Beam 1.0 vs DeepSeek-V4-Flash-0423
Beam 1.0 leads the LLM Stats Score 44.6 to 35.9. DeepSeek-V4-Flash-0423 is 5.3x cheaper per token.
Reflection AI · DeepSeek · Updated for 2026
Which is better?
Beam 1.0 leads the overall LLM Stats Score 44.6 to 35.9, ranking #46 overall.
In the 8 individual benchmarks reported for both models, Beam 1.0 wins 6; this is a narrower head-to-head signal than the composite indexes.
On price, DeepSeek-V4-Flash-0423 is roughly 5.3x 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
- 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 6 of 8 exact shared results
- you want the most recent training data — it shipped Oct 2026
Choose DeepSeek-V4-Flash-0423
- cost matters — it's about 5.3x 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 · 19 for DeepSeek-V4-Flash-0423
Beam 1.0 outperforms in 6 benchmarks (BrowseComp, GPQA, MCP Atlas, SWE-bench Multilingual, SWE-Bench Pro, SWE-Bench Verified), while DeepSeek-V4-Flash-0423 is better at 2 benchmarks (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 4.4x more expensive than DeepSeek-V4-Flash-0423 ($0.09/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 6.7x more expensive than DeepSeek-V4-Flash-0423 ($0.18/1M tokens).
In conclusion, Beam 1.0 is more expensive than DeepSeek-V4-Flash-0423.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Beam 1.0 has 217.3B more parameters than DeepSeek-V4-Flash-0423, making it 76.5% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. DeepSeek-V4-Flash-0423 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-Flash-0423 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-Flash-0423 was released on 2026-04-23.
Beam 1.0 is 6 months newer than DeepSeek-V4-Flash-0423.
Oct 16, 2026
3 days ago
5mo newerApr 23, 2026
5 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while DeepSeek-V4-Flash-0423'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-Flash-0423's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. DeepSeek-V4-Flash-0423 is available from DeepInfra, Novita.
Beam 1.0
DeepSeek-V4-Flash-0423
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and DeepSeek-V4-Flash-0423 side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs DeepSeek-V4-Flash-0423.