Model profile
Sarvam 30B
Evidence coverage
11 of 323 tracked benchmarks are published. 0 are verified and 11 provisional. 5 of 8 categories are measured.
- Published / tracked
- 11 / 323
- Verified
- 0
- Provisional
- 11
- Categories with evidence
- 5 / 8
Evidence by category
- Agentic1 benchmarkReported
- Coding1 benchmarkReported
- Reasoning2 benchmarksReported
- Knowledge6 benchmarksReported
- Math0 benchmarksNot measured
- Multilingual0 benchmarksNot measured
- Multimodal0 benchmarksNot measured
- Inst. Following1 benchmarkReported
Sarvam 30B ranks #169 out of 200 models on the public leaderboard with an overall score of 40.7/100. 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 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 11 of 323 tracked benchmarks. BenchLM only exposes non-generated benchmark rows publicly, so missing categories stay blank until a sourced evaluation is available.
Peer position
Exact provisional scores and ranks for the closest listed peers. A score can appear before a model clears the evidence threshold for a rank, so equal scores can have different rank states.
Range 39.9–41.49
- GPT-4oOpenAICompare#16541.49GPT-4o is #165 with a score of 41.49.
- LFM2.5-8B-A1BLiquidAICompare#16641.42LFM2.5-8B-A1B is #166 with a score of 41.42.
- Claude 3 OpusAnthropicCompare#16841.13Claude 3 Opus is #168 with a score of 41.13.
- Sarvam 30BCurrent modelSarvam#16940.7Sarvam 30B is #169 with a score of 40.7.
- Grok 3 [Beta]xAICompare#17140.43Grok 3 [Beta] is #171 with a score of 40.43.
- Ministral 3 8B (Reasoning)MistralCompare#17240.38Ministral 3 8B (Reasoning) is #172 with a score of 40.38.
- Mistral 7B v0.3MistralCompare#17339.9Mistral 7B v0.3 is #173 with a score of 39.9.
Category percentile
More
Relative position among models eligible for each sourced category. A higher percentile means a stronger position within that category's ranked cohort; 100 is highest.
Category evidence
Scores and ranks appear only where this model has published benchmark evidence. Categories without displayable source records remain not measured.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticRank Not rankedWeight 22%1 benchmarkReported | 35.5 | Not ranked | Not available | 22% | 1 benchmark | Reported |
| CodingRank Not rankedWeight 20%1 benchmarkReported | 34.0 | Not ranked | Not available | 20% | 1 benchmark | Reported |
| ReasoningWeight 17%2 benchmarksReported | Score pending | Not ranked | Not available | 17% | 2 benchmarks | Reported |
| KnowledgeRank Not rankedWeight 12%6 benchmarksReported | 80.0 | Not ranked | Not available | 12% | 6 benchmarks | Reported |
| MathWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingWeight 5%1 benchmarkReported | Score pending | Not ranked | Not available | 5% | 1 benchmark | Reported |
Benchmark Details
Rows below have a displayable published verification record. Each source link and provenance note remains in the page HTML while its category is closed. Source-unverified manual rows and generated rows stay hidden.
Agentic1 benchmark
τ²-Bench Tool-Agent-User Evaluation
Coding1 benchmark
Artificial Analysis SciCode
Reasoning2 benchmarks
Artificial Analysis Long Context Reasoning
Critical Physics Tasks
Knowledge6 benchmarks
Artificial Analysis GPQA Diamond
Artificial Analysis Humanity's Last Exam
Artificial Analysis Omniscience Index
Artificial Analysis Omniscience Accuracy
Artificial Analysis Omniscience Hallucination Rate
Inst. Following1 benchmark
Artificial Analysis IFBench
Frequently Asked Questions
How does Sarvam 30B perform overall in AI benchmarks?
Sarvam 30B has 11 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 11 published benchmark scores out of the 323 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 published context window of 64K, which determines how much text it can process in a single interaction.
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