Sarvam 30B

Sarvam 30B is a reasoning model from Sarvam. 8 benchmarks count toward its score, in 4 categories.

availableShows if the model has enough results for an index.
IndexOverall score out of 100.28.2 ±10.2
CoverageShare of the index weight with results.55%
SpeedOutput tokens per second.
Input / 1MUS dollars per 1M input tokens.Free
Output / 1MUS dollars per 1M output tokens.Free
ContextMaximum tokens in one request.64K
EloLMArena rating and rank.N/A

The index is a score out of 100. The ± range shows how much it can change.

CapabilitiesScore per category, out of 100.

Out of 100
AgenticMulti-step tasks with tools.
23.6
CodingCode writing and repair.
N/A
ReasoningLogic problems and puzzles.
23.6
MultimodalTasks with images and text.
N/A
KnowledgeFacts and expert knowledge.
31.5
MultilingualTasks in many languages.
N/A
InstructionTasks with strict rules in the prompt.
16.0
MathMath problems.
N/A

Results

8 counted
BenchmarkThe test name.CategoryThe capability that the test measures.ResultThe score from the publisher.IndexThis result as a score out of 100.RunThe settings of the run.DateDate of the result.Published byThe source of the result.
Artificial Analysis GPQA DiamondKnowledge63.3%33.4Artificial Analysis
τ²-Bench Tool-Agent-User EvaluationAgentic34.5%23.6Victor Barres et al.
Artificial Analysis IFBenchInstruction26.5%16.0Artificial Analysis
Artificial Analysis Omniscience AccuracyKnowledge12.6%29.5Artificial Analysis
Artificial Analysis Humanity's Last ExamKnowledge7.5%32.7Artificial Analysis
Artificial Analysis Intelligence IndexKnowledge6.6%30.6Artificial Analysis
Critical Physics TasksReasoning0.3%39.0Artificial Analysis
Artificial Analysis Long Context ReasoningReasoning0.0%8.3Artificial Analysis

8 benchmarks count, from 8 of 8 results. A grey row does not count. Too few models took that benchmark.

Sources

BenchLM benchmark aggregationUsed with attribution; per-benchmark results credited to their original authors

More from Sarvam

Sarvam 105B32.7