 3 of 3 exact shared results
- cost matters — it's about 4.2x 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 Jul 2026
Choose MiniMax M3
- you want predictable pricing at $0.30/M input and $1.20/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
6 reported for Laguna S 2.1 · 35 for MiniMax M3
Laguna S 2.1 outperforms in 3 benchmarks (SWE Atlas - Codebase QnA, SWE-Bench Pro, Terminal-Bench 2.1), while MiniMax M3 is better at 0 benchmarks.
Laguna S 2.1 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, Laguna S 2.1 ($0.10/1M tokens) is 3.0x cheaper than MiniMax M3 ($0.30/1M tokens).
For output processing, Laguna S 2.1 ($0.20/1M tokens) is 6.0x cheaper than MiniMax M3 ($1.20/1M tokens).
In conclusion, MiniMax M3 is more expensive than Laguna S 2.1.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
MiniMax M3 has 310.0B more parameters than Laguna S 2.1, making it 262.7% larger.
Context Window
Maximum input and output token capacity
Laguna S 2.1 accepts 1,048,576 input tokens compared to MiniMax M3's 512,000 tokens. Only MiniMax M3 specifies output context (131,072 tokens).
Input capabilities
Documented input modalities across available providers
MiniMax M3 supports multimodal inputs, whereas Laguna S 2.1 does not.
MiniMax M3 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Laguna S 2.1
MiniMax M3
License
Usage and distribution terms
Laguna S 2.1 is licensed under OpenMDW License v1.1, while MiniMax M3 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
OpenMDW License v1.1
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Laguna S 2.1 was released on 2026-07-21, while MiniMax M3 was released on 2026-06-01.
Laguna S 2.1 is 2 months newer than MiniMax M3.
Jul 21, 2026
1 months ago
1mo newerJun 1, 2026
2 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
Laguna S 2.1 is available from Poolside. MiniMax M3 is available from Fireworks, MiniMax, Novita, Together.
Laguna S 2.1
MiniMax M3
Outputs Comparison
Judge for yourself.
Run your own prompts against Laguna S 2.1 and MiniMax M3 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Laguna S 2.1 vs MiniMax M3.