Model comparison
Exaone 4.0 32B vs LFM2.5-VL-1.6B-Extract
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: Exaone 4.0 32B #170 (Estimated); LFM2.5-VL-1.6B-Extract unranked (Not scored). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. Exaone 4.0 32B and LFM2.5-VL-1.6B-Extract share 11 comparable benchmark results. 0 of 8 categories are comparable. 2 results are unique to Exaone 4.0 32B; 4 to LFM2.5-VL-1.6B-Extract.
Updated July 23, 2026- Shared results
- 11
- Exaone 4.0 32B only
- 2
- LFM2.5-VL-1.6B-Extract only
- 4
- Comparable categories
- 0 / 8
Benchmark data for Exaone 4.0 32B and LFM2.5-VL-1.6B-Extract 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.
| Category | Exaone 4.0 32B | Δ | LFM2.5-VL-1.6B-Extract |
|---|---|---|---|
| Knowledge | Exaone 4.0 32B81.8 | MarginNo overlap | LFM2.5-VL-1.6B-ExtractNot measured |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | Exaone 4.0 32B | LFM2.5-VL-1.6B-Extract | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Exaone 4.0 32BNot available | LFM2.5-VL-1.6B-ExtractNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Exaone 4.0 32BNot available | LFM2.5-VL-1.6B-ExtractNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Exaone 4.0 32BNot available | LFM2.5-VL-1.6B-ExtractNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Exaone 4.0 32B128K | LFM2.5-VL-1.6B-Extract128K | Listed context windows are equal. |
Benchmark Deep Dive
Agentic1 benchmarks
| Benchmark | Exaone 4.0 32B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| τ²-bench resultsSource | 4.1% | 8.5% | LFM2.5-VL-1.6B-Extract leads |
Coding1 benchmarks
| Benchmark | Exaone 4.0 32B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA-SciCodeSource | 25.2% | 3.0% | Exaone 4.0 32B leads |
Reasoning2 benchmarks
Knowledge7 benchmarks
| Benchmark | Exaone 4.0 32B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| MMLU-ProSource | 81.8% | — | Not comparable |
| Artificial Analysis Intelligence IndexSource | 6.0% | 1.0% | Exaone 4.0 32B leads |
| AA-GPQA DiamondSource | 62.8% | 28.9% | Exaone 4.0 32B leads |
| AA-HLESource | 4.9% | 5.1% | LFM2.5-VL-1.6B-Extract leads |
| AA-Omniscience IndexSource | -62.3% | -83.9% | Exaone 4.0 32B leads |
| AA-Omniscience AccuracySource | 10.4% | 5.2% | Exaone 4.0 32B leads |
| AA-Omniscience Hallucination RateSource | 81.0% | 94.0% | Exaone 4.0 32B leads |
Math1 benchmarks
| Benchmark | Exaone 4.0 32B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AIME 2025Source | 85.3% | — | Not comparable |
Multimodal4 benchmarks
Inst. Following1 benchmarks
| Benchmark | Exaone 4.0 32B | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA-IFBenchSource | 33.5% | 33.1% | Exaone 4.0 32B leads |
Frequently Asked Questions (2)
Can I compare Exaone 4.0 32B and LFM2.5-VL-1.6B-Extract 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.
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