Capability
39.9/100
field median 56.3
#175 of 230 ranked models
LFM2.5-2.6B
Decision reading
LFM2.5-2.6B scores 39.9 out of 100 and ranks #175 of 230. This profile shows 7 source-displayable benchmark rows; its strongest eligible category is Instruction Following at #104. Published weights can be self-hosted, but infrastructure cost varies and is not a comparable API token rate.
Released Aug 4, 2026 — see all recent releases
Data as of September 18, 2026 · How the score is built
LFM2.5-2.6B will be repriced, updated or retired. Get each notice with its source and date. Follow model changes
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Strongest published evidence
Instruction Following ranks #104. A well-rounded choice across a range of tasks.
Validate before choosing
7 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 $0.95
No comparable first-party hosted token rate
Speed
197tok/s
field median 90 tok/s
First token 12.02 s
Context
128Ktokens
field median 256,000
Maximum output length is tracked separately
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.
- Agentic4/4 verified
- Coding1/1 verified
- ReasoningNot measured
- KnowledgeNot measured
- Math1/1 verified
- MultilingualNot measured
- MultimodalNot measured
- Inst. Following1/1 verified
Verified sourceProvisionalNot measured
Category 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 |
|---|---|---|---|---|---|---|
| AgenticRank #131 of 154Percentile 15thWeight 22%4 benchmarksVerified | 37.2 | #131 of 154 | 15th | 22% | 4 benchmarks | Verified |
| CodingRank #125 of 154Percentile 19thWeight 20%1 benchmarkVerified | 39.2 | #125 of 154 | 19th | 20% | 1 benchmark | Verified |
| 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%1 benchmarkVerified | Score pending | Not ranked | Not available | 5% | 1 benchmark | Verified |
| MultilingualWeight 7%0 benchmarksNot measured | Not measured | Not ranked | Not available | 7% | 0 benchmarks | Not measured |
| MultimodalWeight 12%0 benchmarksNot measured | Not measured | Not ranked | Not available | 12% | 0 benchmarks | Not measured |
| Inst. FollowingRank #104 of 124Percentile 16thWeight 5%1 benchmarkVerified | 38.1 | #104 of 124 | 16th | 5% | 1 benchmark | Verified |
Benchmark ledger
Coding 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.
Coding1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| LiveCodeBench v6 | Score59.4% | Versus best verified row Best verified: Sakana Fugu-Ultra · 93.2% | Gap33.8 behind | WeightDisplay only | Provider exact |
Agentic4 rows
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| BFCL v4Berkeley Function Calling Leaderboard v4 | Score56.9% | Versus best verified row Best verified: BTL-3 · 88.5% | Gap31.6 behind | WeightDisplay only | Provider exact |
| τ³-bench resultsτ³-Bench Tool-Agent-User Evaluation | Score5.7% | Versus best verified row Best verified: Mercury 2.5 · 96.0% | Gap90.3 behind | WeightDisplay only | Provider exact |
| Claw-Eval | Score62.9% | Versus best verified row Best verified: Ornith-1.5-397B · 81.4% | Gap18.6 behind | WeightDisplay only | Provider exact |
| PinchBench | Score68.2% | Versus best verified row Best verified: Pokee-Isaac 28B · 95.7% | Gap27.5 behind | WeightDisplay only | Provider exact |
Math1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| AIME 2025American Invitational Mathematics Examination 2025 | Score51.9% | Versus best verified row Best verified: MAI-Thinking-1 · 97% | Gap45.1 behind | WeightDisplay only | Provider exact |
Inst. Following1 row
| Benchmark | Score | Versus best verified row | Gap | Weight | Evidence |
|---|---|---|---|---|---|
| IFBenchInstruction Following Benchmark | Score59.2% | Versus best verified row Best verified: MAI-Thinking-1 · 85% | Gap25.8 behind | WeightWeighted 70% | Provider exact |
Capability shape
Each axis shows percentile within that category’s eligible cohort. The comparison outline is the median of the six nearest public-score peers; a collapsed vertex means the category is not rank-eligible.
