Beam 1.0 vs Sakana Namazu
Beam 1.0 and Sakana Namazu are closely matched at 44.6 and 41.7 on the LLM Stats Score. Beam 1.0 is 2.9x cheaper per token.
Reflection AI · Sakana AI · Updated for 2026
Which is better?
Beam 1.0 and Sakana Namazu are closely matched on the overall LLM Stats Score at 44.6 and 41.7.
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, Beam 1.0 is roughly 2.9x 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
- you value its reported benchmark strengths — it wins 1 of 1 exact shared results
- cost matters — it's about 2.9x 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
- you need open weights you can self-host or fine-tune
Choose Sakana Namazu
- you want predictable pricing at $0.95/M input and $4.00/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 · 3 for Sakana Namazu
Beam 1.0 outperforms in 1 benchmarks (AIME 2026), while Sakana Namazu 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 2.4x cheaper than Sakana Namazu ($0.95/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 3.3x cheaper than Sakana Namazu ($4.00/1M tokens).
In conclusion, Sakana Namazu is more expensive than Beam 1.0.*
* 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 Sakana Namazu's 256,000 tokens. Sakana Namazu 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
Sakana Namazu supports multimodal inputs, whereas Beam 1.0 does not.
Sakana Namazu can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
Sakana Namazu
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while Sakana Namazu 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 Sakana Namazu was released on 2026-08-03.
Beam 1.0 is 2 months newer than Sakana Namazu.
Oct 16, 2026
3 days ago
2mo newerAug 3, 2026
2 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while Sakana Namazu'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 Sakana Namazu's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. Sakana Namazu is available from Sakana AI.
Beam 1.0
Sakana Namazu
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and Sakana Namazu side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs Sakana Namazu.