Beam 1.0 vs DeepSeek-R1
Comparing Beam 1.0 and DeepSeek-R1 across benchmarks, pricing, and capabilities.
Reflection AI · DeepSeek · Updated for 2026
Which is better?
Beam 1.0 and DeepSeek-R1 trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.
On price, Beam 1.0 is roughly 1.6x 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 1.6x 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 DeepSeek-R1
- you want predictable pricing at $0.55/M input and $2.19/M output
At a glance
The differences that matter most.
Individual benchmarks
22 reported for Beam 1.0 · 0 for DeepSeek-R1
Beam 1.0 and DeepSeek-R1don'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 1.4x cheaper than DeepSeek-R1 ($0.55/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 1.8x cheaper than DeepSeek-R1 ($2.19/1M tokens).
In conclusion, DeepSeek-R1 is more expensive than Beam 1.0.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-R1 has 169.7B more parameters than Beam 1.0, making it 33.9% larger.
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to DeepSeek-R1's 131,072 tokens. Both models can generate responses up to 131,072 tokens.
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while DeepSeek-R1 uses MIT.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
MIT
Open weights
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while DeepSeek-R1 was released on 2025-01-20.
Beam 1.0 is 21 months newer than DeepSeek-R1.
Oct 16, 2026
4 days ago
1.7yr newerJan 20, 2025
1.7 years ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while DeepSeek-R1'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 DeepSeek-R1's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. DeepSeek-R1 is available from DeepSeek, DeepInfra, Together, Fireworks.
Beam 1.0
DeepSeek-R1
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and DeepSeek-R1 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs DeepSeek-R1.