GLM-5.3-Flash vs Llama 4 Maverick
GLM-5.3-Flash leads the LLM Stats Score 51.1 to 14.9. GLM-5.3-Flash is 1.2x cheaper per token.
Zhipu AI · Meta · Updated for 2026
Which is better?
GLM-5.3-Flash leads the overall LLM Stats Score 51.1 to 14.9, ranking #12 overall.
On price, GLM-5.3-Flash is roughly 1.2x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
GLM-5.3-Flash 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 GLM-5.3-Flash
- overall performance matters — it scores 51.1 and ranks #12 on LLM Stats
- your work emphasizes reasoning and coding — it leads those capability indexes
- cost matters — it's about 1.2x 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 Aug 2026
Choose Llama 4 Maverick
- you want predictable pricing at $0.17/M input and $0.60/M output
At a glance
The differences that matter most.
Capability indexes
Additional strengths measured across groups of related public benchmarks
Individual benchmarks
15 reported for GLM-5.3-Flash · 13 for Llama 4 Maverick
GLM-5.3-Flash and Llama 4 Maverickdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.
Human preference
Blind head-to-head votes and playground preference scores
Pricing Analysis
Price comparison per million tokens
For input processing, GLM-5.3-Flash ($0.15/1M tokens) is 1.1x cheaper than Llama 4 Maverick ($0.17/1M tokens).
For output processing, GLM-5.3-Flash ($0.50/1M tokens) is 1.2x cheaper than Llama 4 Maverick ($0.60/1M tokens).
In conclusion, Llama 4 Maverick is more expensive than GLM-5.3-Flash.*
* Using a 3:1 ratio of input to output tokens
Model Size
Parameter count comparison
Llama 4 Maverick has 80.0B more parameters than GLM-5.3-Flash, making it 25.0% larger.
Context Window
Maximum input and output token capacity
GLM-5.3-Flash accepts 1,048,576 input tokens compared to Llama 4 Maverick's 1,000,000 tokens. Llama 4 Maverick can generate longer responses up to 1,000,000 tokens, while GLM-5.3-Flash is limited to 131,072 tokens.
Input capabilities
Documented input modalities across available providers
Both GLM-5.3-Flash and Llama 4 Maverick support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
GLM-5.3-Flash
Llama 4 Maverick
License
Usage and distribution terms
GLM-5.3-Flash is licensed under MIT, while Llama 4 Maverick uses Llama 4 Community License Agreement.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Llama 4 Community License Agreement
Open weights
Release Timeline
When each model was launched
GLM-5.3-Flash was released on 2026-08-26, while Llama 4 Maverick was released on 2025-04-05.
GLM-5.3-Flash is 17 months newer than Llama 4 Maverick.
Aug 26, 2026
5 days ago
1.4yr newerApr 5, 2025
1.4 years ago
Knowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Provider Availability
GLM-5.3-Flash is available from DeepInfra, Novita, ZAI. Llama 4 Maverick is available from DeepInfra, Novita, Lambda, Groq, Fireworks, Together, Sambanova.
GLM-5.3-Flash
Llama 4 Maverick
Outputs Comparison
Judge for yourself.
Run your own prompts against GLM-5.3-Flash and Llama 4 Maverick side-by-side, then vote on the output you prefer.
FAQ
Common questions about GLM-5.3-Flash vs Llama 4 Maverick.