Beam 1.0 vs GLM-5.3-Flash
GLM-5.3-Flash leads the LLM Stats Score 48.8 to 44.6. GLM-5.3-Flash is 2.5x cheaper per token.
Reflection AI · Zhipu AI · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 48.8 to 44.6, ranking #28 overall.
In the 4 individual benchmarks reported for both models, GLM-5.3-Flash wins 4; this is a narrower head-to-head signal than the composite indexes.
On price, GLM-5.3-Flash is roughly 2.5x 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 want the most recent training data — it shipped Oct 2026
Choose GLM-5.3-Flash
- overall performance matters — it scores 48.8 and ranks #28 on LLM Stats
- your work emphasizes agents — it leads those capability indexes
- you value its reported benchmark strengths — it wins 4 of 4 exact shared results
- cost matters — it's about 2.5x 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 · 15 for GLM-5.3-Flash
Beam 1.0 outperforms in 0 benchmarks, while GLM-5.3-Flash is better at 4 benchmarks (AutomationBench, DeepSWE 1.1, Humanity's Last Exam, Terminal-Bench 2.1).
GLM-5.3-Flash 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 2.7x more expensive than GLM-5.3-Flash ($0.15/1M tokens).
For output processing, Beam 1.0 ($1.20/1M tokens) is 2.4x more expensive than GLM-5.3-Flash ($0.50/1M tokens).
In conclusion, Beam 1.0 is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Beam 1.0 has 181.3B more parameters than GLM-5.3-Flash, making it 56.7% larger.
Context Window
Maximum input and output token capacity
Both models have the same input context window of 1,048,576 tokens. GLM-5.3-Flash can generate longer responses up to 1,048,576 tokens, while Beam 1.0 is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
GLM-5.3-Flash supports multimodal inputs, whereas Beam 1.0 does not.
GLM-5.3-Flash can handle both text and other forms of data like images, making it suitable for multimodal applications.
Beam 1.0
GLM-5.3-Flash
License
Usage and distribution terms
Beam 1.0 is licensed under Apache 2.0, while GLM-5.3-Flash 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 GLM-5.3-Flash was released on 2026-08-26.
Beam 1.0 is 2 months newer than GLM-5.3-Flash.
Oct 16, 2026
6 days ago
1mo newerAug 26, 2026
1 months ago
Knowledge Cutoff
When training data ends
Beam 1.0 has a documented knowledge cutoff of 2026-06-30, while GLM-5.3-Flash'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 GLM-5.3-Flash's cutoff date.
Jun 2026
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Provider Availability
Beam 1.0 is available from Together, Reflection AI. GLM-5.3-Flash is available from DeepInfra, FriendliAI, Novita, ZAI.
Beam 1.0
GLM-5.3-Flash
Outputs Comparison
Judge for yourself.
Run your own prompts against Beam 1.0 and GLM-5.3-Flash side-by-side, then vote on the output you prefer.
FAQ
Common questions about Beam 1.0 vs GLM-5.3-Flash.