Model comparison
LFM2.5-VL-1.6B-Extract vs Sarvam 105B
Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: LFM2.5-VL-1.6B-Extract unranked (Not scored); Sarvam 105B #157 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-VL-1.6B-Extract and Sarvam 105B share 11 comparable benchmark results. 0 of 8 categories are comparable. 4 results are unique to LFM2.5-VL-1.6B-Extract; 0 to Sarvam 105B.
Updated July 23, 2026- Shared results
- 11
- LFM2.5-VL-1.6B-Extract only
- 4
- Sarvam 105B only
- 0
- Comparable categories
- 0 / 8
Benchmark data for LFM2.5-VL-1.6B-Extract and Sarvam 105B is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 11 shared benchmark results across 5 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 has partial data for these models, but not enough overlapping benchmark coverage to produce a fair score-level comparison yet.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | LFM2.5-VL-1.6B-Extract | Sarvam 105B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-VL-1.6B-ExtractNot available | Sarvam 105B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | LFM2.5-VL-1.6B-ExtractNot available | Sarvam 105BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-VL-1.6B-ExtractNot available | Sarvam 105BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-VL-1.6B-Extract128K | Sarvam 105B128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Sarvam 105B | Result |
|---|---|---|---|
| τ²-bench resultsSource | 8.5% | 46.8% | Sarvam 105B leads |
Coding1 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Sarvam 105B | Result |
|---|---|---|---|
| AA-SciCodeSource | 3.0% | 26.4% | Sarvam 105B leads |
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Sarvam 105B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 1.0% | 11.9% | Sarvam 105B leads |
| AA-GPQA DiamondSource | 28.9% | 73.8% | Sarvam 105B leads |
| AA-HLESource | 5.1% | 10.1% | Sarvam 105B leads |
| AA-Omniscience IndexSource | -83.9% | -59.5% | Sarvam 105B leads |
| AA-Omniscience AccuracySource | 5.2% | 17.6% | Sarvam 105B leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 93.5% | Sarvam 105B leads |
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Sarvam 105B | Result |
|---|---|---|---|
| AA-IFBenchSource | 33.1% | 34.4% | Sarvam 105B leads |
Frequently Asked Questions (3)
Can I compare LFM2.5-VL-1.6B-Extract and Sarvam 105B 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-VL-1.6B-Extract and Sarvam 105B today?
Sarvam 105B: $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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