Beam 1.0 vs Mistral Medium 3.5
Beam 1.0 leads the LLM Stats Score 44.6 to 26.4. 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 26.4, 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, 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 and agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
- 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 Mistral Medium 3.5
- you want predictable pricing at $1.50/M input and $7.50/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 · 11 for Mistral Medium 3.5
Beam 1.0 outperforms in 4 benchmarks (BrowseComp, IFBench, SWE-Bench Verified, Tau3 Banking), while Mistral Medium 3.5 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 3.8x cheaper than Mistral Medium 3.5 ($1.50/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 6.3x cheaper than Mistral Medium 3.5 ($7.50/1M tokens).
In conclusion, Mistral Medium 3.5 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 373.3B more parameters than Mistral Medium 3.5, making it 291.6% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to Mistral Medium 3.5's 256,000 tokens. Mistral Medium 3.5 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 Medium 3.5 supports multimodal inputs, whereas Beam 1.0 does not.
Mistral Medium 3.5 can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
Mistral Medium 3.5
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while Mistral Medium 3.5 uses Modified MIT License.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Modified MIT License
Open weights
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while Mistral Medium 3.5 was released on 2026-04-29.
Beam 1.0 is 6 months newer than Mistral Medium 3.5.
Oct 16, 2026
1 weeks ago
5mo newerApr 29, 2026
6 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while Mistral Medium 3.5'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 Mistral Medium 3.5's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. Mistral Medium 3.5 is available from Mistral AI.
Beam 1.0
Mistral Medium 3.5
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and Mistral Medium 3.5 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs Mistral Medium 3.5.