Sarvam 30B
BenchLM is tracking Sarvam 30B, but sourced benchmark results are not published on the site yet. This page currently shows the model metadata we can verify now, and score-level benchmark coverage will appear once public evaluations land.
Sarvam 30B is a open weight model with a 64K token context window. It uses explicit chain-of-thought reasoning, which typically improves performance on math and complex reasoning tasks at the cost of higher latency and token usage.
This profile currently has 0 sourced benchmarks on BenchLM, so the benchmark sections below are intentionally marked as coming soon.
Its strongest category is Mathematics (#23). This performance profile makes it particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Ranking Distribution
Category rank across 4 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
Coding
Reasoning
Knowledge
Math
#23Multilingual
Multimodal
Inst. Following
Benchmark Details
Only benchmark rows with an attached exact-source record are shown here. Source-unverified manual rows and generated rows are hidden from model pages.
Compare This Model
See how Sarvam 30B stacks up against similar models
Frequently Asked Questions
How does Sarvam 30B perform overall in AI benchmarks?
BenchLM is tracking Sarvam 30B, but sourced benchmark coverage is still coming soon. We currently list its creator, model type, and context window while we wait for public benchmark results.
Is Sarvam 30B open source?
Yes, Sarvam 30B is an open weight model created by Sarvam, meaning it can be downloaded and run locally or fine-tuned for specific use cases.
Does Sarvam 30B have full benchmark coverage on BenchLM?
Not yet. Sarvam 30B currently has 0 published benchmark scores out of the 193 benchmarks BenchLM tracks. BenchLM only exposes non-generated public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
What is the context window size of Sarvam 30B?
Sarvam 30B has a context window of 64K, which determines how much text it can process in a single interaction.
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