Side-by-side benchmark comparison across agentic, coding, multimodal, knowledge, reasoning, and math workflows.
Sarvam 30B
48
Winner · 0/8 categoriesTrinity-Large-Thinking
~0
0/8 categoriesSarvam 30B· Trinity-Large-Thinking
Benchmark data for Sarvam 30B and Trinity-Large-Thinking is coming soon on BenchLM.
BenchLM has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Trinity-Large-Thinking is priced at $0.25 input / $0.90 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Sarvam 30B. Trinity-Large-Thinking has the larger context window at 512K, compared with 64K for Sarvam 30B.
BenchLM keeps the benchmark table and the operator tradeoffs on the same page so a better score does not hide a materially slower, pricier, or smaller-context model.
Runtime metrics show N/A when BenchLM does not have a sourced snapshot for that exact model. The scoring rules and freshness policy are documented on the methodology page.
| Benchmark | Sarvam 30B | Trinity-Large-Thinking |
|---|---|---|
| Agentic | ||
| BrowseComp | 35.5% | — |
| Tau2-Airline | — | 88.0% |
| Tau2-Telecom | — | 94.7% |
| BFCL v4 | — | 70.1% |
| Coding | ||
| HumanEval | 92.1% | — |
| LiveCodeBench v6 | 70.0% | — |
| SWE-bench Verified | 34% | — |
| SWE-bench Verified* | — | 63.2% |
| Multimodal & Grounded | ||
| Coming soon | ||
| Reasoning | ||
| gpqaDiamond | 66.5% | — |
| Knowledge | ||
| MMLU | 85.1% | — |
| MMLU-Pro | 80% | — |
| GPQA-D | — | 76.3% |
| MMLU-Pro (Arcee) | — | 83.4% |
| Instruction Following | ||
| IFBench | — | 52.3% |
| Multilingual | ||
| Coming soon | ||
| Mathematics | ||
| MATH-500 | 97% | — |
| AIME 2025 | 80% | — |
| HMMT Feb 2025 | 73.3% | — |
| HMMT Nov 2025 | 74.2% | — |
| AIME25 (Arcee) | — | 96.3% |
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
BenchLM only shows category winners and benchmark-level calls when we have sourced results that can be compared fairly. For these models, the public benchmark coverage is not complete enough yet.
Sarvam 30B: $0.00 input / $0.00 output per 1M tokens Trinity-Large-Thinking: $0.25 input / $0.90 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
Get notified when new models drop, benchmark scores change, or the leaderboard shifts. One email per week.
Free. No spam. Unsubscribe anytime. We only store derived location metadata for consent routing.