Model comparison
LFM2.5-VL-1.6B-Extract vs Qwen3.6 Plus
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); Qwen3.6 Plus #30 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. LFM2.5-VL-1.6B-Extract and Qwen3.6 Plus share 12 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to LFM2.5-VL-1.6B-Extract; 48 to Qwen3.6 Plus.
Updated July 23, 2026- Shared results
- 12
- LFM2.5-VL-1.6B-Extract only
- 3
- Qwen3.6 Plus only
- 48
- Comparable categories
- 0 / 8
Benchmark data for LFM2.5-VL-1.6B-Extract and Qwen3.6 Plus 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.
Qwen3.6 Plus 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.
| Category | LFM2.5-VL-1.6B-Extract | Δ | Qwen3.6 Plus |
|---|---|---|---|
| Agentic | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.6 Plus61.6 |
| Coding | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.6 Plus70.3 |
| Reasoning | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.6 Plus62.0 |
| Knowledge | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.6 Plus57.1 |
| Math | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.6 Plus60.5 |
| Multilingual | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.6 Plus84.7 |
| Multimodal | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.6 Plus79.8 |
| Inst. Following | LFM2.5-VL-1.6B-ExtractNot measured | MarginNo overlap | Qwen3.6 Plus82.3 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | LFM2.5-VL-1.6B-Extract | Qwen3.6 Plus | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | LFM2.5-VL-1.6B-ExtractNot available | Qwen3.6 PlusNot available | A complete price comparison is not available. |
| Generation speedtokens per second | LFM2.5-VL-1.6B-ExtractNot available | Qwen3.6 PlusNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | LFM2.5-VL-1.6B-ExtractNot available | Qwen3.6 PlusNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | LFM2.5-VL-1.6B-Extract128K | Qwen3.6 Plus1M | Qwen3.6 Plus lists the larger context window. |
Benchmark Deep Dive
Agentic16 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.6 Plus | Result |
|---|---|---|---|
| τ²-bench resultsSource | 8.5% | 97.7% | Qwen3.6 Plus leads |
| Terminal-Bench 2.0Source | — | 61.6% | Not comparable |
| Claw-EvalSource | — | 58.8% | Not comparable |
| QwenClawBenchSource | — | 57.2% | Not comparable |
| τ³-bench resultsSource | — | 70.7% | Not comparable |
| VITA-BenchSource | — | 44.3% | Not comparable |
| DeepPlanningSource | — | 41.5% | Not comparable |
| ToolathlonSource | — | 39.8% | Not comparable |
| MCP AtlasSource | — | 48.2% | Not comparable |
| MCP-TasksSource | — | 74.1% | Not comparable |
| WideResearchSource | — | 74.3% | Not comparable |
| AA Agentic IndexSource | — | 27.6% | Not comparable |
| GDPval-AASource | — | 31.8% | Not comparable |
| GDPval-AASource | — | 1135 | Not comparable |
| Gert LabsSource | — | 50.60% | Not comparable |
| ResearchClawBenchSource | — | 18.0% | Not comparable |
Coding7 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.6 Plus | Result |
|---|---|---|---|
| AA-SciCodeSource | 3.0% | 40.7% | Qwen3.6 Plus leads |
| SWE-bench VerifiedSource | — | 78.8% | Not comparable |
| SWE-bench ProSource | — | 56.6% | Not comparable |
| SWE MultilingualSource | — | 73.8% | Not comparable |
| LiveCodeBench v6Source | — | 87.1% | Not comparable |
| Vibe Code BenchSource | — | 25.56% | Not comparable |
| AA Coding IndexSource | — | 54.5% | Not comparable |
Reasoning4 benchmarks
Knowledge12 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.6 Plus | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 1.0% | 39.6% | Qwen3.6 Plus leads |
| AA-GPQA DiamondSource | 28.9% | 88.2% | Qwen3.6 Plus leads |
| AA-HLESource | 5.1% | 25.7% | Qwen3.6 Plus leads |
| AA-Omniscience IndexSource | -83.9% | 2.7% | Qwen3.6 Plus leads |
| AA-Omniscience AccuracySource | 5.2% | 26.2% | Qwen3.6 Plus leads |
| AA-Omniscience Hallucination RateSource | 94.0% | 32.0% | Qwen3.6 Plus leads |
| GPQASource | — | 90.4% | Not comparable |
| SuperGPQASource | — | 71.6% | Not comparable |
| MMLU-ProSource | — | 88.5% | Not comparable |
| MMLU-ReduxSource | — | 94.5% | Not comparable |
| C-EvalSource | — | 93.3% | Not comparable |
| HLESource | — | 28.8% | Not comparable |
Math7 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.6 Plus | Result |
|---|---|---|---|
| AIME26Source | — | 95.3% | Not comparable |
| HMMT Feb 2025Source | — | 96.7% | Not comparable |
| HMMT Nov 2025Source | — | 94.6% | Not comparable |
| HMMT Feb 2026Source | — | 87.8% | Not comparable |
| MMAnswerBenchSource | — | 83.8% | Not comparable |
| FrontierMath v2 (Tiers 1-3)Source | — | 26.207% | Not comparable |
| FrontierMath v2 (Tier 4)Source | — | 8.333% | Not comparable |
Multilingual2 benchmarks
Multimodal12 benchmarks
| Benchmark | LFM2.5-VL-1.6B-Extract | Qwen3.6 Plus | 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% | 78.0% | Qwen3.6 Plus leads |
| MMMUSource | — | 86.0% | Not comparable |
| MMMU-ProSource | — | 78.8% | Not comparable |
| MathVisionSource | — | 88.0% | Not comparable |
| VideoMMMUSource | — | 84.0% | Not comparable |
| ScreenSpot ProSource | — | 68.2% | Not comparable |
| CharXivSource | — | 81.5% | Not comparable |
| V*Source | — | 96.9% | Not comparable |
| Design Arena WebsiteSource | — | 1249 | Not comparable |
Frequently Asked Questions (3)
Can I compare LFM2.5-VL-1.6B-Extract and Qwen3.6 Plus 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 Qwen3.6 Plus today?
Qwen3.6 Plus: Pricing unavailable Both model pages still include creator, context window, reasoning mode, and other metadata while benchmark coverage fills in.
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