LFM2.5-2.6B category percentile values
- Agentic15th percentile
- Coding19th percentile
- ReasoningNot eligible
- KnowledgeNot eligible
- MathNot eligible
- MultilingualNot eligible
- MultimodalNot eligible
- Instruction following16th percentile
The dashed outline is median of 6 nearest peers.
Eligible category ranks
- Agentic#131/154
- Coding#125/154
- ReasoningNot ranked
- KnowledgeNot ranked
- MathNot ranked
- MultilingualNot ranked
- MultimodalNot ranked
- Inst. Following#104/124
Top decileTop quartileMid-fieldNot eligible
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 publishedLiquidAI Hugging Face model card
- Context window
- 128KLiquidAI Hugging Face model card
- 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
- Liquid AI publishes the 2.69B-parameter BF16 checkpoint and GGUF, ONNX, and MLX deployment formats under the LFM Open License v1.0. Liquid reports 220 output tokens/s on an Apple M5 Max and 113 tokens/s on an AMD Ryzen AI Max+ 395, with memory use under 2.5 GB; these are provider measurements, not BenchLM runtime tests.
- 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 recordLiquidAI Hugging Face model card
- 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 parametersLineage
The sequence follows explicit supersedes links. A successor's displayed score stays at least 0.1 points above its predecessor; raw benchmark rows do not move. Scores and prices remain blank when the corresponding public row or first-party rate is unavailable.
Aug 4, 2026 · you are here
LFM2.5-2.6BScore 39.9 · Price not listed
Reasoning
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.
LFM2.5-2.6B ranks #175 of 230 on the public leaderboard with a score of 39.9/100. It does not yet have enough sourced coverage for a verified position.
LFM2.5-2.6B is a open weight model with a 128K context window. It uses an explicit reasoning mode, which can improve complex problem solving while adding latency and token use.
Liquid AI publishes the 2.69B-parameter BF16 checkpoint and GGUF, ONNX, and MLX deployment formats under the LFM Open License v1.0. Liquid reports 220 output tokens/s on an Apple M5 Max and 113 tokens/s on an AMD Ryzen AI Max+ 395, with memory use under 2.5 GB; these are provider measurements, not BenchLM runtime tests.
7 of 446 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Instruction Following at #104, while its lowest eligible position is Agentic at #131. a well-rounded choice across a range of tasks.
Radar
Full release historyLFM2.5-2.6B release history
LFM2.5-2.6B released
LiquidAI · Model release
Source confirmed
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Questions
How does LFM2.5-2.6B perform overall in AI benchmarks?
LFM2.5-2.6B ranks #175 out of 230 models on the public BenchAlign leaderboard, with a score of 39.9/100. Its evidence status is Estimated, and this profile shows 7 source-displayable benchmark rows. The label describes evidence depth, not a provider quality claim; inspect category rows before choosing a workload.
Is LFM2.5-2.6B good for coding and programming?
LFM2.5-2.6B ranks #125 out of 154 eligible models for coding and programming, with a public category score of 39.2/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is LFM2.5-2.6B good for mathematics?
LFM2.5-2.6B has source-displayable benchmark coverage for mathematics, 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 LFM2.5-2.6B good for agentic tool use and computer tasks?
LFM2.5-2.6B ranks #131 out of 154 eligible models for agentic tool use and computer tasks, with a public category score of 37.2/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is LFM2.5-2.6B good for instruction following?
LFM2.5-2.6B ranks #104 out of 124 eligible models for instruction following, with a public category score of 38.1/100. Higher-ranked alternatives are available for workloads where this category decides the choice. Check the underlying rows before treating the aggregate as a workload guarantee.
Is LFM2.5-2.6B open source?
LFM2.5-2.6B 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.
Does LFM2.5-2.6B have full benchmark coverage on BenchLM?
No. LFM2.5-2.6B currently has 19 source-displayable rows across 446 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 LFM2.5-2.6B?
LFM2.5-2.6B has a documented context window of 128K. That figure is the maximum combined prompt and retained-conversation space reported for this exact model; it is not the maximum output length. The profile keeps output limits separate because providers often publish those limits independently.
Compare LFM2.5-2.6B with every tracked model490 comparisons
Last updated September 18, 2026. Runtime fields remain blank until a sourced snapshot exists.
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