Capability
Unranked
field median 57.3
Not eligible for a public rank
Model profile · LiquidAI
LFM2.5-Embedding-350M
LFM2.5-Embedding-350M is tracked, but not publicly ranked yet. The profile exposes 2 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
Data as of August 2, 2026 · How the score is built
Strongest published evidence
Published rows are visible, but no category has enough eligible evidence for a comparative rank.
Validate before choosing
2 published rows leave some tracked benchmark slots empty. No comparable first-party API token rate is published.
Decision snapshot
Each value carries a field reference instead of floating alone. Markers compare this model with the current ranked and priced catalog; they are not absolute quality thresholds.
Price
Self-hosted; infrastructure cost varies
input median $1
No comparable first-party hosted token rate
Speed
Not measured
field median 108 tok/s
Time to first token not measured
Context
32Ktokens
field median 256,000
Reported for this model; direct source link not stored
How much of this is verified
Coverage is split by category so a strong number never hides a thin evidence base. Verified means the row is tied to a published source; provisional rows remain visible but separate.
- AgenticNot measured
- CodingNot measured
- ReasoningNot measured
- KnowledgeNot measured
- MathNot measured
- Multilingual2/2 verified
- MultimodalNot measured
- Inst. FollowingNot measured
Verified sourceProvisionalNot measured
Spec sheet
Each documented value carries its source. Missing fields stay visible as not sourced or not published, rather than disappearing from the page.
- API model ID
- Not published
- Context window
- 32K
- Maximum output
- Not sourced yet
- Knowledge cutoff
- Not sourced yet
- Input modalities
- Not sourced yet
- Output modalities
- Not sourced yet
- Parameters
- Not sourced yet
- Availability
- Not sourced yet
- Cloud regions
- Not tracked yet
- Lifecycle
- Current
- API capabilities
- Tool calling, structured outputs, and batch support are not tracked yet
- Prompt caching
- Not documented in the pricing record
- Self-host
- Open weights available; hardware estimate not sourced
- Rate limits
- Not tracked yet
Deployment options
Self-host and provider-specific paths stay separate from benchmark evidence so operating constraints are visible before a score becomes the whole decision.
Published weights are available, but BenchLM does not yet have a sourced parameter and VRAM profile for this exact model. Hardware cost estimates stay unavailable until that sizing record is complete.
Estimate VRAM from known parametersCategory score record
Scores and ranks appear only where published evidence can be displayed. The table keeps the score, weight, cohort, and evidence state together.
| Category | Score | Rank | Percentile | Weight | Benchmarks | Evidence |
|---|---|---|---|---|---|---|
| AgenticWeight 22%0 benchmarksNot measured | Not measured | Not ranked | Not available | 22% | 0 benchmarks | Not measured |
| CodingWeight 20%0 benchmarksNot measured | Not measured | Not ranked | Not available | 20% | 0 benchmarks | Not measured |
| ReasoningWeight 17%0 benchmarksNot measured | Not measured | Not ranked | Not available | 17% | 0 benchmarks | Not measured |
| KnowledgeWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| MathWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
| MultilingualWeight 7%2 benchmarksVerified | Score pending | Not ranked | Not available | 7% | 2 benchmarks | Verified |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingWeight 5%0 benchmarksNot measured | Not measured | Not ranked | Not available | 5% | 0 benchmarks | Not measured |
Benchmark ledger
Multilingual opens by default. The marker compares each value with the best source-verified result in the catalog; provisional leaders do not set the reference. Expand the remaining categories for every published row.
Multilingual2 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| NanoBEIR MultilingualNanoBEIR Multilingual Extended | Score57.7% | Versus best verified row Best verified: LFM2.5-ColBERT-350M · 60.5% | Gap2.8 behind | WeightDisplay only | Provider exact |
| MKQA-11MKQA-11 multilingual retrieval | Score69.1% | Versus best verified row Best verified: LFM2.5-ColBERT-350M · 69.4% | Gap0.3 behind | WeightDisplay only | Provider exact |
Lineage
The sequence follows explicit supersedes links. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Jun 18, 2026 · you are here
LFM2.5-Embedding-350MNot publicly ranked · Price not listed
Embedding
How to read this profile
The visual layer above carries the decisions. These notes preserve the model, ranking, coverage, and family context behind the numbers.
We track LFM2.5-Embedding-350M, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.
LFM2.5-Embedding-350M is a open weight model with a 32K context window. No explicit reasoning mode is documented in this profile.
Official exact-value snapshot from Liquid AI's June 18, 2026 LFM2.5 Retrievers launch post and matching Hugging Face model cards. BenchLM stores the NanoBEIR Multilingual Extended NDCG@10 and MKQA-11 Recall@20 averages as display-only multilingual retrieval rows because these models are retrievers/embedding models, not general chat LLMs.
LFM2.5-Embedding-350M sits in the LFM2.5 Retrievers family with LFM2.5-ColBERT-350M. 2 of 376 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Radar
Full release historyLFM2.5-Embedding-350M release history
LFM2.5 Retrievers released
LiquidAI · Model release
Source confirmed
Frequently asked questions
How does LFM2.5-Embedding-350M perform overall in AI benchmarks?
LFM2.5-Embedding-350M has 2 source-displayable benchmark rows, but it does not qualify for a public overall rank. The available rows remain visible by category without being converted into a site-wide score. Missing evidence stays blank instead of being estimated from an earlier model.
Is LFM2.5-Embedding-350M good for multilingual tasks?
LFM2.5-Embedding-350M has source-displayable benchmark coverage for multilingual tasks, but the public category table does not assign it a rank there. The individual rows remain available for inspection. A missing category position means the evidence threshold was not met; it does not convert the model's unmeasured work into a zero.
Is LFM2.5-Embedding-350M open source?
LFM2.5-Embedding-350M is an open-weight model from LiquidAI. Its weights can be downloaded for local or hosted deployment, subject to the published license. Open weight does not automatically mean open source: training data and training code may remain private, and commercial restrictions can still apply.
Which sibling models are related to LFM2.5-Embedding-350M?
LFM2.5-Embedding-350M belongs to the LFM2.5 Retrievers family. Related tracked variants include LFM2.5-ColBERT-350M. A sibling link indicates shared lineage or a documented configuration relationship; it does not mean the variants have identical pricing, context limits, benchmark evidence, or deployment behavior. Compare before switching.
Does LFM2.5-Embedding-350M have full benchmark coverage on BenchLM?
No. LFM2.5-Embedding-350M currently has 2 source-displayable rows across 376 tracked benchmark slots. The profile exposes published, non-generated evidence and leaves missing categories blank until an exact evaluation is available. Coverage describes how much was measured; it is not a penalty added to an individual benchmark result.
What is the context window size of LFM2.5-Embedding-350M?
LFM2.5-Embedding-350M has a reported context window of 32K in the exact-model catalog record. The value stays visible, but the profile marks its source link as unavailable instead of presenting it as directly documented. Maximum output length remains separate because providers often publish a different limit.
Last updated August 2, 2026. Runtime fields remain blank until a sourced snapshot exists.
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