Beam 1.0 vs DeepSeek-V4-Pro-Max
Beam 1.0 and DeepSeek-V4-Pro-Max are closely matched at 44.6 and 42.8 on the LLM Stats Score. Beam 1.0 is 2.7x cheaper per token.
Reflection AI · DeepSeek · Updated for 2026
Which is better?
Beam 1.0 and DeepSeek-V4-Pro-Max are closely matched on the overall LLM Stats Score at 44.6 and 42.8.
In the 8 individual benchmarks reported for both models, Beam 1.0 wins 5; this is a narrower head-to-head signal than the composite indexes.
On price, Beam 1.0 is roughly 2.7x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.
Choose Beam 1.0
- you value its reported benchmark strengths — it wins 5 of 8 exact shared results
- cost matters — it's about 2.7x cheaper per token
- you want the most recent training data — it shipped Oct 2026
Choose DeepSeek-V4-Pro-Max
- you want predictable pricing at $1.30/M input and $2.60/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 · 22 for DeepSeek-V4-Pro-Max
Beam 1.0 outperforms in 5 benchmarks (GPQA, MCP Atlas, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified), while DeepSeek-V4-Pro-Max is better at 3 benchmarks (BrowseComp, Humanity's Last Exam, LiveCodeBench).
Beam 1.0 shows notably better performance in the majority of 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 3.3x cheaper than DeepSeek-V4-Pro-Max ($1.30/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 2.2x cheaper than DeepSeek-V4-Pro-Max ($2.60/1M tokens).
In conclusion, DeepSeek-V4-Pro-Max is more expensive than Beam 1.0.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
DeepSeek-V4-Pro-Max has 1098.7B more parameters than Beam 1.0, making it 219.2% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. DeepSeek-V4-Pro-Max can generate longer responses up to 1,048,576 tokens, while Beam 1.0 is limited to 131,072 tokens.
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while DeepSeek-V4-Pro-Max 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-V4-Pro-Max was released on 2026-04-23.
Beam 1.0 is 6 months newer than DeepSeek-V4-Pro-Max.
Oct 16, 2026
6 days ago
5mo newerApr 23, 2026
6 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while DeepSeek-V4-Pro-Max'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-V4-Pro-Max's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. DeepSeek-V4-Pro-Max is available from DeepInfra, Novita, DeepSeek, Fireworks, Together.
Beam 1.0
DeepSeek-V4-Pro-Max
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and DeepSeek-V4-Pro-Max side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs DeepSeek-V4-Pro-Max.