Beam 1.0 vs Mistral Small 4
Beam 1.0 leads the LLM Stats Score 44.6 to 18.3. Mistral Small 4 is 2.3x 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 18.3, ranking #46 overall.
In the 4 individual benchmarks reported for both models, Beam 1.0 wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, Mistral Small 4 is roughly 2.3x 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 and coding — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 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 Mistral Small 4
- cost matters — it's about 2.3x 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 · 9 for Mistral Small 4
Beam 1.0 outperforms in 4 benchmarks (AA-LCR, GPQA, IFBench, LiveCodeBench), while Mistral Small 4 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 2.7x more expensive than Mistral Small 4 ($0.15/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 2.0x more expensive than Mistral Small 4 ($0.60/1M tokens).
In conclusion, Beam 1.0 is more expensive than Mistral Small 4.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Beam 1.0 has 382.3B more parameters than Mistral Small 4, making it 321.3% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to Mistral Small 4's 256,000 tokens. Mistral Small 4 can generate longer responses up to 256,000 tokens, while Beam 1.0 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Mistral Small 4 supports multimodal inputs, whereas Beam 1.0 does not.
Mistral Small 4 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
Mistral Small 4
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while Mistral Small 4 was released on 2026-03-16.
Beam 1.0 is 7 months newer than Mistral Small 4.
Oct 16, 2026
6 days ago
7mo newerMar 16, 2026
7 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while Mistral Small 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 Small 4's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. Mistral Small 4 is available from Mistral AI.
Beam 1.0
Mistral Small 4
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and Mistral Small 4 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs Mistral Small 4.