liquid · Open weights
LFM2.5 2.6B
Liquid AI's compact 2.6B reasoning model for agent workflows, data extraction, RAG, and long-context processing. It supports tool calling and structured output, but Liquid advises against using it for agentic coding. Warning: prompts and responses may be logged and used for model training or service improvement; do not send sensitive data.
- Reasoning
- Tools
- Structured output
- Input
- Text
- Output
- Text
- Reasoning effort
- minimal · low · medium · high · xhigh
- Discovery tier
- Open weights
Pricing
USD per 1M tokens- Input
- $0.10
- Output
- $0.20
Reference prices as published by the model provider. API example
Specifications
- Context window
- 128K
- Max output
- 32.8K
- Average output (7 days)
- 459 tokens
- Longer than 45% of 465 models
Side by side
LFM2.5 2.6B compared
Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.
| LFM2.5 2.6BliquidThis model | LFM2.5 24B A2BliquidRemove | |
|---|---|---|
| Provider | liquid | liquid |
| Discovery tier | Open weights | Efficient |
| Added | Aug 12, 2026 | Oct 14, 2026 |
| Limits | ||
| Context window | 128K | 128K |
| Max output | 32.77KBest | 8.19K |
| Average output | 459 tokens | 5.1K tokens |
| Pricing, USD per 1M tokens | ||
| Input | $0.10 | $0.05Best |
| Output | $0.20Best | $0.70 |
| Cache read | — | $0.025 |
| Performance | ||
| Request success (24h) | — | 100.0% |
| Benchmark indexes | ||
| Intelligence Index | 8.4 | — |
| Coding Index | 7.7 | — |
| Capabilities | ||
| Reasoning | Yes | Yes |
| Tools | Yes | Yes |
| Structured output | Yes | Yes |
| Reasoning effort | minimal, low, medium, high, xhigh | — |
Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
- LFM2.5 2.6B
- LFM2.5 24B A2B
Intelligence Index
- LFM2.5 2.6B8.4
- LFM2.5 24B A2B—
LFM2.5 2.6B: Better than 13% of 326 scored models
Indexes
Coding Index
Composite of the coding evaluations
- LFM2.5 2.6B: 7.7
- LFM2.5 24B A2B: —
Document reasoning
AA-LCRv1.1
Reasoning across long documents
- LFM2.5 2.6B: 5.7%
- LFM2.5 24B A2B: —
Reasoning and knowledge
Humanity’s Last Exam
Expert-level questions across academic fields
- LFM2.5 2.6B: 6.2%
- LFM2.5 24B A2B: —
GPQA Diamond
Graduate-level science questions
- LFM2.5 2.6B: 55.8%
- LFM2.5 24B A2B: —
Coding
SciCode
Scientific computing in Python
- LFM2.5 2.6B: 14.4%
- LFM2.5 24B A2B: —
Legacy evaluations
AA-LCR (unversioned)
Long-context reasoning, earlier grading
- LFM2.5 2.6B: 5.7%
- LFM2.5 24B A2B: —
Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis
Developers
Use it through the API
Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.
curl https://www.noumen.xyz/api/v1/chat/completions \
-H "Authorization: Bearer $NOUMEN_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "liquid/lfm-2.5-2.6b",
"reasoning_effort": "medium",
"messages": [{ "role": "user", "content": "Explain this stack trace." }]
}'import os
from openai import OpenAI
client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
model="liquid/lfm-2.5-2.6b",
messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)liquid
Related models
Other liquid models, starting with the same family.
- Efficient