Beam 1.0 vs Pixtral Large
Beam 1.0 leads the LLM Stats Score 44.6 to 11.3. Beam 1.0 is 5.0x cheaper per token.
Reflection AI · Mistral AI · Updated for 2026
Which is better?
Beam 1.0 leads the overall LLM Stats Score 44.6 to 11.3, ranking #46 overall.
On price, Beam 1.0 is roughly 5.0x 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
- overall performance matters — it scores 44.6 and ranks #46 on LLM Stats
- your work emphasizes reasoning — it leads those capability indexes
- cost matters — it's about 5.0x 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
Choose Pixtral Large
- you want predictable pricing at $2.00/M input and $6.00/M output
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 · 7 for Pixtral Large
Beam 1.0 and Pixtral Largedon'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, Beam 1.0 ($0.40/1M tokens) is 5.0x cheaper than Pixtral Large ($2.00/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 5.0x cheaper than Pixtral Large ($6.00/1M tokens).
In conclusion, Pixtral Large is more expensive than Beam 1.0.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Beam 1.0 has 377.3B more parameters than Pixtral Large, making it 304.3% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to Pixtral Large's 128,000 tokens. Beam 1.0 can generate longer responses up to 131,072 tokens, while Pixtral Large is limited to 128,000 tokens.
Input capabilities
Documented input modalities across available providers
Pixtral Large supports multimodal inputs, whereas Beam 1.0 does not.
Pixtral Large can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
Pixtral Large
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while Pixtral Large uses Mistral Research License (MRL) for research; Mistral Commercial License for commercial use.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Mistral Research License (MRL) for research; Mistral Commercial License for commercial use
Open weights
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while Pixtral Large was released on 2024-11-18.
Beam 1.0 is 23 months newer than Pixtral Large.
Oct 16, 2026
3 days ago
1.9yr newerNov 18, 2024
1.9 years ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while Pixtral Large'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 Pixtral Large's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. Pixtral Large is available from Mistral AI.
Beam 1.0
Pixtral Large
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and Pixtral Large side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs Pixtral Large.