Model comparison
LFM2.5-VL-1.6B-Extract vs Qwen3.5-122B-A10B
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: LFM2.5-VL-1.6B-Extract unranked (Not scored); Qwen3.5-122B-A10B #47 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-VL-1.6B-Extract and Qwen3.5-122B-A10B share 12 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to LFM2.5-VL-1.6B-Extract; 19 to Qwen3.5-122B-A10B.
Updated July 23, 2026- Shared results
- 12
- LFM2.5-VL-1.6B-Extract only
- 3
- Qwen3.5-122B-A10B only
- 19
- Comparable categories
- 0 / 8
Benchmark data for LFM2.5-VL-1.6B-Extract and Qwen3.5-122B-A10B 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.
Qwen3.5-122B-A10B has the larger context window at 262K, 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.
| Category | LFM2.5-VL-1.6B-Extract | Δ | Qwen3.5-122B-A10B |
|---|---|---|---|
| Agentic | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.5-122B-A10B56.4 |
| Coding | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.5-122B-A10B72.0 |
| Reasoning | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.5-122B-A10B60.2 |
| Knowledge | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.5-122B-A10B83.6 |
| Multilingual | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.5-122B-A10B82.2 |
| Multimodal | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.5-122B-A10B77.2 |
| Inst. Following | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.5-122B-A10B93.4 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | LFM2.5-VL-1.6B-Extract | Qwen3.5-122B-A10B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-VL-1.6B-ExtractNot available | Qwen3.5-122B-A10B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | LFM2.5-VL-1.6B-ExtractNot available | Qwen3.5-122B-A10BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-VL-1.6B-ExtractNot available | Qwen3.5-122B-A10BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-VL-1.6B-Extract128K | Qwen3.5-122B-A10B262K | Qwen3.5-122B-A10B lists the larger context window. |
Benchmark Deep Dive
Agentic7 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| τ²-bench resultsSource | 8.5% | 93.6% | Qwen3.5-122B-A10B leads |
| Terminal-Bench 2.0Source | — | 49.4% | Not comparable |
| BrowseCompSource | — | 63.8% | Not comparable |
| OSWorld-VerifiedSource | — | 58% | Not comparable |
| AA Agentic IndexSource | — | 20.7% | Not comparable |
| GDPval-AASource | — | 23.9% | Not comparable |
| GDPval-AASource | — | 978 | Not comparable |
Coding3 benchmarks
Reasoning3 benchmarks
Knowledge9 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 1.0% | 32.3% | Qwen3.5-122B-A10B leads |
| AA-GPQA DiamondSource | 28.9% | 85.7% | Qwen3.5-122B-A10B leads |
| AA-HLESource | 5.1% | 23.4% | Qwen3.5-122B-A10B leads |
| AA-Omniscience IndexSource | -83.9% | -39.6% | Qwen3.5-122B-A10B leads |
| AA-Omniscience AccuracySource | 5.2% | 24.7% | Qwen3.5-122B-A10B leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 85.5% | Qwen3.5-122B-A10B leads |
| MMLU-ProSource | — | 86.7% | Not comparable |
| SuperGPQASource | — | 67.1% | Not comparable |
| GPQASource | — | 86.6% | Not comparable |
Multilingual1 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
Multimodal9 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.5-122B-A10B | Result |
|---|---|---|---|
| Liquid Extract JSON ValiditySource | 99.6% | — | Not comparable |
| Liquid Extract F1Source | 99.6% | — | Not comparable |
| Liquid Extract VLM JudgeSource | 90.6% | — | Not comparable |
| AA-MMMU-ProSource | 26.5% | 75.0% | Qwen3.5-122B-A10B leads |
| MMMUSource | — | 83.9% | Not comparable |
| MMVUSource | — | 74.7% | Not comparable |
| MathVisionSource | — | 86.2% | Not comparable |
| CharXivSource | — | 77.2% | Not comparable |
| V*Source | — | 93.2% | Not comparable |
Frequently Asked Questions (3)
Can I compare LFM2.5-VL-1.6B-Extract and Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B today?
Qwen3.5-122B-A10B: $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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