GLM-5 vs Sarvam-30B Comparison
Comparing GLM-5 and Sarvam-30B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
GLM-5 outperforms in 2 benchmarks (BrowseComp, SWE-Bench Verified), while Sarvam-30B is better at 0 benchmarks.
GLM-5 significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
Cost data unavailable.
Model Size
Parameter count comparison
GLM-5 has 714.0B more parameters than Sarvam-30B, making it 2380.0% larger.
Context Window
Maximum input and output token capacity
Only GLM-5 specifies input context (200,000 tokens). Only GLM-5 specifies output context (128,000 tokens).
License
Usage and distribution terms
GLM-5 is licensed under MIT, while Sarvam-30B uses Apache 2.0.
License differences may affect how you can use these models in commercial or open-source projects.
MIT
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
GLM-5 was released on 2026-02-11, while Sarvam-30B was released on 2026-03-06.
Sarvam-30B is 1 month newer than GLM-5.
Feb 11, 2026
1 months ago
Mar 6, 2026
1 weeks ago
3w newerKnowledge Cutoff
When training data ends
Neither model specifies a knowledge cutoff date.
Unable to compare the recency of their training data.
Outputs Comparison
Key Takeaways
GLM-5
View detailsZhipu AI
Sarvam-30B
View detailsSarvam AI
Detailed Comparison
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