Sarvam 30B
BenchLM is tracking Sarvam 30B, but this profile is currently excluded from the public leaderboard because it still lacks enough non-generated benchmark coverage to rank safely. Only non-generated public benchmark rows appear below.
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 16 of 225 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Mathematics (#21). 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
#21Multilingual
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.
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Frequently Asked Questions
How does Sarvam 30B perform overall in AI benchmarks?
Sarvam 30B has 16 published benchmark scores on BenchLM, but it does not yet have enough non-generated coverage to receive a global overall rank.
Is Sarvam 30B good for knowledge and understanding?
Sarvam 30B has visible benchmark coverage in knowledge and understanding, but BenchLM does not currently assign it a global category rank there.
Is Sarvam 30B good for coding and programming?
Sarvam 30B has visible benchmark coverage in coding and programming, but BenchLM does not currently assign it a global category rank there.
Is Sarvam 30B good for reasoning and logic?
Sarvam 30B has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is Sarvam 30B good for agentic tool use and computer tasks?
Sarvam 30B has visible benchmark coverage in agentic tool use and computer tasks, but BenchLM does not currently assign it a global category rank there.
Is Sarvam 30B good for instruction following?
Sarvam 30B has visible benchmark coverage in instruction following, but BenchLM does not currently assign it a global category rank there.
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 16 published benchmark scores out of the 225 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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