Capability
Unranked
field median 57.3
Not eligible for a public rank
Model profile · LiquidAI
LFM2.5-2.6B
Released Aug 4, 2026 — see all recent releases
LFM2.5-2.6B is tracked, but not publicly ranked yet. The profile exposes 7 sourced benchmark rows and leaves unsupported fields blank until a published record exists.
Data as of August 4, 2026 · How the score is built
Strongest published evidence
Instruction Following ranks #29. 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 $1
No comparable first-party hosted token rate
Speed
Not measured
field median 92 tok/s
Time to first token not measured
Context
128Ktokens
field median 200,000
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
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 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 |
|---|---|---|---|---|---|---|
| AgenticRank #59 of 131Percentile 55thWeight 22%4 benchmarksVerified | 48.3 | #59 of 131 | 55th | 22% | 4 benchmarks | Verified |
| CodingWeight 20%1 benchmarkVerified | Score pending | Not ranked | Not available | 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 #29 of 36Percentile 20thWeight 5%1 benchmarkVerified | 43.8 | #29 of 36 | 20th | 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: Qwen3.7 Max · 75.0% | Gap18.1 behind | WeightDisplay only | Provider exact |
| τ³-bench resultsτ³-Bench Tool-Agent-User Evaluation | Score5.7% | Versus best verified row Best verified: Mistral Medium 3.5 128B · 91.4% | Gap85.7 behind | WeightDisplay only | Provider exact |
| Claw-Eval | Score62.9% | Versus best verified row Best verified: Ornith-1.0-397B · 77.1% | Gap14.2 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 65% | 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.
Aug 4, 2026 · you are here
LFM2.5-2.6BNot publicly ranked · 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.
We track LFM2.5-2.6B, but the public leaderboard excludes this profile until enough non-generated benchmark coverage is available. Only published rows appear above.
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 381 tracked benchmark slots currently have displayable evidence. Missing categories stay blank.
Its strongest eligible category is Instruction Following at #29, while its lowest eligible position is Agentic at #59. 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
Frequently asked questions
How does LFM2.5-2.6B perform overall in AI benchmarks?
LFM2.5-2.6B has 7 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 LFM2.5-2.6B good for coding and programming?
LFM2.5-2.6B has source-displayable benchmark coverage for coding and programming, 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 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 #59 out of 131 eligible models for agentic tool use and computer tasks, with a public category score of 48.3/100. 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 #29 out of 36 eligible models for instruction following, with a public category score of 43.8/100. 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 8 source-displayable rows across 381 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.
Last updated August 4, 2026. Runtime fields remain blank until a sourced snapshot exists.
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