Benchmark analysis of Sarvam 105B by Sarvam across 12 sourced tests on BenchLM.
According to BenchLM.ai, Sarvam 105B ranks #53 out of 104 models with an overall score of 60/100. 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 12 of 126 tracked benchmarks. BenchLM only exposes verified benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Its strongest category is Mathematics (#20), while its weakest is Instruction Following (#42). This performance profile makes it particularly strong for mathematical reasoning, scientific computing, and quantitative analysis.
Provider
SarvamSource Type
Open WeightReasoning
ReasoningContext Window
128K
Model Status
Current
Release Date
Mar 6, 2026Overall Score
Pricing
$0.00 / $0.00
Input / output per 1M
Runtime
N/A
Latency unavailable
HMMT Feb 2025 2025 · Quarterly refresh · updated April 3, 2026
HMMT Nov 2025 2025 · Quarterly refresh · updated April 3, 2026
Sarvam 105B ranks #53 out of 104 models with an overall score of 60. It is created by Sarvam and features a 128K context window.
Sarvam 105B has visible benchmark coverage in knowledge and understanding, but BenchLM does not currently assign it a global category rank there.
Sarvam 105B has visible benchmark coverage in coding and programming, but BenchLM does not currently assign it a global category rank there.
Sarvam 105B ranks #20 out of 104 models in mathematics benchmarks with an average score of 92.3. There are stronger options in this category.
Sarvam 105B has visible benchmark coverage in reasoning and logic, but BenchLM does not currently assign it a global category rank there.
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.
Sarvam 105B ranks #42 out of 104 models in instruction following benchmarks with an average score of 84.8. There are stronger options in this category.
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.
Not yet. Sarvam 105B currently has 12 verified benchmark scores out of the 126 benchmarks BenchLM tracks. BenchLM only exposes verified public benchmark rows, so missing categories stay blank until a sourced evaluation is available.
Sarvam 105B has a context window of 128K, which determines how much text it can process in a single interaction.
New model releases, benchmark scores, and leaderboard changes. Every Friday.
Free. Your signup is stored with a derived country code for compliance routing.