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Model comparison

DeepSeek V3.2 vs LFM2.5-VL-1.6B-Extract

Data verified

Head-to-head evidence from 11 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

55.4/100
No comparison
0 category wins0 category wins

Public leaderboard positions: DeepSeek V3.2 #82 (Supported); 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. DeepSeek V3.2 and LFM2.5-VL-1.6B-Extract share 11 comparable benchmark results. 0 of 8 categories are comparable. 8 results are unique to DeepSeek V3.2; 4 to LFM2.5-VL-1.6B-Extract.

Updated July 23, 2026
Shared results
11
DeepSeek V3.2 only
8
LFM2.5-VL-1.6B-Extract only
4
Comparable categories
0 / 8

Benchmark data for DeepSeek V3.2 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 scores and score margins for DeepSeek V3.2 and LFM2.5-VL-1.6B-Extract
CategoryDeepSeek V3.2ΔLFM2.5-VL-1.6B-Extract
CodingDeepSeek V3.260.9MarginNo overlapLFM2.5-VL-1.6B-ExtractNot measured
MathDeepSeek V3.217.1MarginNo overlapLFM2.5-VL-1.6B-ExtractNot measured

Operational comparison

Runtime and commercial metrics are compared only when both models have a complete sourced value.

MetricDeepSeek V3.2LFM2.5-VL-1.6B-ExtractComparison
Input / output priceUSD per 1M tokensDeepSeek V3.2$0.28 input / $0.42 outputLFM2.5-VL-1.6B-ExtractNot availableA complete price comparison is not available.
Generation speedtokens per secondDeepSeek V3.235 tok/sLFM2.5-VL-1.6B-ExtractNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenDeepSeek V3.23.75 sLFM2.5-VL-1.6B-ExtractNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensDeepSeek V3.2128KLFM2.5-VL-1.6B-Extract128KListed context windows are equal.

Benchmark Deep Dive

Agentic
BenchmarkDeepSeek V3.2LFM2.5-VL-1.6B-ExtractResult
Claw-EvalSource 40.2%Not comparable
VITA-BenchSource 18.5%Not comparable
τ²-bench resultsSource 78.9%8.5%DeepSeek V3.2 leads
Gert LabsSource 29.57%Not comparable
Coding
BenchmarkDeepSeek V3.2LFM2.5-VL-1.6B-ExtractResult
SWE-RebenchSource 60.9%Not comparable
React Native EvalsSource 71.5%Not comparable
AA-SciCodeSource 38.7%3.0%DeepSeek V3.2 leads
Reasoning
BenchmarkDeepSeek V3.2LFM2.5-VL-1.6B-ExtractResult
AA-LCRSource 39.0%0.0%DeepSeek V3.2 leads
CritPtSource 0.9%0.0%DeepSeek V3.2 leads
Knowledge
BenchmarkDeepSeek V3.2LFM2.5-VL-1.6B-ExtractResult
Artificial Analysis Intelligence IndexSource 24.7%1.0%DeepSeek V3.2 leads
AA-GPQA DiamondSource 75.1%28.9%DeepSeek V3.2 leads
AA-HLESource 10.5%5.1%DeepSeek V3.2 leads
AA-Omniscience IndexSource -46.7%-83.9%DeepSeek V3.2 leads
AA-Omniscience AccuracySource 24.2%5.2%DeepSeek V3.2 leads
AA-Omniscience Hallucination RateSource 93.5%94.0%DeepSeek V3.2 leads
Math
BenchmarkDeepSeek V3.2LFM2.5-VL-1.6B-ExtractResult
FrontierMath v2 (Tiers 1-3)Source 22.100%Not comparable
FrontierMath v2 (Tier 4)Source 2.100%Not comparable
Multimodal
BenchmarkDeepSeek V3.2LFM2.5-VL-1.6B-ExtractResult
Design Arena WebsiteSource 1204Not comparable
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%Not comparable
Inst. Following
BenchmarkDeepSeek V3.2LFM2.5-VL-1.6B-ExtractResult
AA-IFBenchSource 49.0%33.1%DeepSeek V3.2 leads
Frequently Asked Questions (3)

Can I compare DeepSeek V3.2 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.

What data is available for DeepSeek V3.2 and LFM2.5-VL-1.6B-Extract today?

DeepSeek V3.2: $0.28 input / $0.42 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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Last updated: July 23, 2026

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