Sarvam 105B
According to BenchLM.ai, Sarvam 105B ranks #82 out of 119 models on the provisional leaderboard with an overall score of 39/100. It does not yet have enough sourced coverage for BenchLM's verified leaderboard. While not a frontier model, it offers specific advantages depending on the use case.
Sarvam 105B is a open weight model with a 128K 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 (#14), while its weakest is Instruction Following (#57). This performance profile makes it particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Ranking Distribution
Category rank across 5 benchmark categories — sorted by best rank
Category Performance
Scores across all benchmark categories (0-100 scale)
Category Breakdown
Agentic
Coding
Reasoning
Knowledge
Math
#14Multilingual
Multimodal
Inst. Following
#57Benchmark 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 105B perform overall in AI benchmarks?
Sarvam 105B currently ranks #82 out of 119 models on BenchLM's provisional leaderboard with an overall score of 39 (estimated). It is created by Sarvam and features a 128K context window.
Is Sarvam 105B good for knowledge and understanding?
Sarvam 105B has visible benchmark coverage in knowledge and understanding, but BenchLM does not currently assign it a global category rank there.
Is Sarvam 105B good for coding and programming?
Sarvam 105B has visible benchmark coverage in coding and programming, but BenchLM does not currently assign it a global category rank there.
Is Sarvam 105B good for reasoning and logic?
Sarvam 105B has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
Is Sarvam 105B good for agentic tool use and computer tasks?
Sarvam 105B 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 105B good for instruction following?
Sarvam 105B ranks #57 out of 119 models in instruction following benchmarks with an average score of 65.2. There are stronger options in this category.
Is Sarvam 105B open source?
Yes, Sarvam 105B 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 105B have full benchmark coverage on BenchLM?
Not yet. Sarvam 105B 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 105B?
Sarvam 105B has a context window of 128K, which determines how much text it can process in a single interaction.
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