Model comparison
LFM2.5-1.2B-JP-202606 vs Sarvam 30B
Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: LFM2.5-1.2B-JP-202606 unranked (Not scored); Sarvam 30B #169 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-1.2B-JP-202606 and Sarvam 30B share 0 comparable benchmark results. 0 of 8 categories are comparable. 0 results are unique to LFM2.5-1.2B-JP-202606; 11 to Sarvam 30B.
Updated July 23, 2026- Shared results
- 0
- LFM2.5-1.2B-JP-202606 only
- 0
- Sarvam 30B only
- 11
- Comparable categories
- 0 / 8
Benchmark data for LFM2.5-1.2B-JP-202606 and Sarvam 30B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 evidence categories; 0 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
BenchLM does not have sourced benchmark coverage for LFM2.5-1.2B-JP-202606 yet. This comparison is currently limited to metadata such as context window, reasoning mode, and pricing where available.
Sarvam 30B has the larger context window at 64K, compared with 32K for LFM2.5-1.2B-JP-202606.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | LFM2.5-1.2B-JP-202606 | Sarvam 30B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-1.2B-JP-202606Not available | Sarvam 30B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | LFM2.5-1.2B-JP-202606Not available | Sarvam 30BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-1.2B-JP-202606Not available | Sarvam 30BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-1.2B-JP-20260632K | Sarvam 30B64K | Sarvam 30B lists the larger context window. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | LFM2.5-1.2B-JP-202606 | Sarvam 30B | Result |
|---|---|---|---|
| τ²-bench resultsSource | — | 34.5% | Not comparable |
Coding1 benchmarks
| Benchmark | LFM2.5-1.2B-JP-202606 | Sarvam 30B | Result |
|---|---|---|---|
| AA-SciCodeSource | — | 19.2% | Not comparable |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | LFM2.5-1.2B-JP-202606 | Sarvam 30B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | — | 6.6% | Not comparable |
| AA-GPQA DiamondSource | — | 63.3% | Not comparable |
| AA-HLESource | — | 7.0% | Not comparable |
| AA-Omniscience IndexSource | — | -72.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 12.7% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 97.0% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | LFM2.5-1.2B-JP-202606 | Sarvam 30B | Result |
|---|---|---|---|
| AA-IFBenchSource | — | 26.5% | Not comparable |
Frequently Asked Questions (3)
Can I compare LFM2.5-1.2B-JP-202606 and Sarvam 30B on BenchLM yet?
Not fully yet. BenchLM is tracking both models, but the sourced benchmark breakdown for this comparison is still coming soon.
Why does this comparison show “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.
What data is available for LFM2.5-1.2B-JP-202606 and Sarvam 30B today?
Sarvam 30B: $0.00 input / $0.00 output per 1M tokens Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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