Model comparison
Claude Opus 4.6 (Adaptive) vs LFM2.5-VL-1.6B-Extract
Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: Claude Opus 4.6 (Adaptive) #35 (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. Claude Opus 4.6 (Adaptive) and LFM2.5-VL-1.6B-Extract share 12 comparable benchmark results. 0 of 8 categories are comparable. 4 results are unique to Claude Opus 4.6 (Adaptive); 3 to LFM2.5-VL-1.6B-Extract.
Updated July 23, 2026- Shared results
- 12
- Claude Opus 4.6 (Adaptive) only
- 4
- LFM2.5-VL-1.6B-Extract only
- 3
- Comparable categories
- 0 / 8
Benchmark data for Claude Opus 4.6 (Adaptive) and LFM2.5-VL-1.6B-Extract is coming soon on BenchLM.
Confidence note. This is a partial-evidence comparison with 12 shared benchmark results across 6 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.
Claude Opus 4.6 (Adaptive) has the larger context window at 1M, compared with 128K for LFM2.5-VL-1.6B-Extract.
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 | Claude Opus 4.6 (Adaptive) | LFM2.5-VL-1.6B-Extract | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | Claude Opus 4.6 (Adaptive)Not available | LFM2.5-VL-1.6B-ExtractNot available | A complete price comparison is not available. |
| Generation speedtokens per second | Claude Opus 4.6 (Adaptive)Not available | LFM2.5-VL-1.6B-ExtractNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | Claude Opus 4.6 (Adaptive)Not available | LFM2.5-VL-1.6B-ExtractNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | Claude Opus 4.6 (Adaptive)1M | LFM2.5-VL-1.6B-Extract128K | Claude Opus 4.6 (Adaptive) lists the larger context window. |
Benchmark Deep Dive
Agentic2 benchmarks
Coding2 benchmarks
Reasoning2 benchmarks
Knowledge6 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 43.7% | 1.0% | Claude Opus 4.6 (Adaptive) leads |
| AA-GPQA DiamondSource | 89.6% | 28.9% | Claude Opus 4.6 (Adaptive) leads |
| AA-HLESource | 36.7% | 5.1% | Claude Opus 4.6 (Adaptive) leads |
| AA-Omniscience IndexSource | 13.5% | -83.9% | Claude Opus 4.6 (Adaptive) leads |
| AA-Omniscience AccuracySource | 46.4% | 5.2% | Claude Opus 4.6 (Adaptive) leads |
| AA-Omniscience Hallucination RateSource | 61.3% | 94.0% | Claude Opus 4.6 (Adaptive) leads |
Multilingual1 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA Global-MMLU-LiteSource | 92.2% | — | Not comparable |
Multimodal5 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA-MMMU-ProSource | 75.4% | 26.5% | Claude Opus 4.6 (Adaptive) leads |
| Design Arena WebsiteSource | 1325 | — | Not comparable |
| Liquid Extract JSON ValiditySource | — | 99.6% | Not comparable |
| Liquid Extract F1Source | — | 99.6% | Not comparable |
| Liquid Extract VLM JudgeSource | — | 90.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | Claude Opus 4.6 (Adaptive) | LFM2.5-VL-1.6B-Extract | Result |
|---|---|---|---|
| AA-IFBenchSource | 53.1% | 33.1% | Claude Opus 4.6 (Adaptive) leads |
Frequently Asked Questions (2)
Can I compare Claude Opus 4.6 (Adaptive) 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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