Beam 1.0 vs Mistral Large 4
Beam 1.0 and Mistral Large 4 are closely matched at 44.6 and 45.8 on the LLM Stats Score. Beam 1.0 is 1.7x cheaper per token.
Reflection AI · Mistral AI · Updated for 2026
Which is better?
Beam 1.0 and Mistral Large 4 are closely matched on the overall LLM Stats Score at 44.6 and 45.8.
In the 4 individual benchmarks reported for both models, Mistral Large 4 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Beam 1.0 is roughly 1.7x 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
- cost matters — it's about 1.7x 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 Oct 2026
- you need open weights you can self-host or fine-tune
Choose Mistral Large 4
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
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 · 18 for Mistral Large 4
Beam 1.0 outperforms in 0 benchmarks, while Mistral Large 4 is better at 4 benchmarks (AutomationBench, DeepSWE 1.1, SciCode, SWE Atlas - Codebase QnA).
Mistral Large 4 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 1.7x cheaper than Mistral Large 4 ($0.68/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 1.7x cheaper than Mistral Large 4 ($2.09/1M tokens).
In conclusion, Mistral Large 4 is more expensive than Beam 1.0.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Mistral Large 4 has 548.7B more parameters than Beam 1.0, making it 109.5% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to Mistral Large 4's 1,000,000 tokens. Only Beam 1.0 specifies output context (131,072 tokens).
Input capabilities
Documented input modalities across available providers
Mistral Large 4 supports multimodal inputs, whereas Beam 1.0 does not.
Mistral Large 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
Mistral Large 4
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while Mistral Large 4 uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while Mistral Large 4 was released on 2026-10-06.
Beam 1.0 is 0 month newer than Mistral Large 4.
Oct 16, 2026
3 days ago
1w newerOct 6, 2026
1 weeks ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while Mistral Large 4'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 Mistral Large 4's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. Mistral Large 4 is available from Mistral AI.
Beam 1.0
Mistral Large 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and Mistral Large 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs Mistral Large 4.