Qwen3.5-397B-A17B vs Sarvam-30B Comparison
Comparing Qwen3.5-397B-A17B and Sarvam-30B across benchmarks, pricing, and capabilities.
Performance Benchmarks
Comparative analysis across standard metrics
Qwen3.5-397B-A17B outperforms in 7 benchmarks (BrowseComp, GPQA, HMMT 2025, HMMT25, LiveCodeBench v6, MMLU-Pro, SWE-Bench Verified), while Sarvam-30B is better at 0 benchmarks.
Qwen3.5-397B-A17B 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
Qwen3.5-397B-A17B has 367.0B more parameters than Sarvam-30B, making it 1223.3% larger.
Context Window
Maximum input and output token capacity
Only Qwen3.5-397B-A17B specifies input context (262,144 tokens). Only Qwen3.5-397B-A17B specifies output context (64,000 tokens).
Input Capabilities
Supported data types and modalities
Qwen3.5-397B-A17B supports multimodal inputs, whereas Sarvam-30B does not.
Qwen3.5-397B-A17B can handle both text and other forms of data like images, making it suitable for multimodal applications.
Qwen3.5-397B-A17B
Sarvam-30B
License
Usage and distribution terms
Both models are licensed under Apache 2.0.
Both models share the same licensing terms, providing consistent usage rights.
Apache 2.0
Open weights
Apache 2.0
Open weights
Release Timeline
When each model was launched
Qwen3.5-397B-A17B was released on 2026-02-16, while Sarvam-30B was released on 2026-03-06.
Sarvam-30B is 1 month newer than Qwen3.5-397B-A17B.
Feb 16, 2026
4 weeks ago
Mar 6, 2026
1 weeks ago
2w 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
Qwen3.5-397B-A17B
View detailsAlibaba Cloud / Qwen Team
Sarvam-30B
View detailsSarvam AI
Detailed Comparison
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