Beam 1.0 vs GPT-4.1 nano
Beam 1.0 leads the LLM Stats Score 44.6 to 1.1. GPT-4.1 nano is 3.4x cheaper per token.
Reflection AI · OpenAI · Updated for 2026
Which is better?
Beam 1.0 leads the overall LLM Stats Score 44.6 to 1.1, ranking #46 overall.
In the 1 individual benchmarks reported for both models, Beam 1.0 wins 1; this is a narrower head-to-head signal than the composite indexes.
On price, GPT-4.1 nano is roughly 3.4x 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 1 of 1 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
- you need open weights you can self-host or fine-tune
Choose GPT-4.1 nano
- cost matters — it's about 3.4x 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 · 24 for GPT-4.1 nano
Beam 1.0 outperforms in 1 benchmarks (GPQA), while GPT-4.1 nano 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 4.0x more expensive than GPT-4.1 nano ($0.10/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 3.0x more expensive than GPT-4.1 nano ($0.40/1M tokens).
In conclusion, Beam 1.0 is more expensive than GPT-4.1 nano.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Beam 1.0 accepts 1,048,576 input tokens compared to GPT-4.1 nano's 1,047,576 tokens. Beam 1.0 can generate longer responses up to 131,072 tokens, while GPT-4.1 nano is limited to 32,768 tokens.
Input capabilities
Documented input modalities across available providers
GPT-4.1 nano supports multimodal inputs, whereas Beam 1.0 does not.
GPT-4.1 nano can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
GPT-4.1 nano
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while GPT-4.1 nano uses a proprietary license.
License differences may affect how you can use these models in commercial or open-source projects.
Apache 2.0
Open weights
Proprietary
Closed source
Release Timeline
When each model was launched
Beam 1.0 was released on 2026-10-16, while GPT-4.1 nano was released on 2025-04-14.
Beam 1.0 is 18 months newer than GPT-4.1 nano.
Oct 16, 2026
6 days ago
1.5yr newerApr 14, 2025
1.5 years ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a knowledge cutoff of 2026-06-30, while GPT-4.1 nano has a cutoff of 2024-05-31.
Beam 1.0 has more recent training data (up to 2026-06-30), making it potentially better informed about events through that date compared to GPT-4.1 nano (2024-05-31).
Jun 2026
2.1 yr newerMay 2024
Provider Availability
Beam 1.0 is available from Together, Reflection AI. GPT-4.1 nano is available from OpenAI.
Beam 1.0
GPT-4.1 nano
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and GPT-4.1 nano side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs GPT-4.1 nano.