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Model comparison

LFM2.5-VL-1.6B-Extract vs Muse Spark

Data verified

Head-to-head evidence from 12 shared benchmark results across 6 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.

N/A
No comparison
71.04/100
0 category wins0 category wins

Public leaderboard positions: LFM2.5-VL-1.6B-Extract unranked (Not scored); Muse Spark #13 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.

Evidence parity. LFM2.5-VL-1.6B-Extract and Muse Spark share 12 comparable benchmark results. 0 of 8 categories are comparable. 3 results are unique to LFM2.5-VL-1.6B-Extract; 27 to Muse Spark.

Updated July 23, 2026
Shared results
12
LFM2.5-VL-1.6B-Extract only
3
Muse Spark only
27
Comparable categories
0 / 8

Benchmark data for LFM2.5-VL-1.6B-Extract and Muse Spark 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.

Muse Spark 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 scores and score margins for LFM2.5-VL-1.6B-Extract and Muse Spark
CategoryLFM2.5-VL-1.6B-ExtractΔMuse Spark
AgenticLFM2.5-VL-1.6B-ExtractNot measuredMarginNo overlapMuse Spark59.0
CodingLFM2.5-VL-1.6B-ExtractNot measuredMarginNo overlapMuse Spark67.8
ReasoningLFM2.5-VL-1.6B-ExtractNot measuredMarginNo overlapMuse Spark42.5
KnowledgeLFM2.5-VL-1.6B-ExtractNot measuredMarginNo overlapMuse Spark50.4
MathLFM2.5-VL-1.6B-ExtractNot measuredMarginNo overlapMuse Spark32.9
MultimodalLFM2.5-VL-1.6B-ExtractNot measuredMarginNo overlapMuse Spark82.5

Operational comparison

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

MetricLFM2.5-VL-1.6B-ExtractMuse SparkComparison
Input / output priceUSD per 1M tokensLFM2.5-VL-1.6B-ExtractNot availableMuse SparkNot availableA complete price comparison is not available.
Generation speedtokens per secondLFM2.5-VL-1.6B-ExtractNot availableMuse SparkNot availableA complete speed comparison is not available.
First-answer latencyseconds to first tokenLFM2.5-VL-1.6B-ExtractNot availableMuse SparkNot availableA complete latency comparison is not available.
Context windowmaximum listed tokensLFM2.5-VL-1.6B-Extract128KMuse Spark262KMuse Spark lists the larger context window.

Benchmark Deep Dive

Agentic
BenchmarkLFM2.5-VL-1.6B-ExtractMuse SparkResult
τ²-bench resultsSource 8.5%91.5%Muse Spark leads
Terminal-Bench 2.0Source —59%Not comparable
DeepSearchQASource —74.8%Not comparable
CyberGymSource —43.5%Not comparable
Claw-EvalSource —63.8%Not comparable
AA Agentic IndexSource —28.7%Not comparable
GDPval-AASource —32.2%Not comparable
GDPval-AASource —1144Not comparable
Coding
BenchmarkLFM2.5-VL-1.6B-ExtractMuse SparkResult
AA-SciCodeSource 3.0%51.5%Muse Spark leads
SWE-bench VerifiedSource —77.4%Not comparable
SWE-bench ProSource —52.4%Not comparable
LiveCodeBench ProSource —80.0%Not comparable
Vibe Code BenchSource —19.67%Not comparable
AA Coding IndexSource —58.6%Not comparable
Reasoning
BenchmarkLFM2.5-VL-1.6B-ExtractMuse SparkResult
AA-LCRSource 0.0%69.7%Muse Spark leads
CritPtSource 0.0%11.3%Muse Spark leads
ARC-AGI-2Source —42.5%Not comparable
Knowledge
BenchmarkLFM2.5-VL-1.6B-ExtractMuse SparkResult
Artificial Analysis Intelligence IndexSource 1.0%43.1%Muse Spark leads
AA-GPQA DiamondSource 28.9%88.4%Muse Spark leads
AA-HLESource 5.1%39.9%Muse Spark leads
AA-Omniscience IndexSource -83.9%4.1%Muse Spark leads
AA-Omniscience AccuracySource 5.2%44.6%Muse Spark leads
AA-Omniscience Hallucination RateSource 94.0%73.2%Muse Spark leads
GPQA-DSource —89.5%Not comparable
HLESource —50.4%Not comparable
HLE w/o toolsSource —42.8%Not comparable
HealthBench HardSource —42.8%Not comparable
MedXpertQA (Text)Source —52.6%Not comparable
Math
BenchmarkLFM2.5-VL-1.6B-ExtractMuse SparkResult
FrontierMath v2 (Tiers 1-3)Source —39.000%Not comparable
FrontierMath v2 (Tier 4)Source —14.600%Not comparable
Multimodal
BenchmarkLFM2.5-VL-1.6B-ExtractMuse SparkResult
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%80.5%Muse Spark leads
CharXivSource —86.4%Not comparable
MMMU-ProSource —80.4%Not comparable
ERQASource —64.7%Not comparable
SimpleVQASource —71.3%Not comparable
ScreenSpot ProSource —84.1%Not comparable
ZeroBenchSource —33.0%Not comparable
MedXpertQA (MM)Source —78.4%Not comparable
Inst. Following
BenchmarkLFM2.5-VL-1.6B-ExtractMuse SparkResult
AA-IFBenchSource 33.1%75.9%Muse Spark leads
Frequently Asked Questions (2)

Can I compare LFM2.5-VL-1.6B-Extract and Muse Spark 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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Last updated: July 23, 2026

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