Model comparison

LFM2.5-230M vs SWE-1.7

Data verified July 14, 2026

Head-to-head evidence from 0 shared benchmark results across 0 categories. Overall scores shown here use BenchLM's provisional ranking lane.

Benchmark data for one or both models is coming soon. This page currently shows metadata and pricing where BenchLM has it, and score-level comparisons will populate as public benchmark results land.

LFM2.5-230M

LiquidAI

31/100

Margin

43.0pts

winning →

SWE-1.7

Cognition

74/100

0 category wins0 category wins

Evidence parity. LFM2.5-230M and SWE-1.7 share 0 comparable benchmark results. 0 of 8 categories are comparable. 6 results are unique to LFM2.5-230M; 4 to SWE-1.7.

Updated July 14, 2026

Shared results: 0
LFM2.5-230M only: 6
SWE-1.7 only: 4
Comparable categories: 0 / 8

Benchmark data for LFM2.5-230M and SWE-1.7 is coming soon on BenchLM.

Confidence note. This is a partial-evidence comparison with 0 shared benchmark results across 0 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.

SWE-1.7 has the larger context window at 256K, compared with 32K for LFM2.5-230M.

Operational comparison

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

Metric	LFM2.5-230M	SWE-1.7	Comparison
Input / output priceUSD per 1M tokens	LFM2.5-230M$0 input / $0 output	SWE-1.7Not available	A complete price comparison is not available.
Generation speedtokens per second	LFM2.5-230MNot available	SWE-1.7Not available	A complete speed comparison is not available.
First-answer latencyseconds to first token	LFM2.5-230MNot available	SWE-1.7Not available	A complete latency comparison is not available.
Context windowmaximum listed tokens	LFM2.5-230M32K	SWE-1.7256K	SWE-1.7 lists the larger context window.

Benchmark Deep Dive

Agentic

2 benchmarks

Benchmark	LFM2.5-230M	SWE-1.7	Result
BFCL v4Source	21.0%	—	Not comparable
Terminal-Bench 2.0Source	—	81.5%	Not comparable

Coding

3 benchmarks

Benchmark	LFM2.5-230M	SWE-1.7	Result
FrontierCodeSource	—	42.3%	Not comparable
Terminal-Bench 2.0Source	—	81.5%	Not comparable
SWE MultilingualSource	—	77.8%	Not comparable

Knowledge

3 benchmarks

Benchmark	LFM2.5-230M	SWE-1.7	Result
GPQASource	25.4%	—	Not comparable
GPQA-DSource	25.4%	—	Not comparable
MMLU-ProSource	20.3%	—	Not comparable

Inst. Following

2 benchmarks

Benchmark	LFM2.5-230M	SWE-1.7	Result
IFEvalSource	71.7%	—	Not comparable
IFBenchSource	38.4%	—	Not comparable

Frequently Asked Questions (3)

Can I compare LFM2.5-230M and SWE-1.7 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 LFM2.5-230M and SWE-1.7 today?

LFM2.5-230M: $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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Last updated: July 14, 2026

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