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
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Public evaluations
Benchmarks
Independent evaluation results, with how each score ranks among the models in the Noumen catalog.
Intelligence Index
8.4
Better than 13% of 326 scored models
Indexes
Coding Index
7.7Composite of the coding evaluations
Better than 3% of 168 scored models
Document reasoning
AA-LCRv1.1
5.7%Reasoning across long documents
Better than 3% of 309 scored models
Reasoning and knowledge
Humanity’s Last Exam
6.2%Expert-level questions across academic fields
Better than 21% of 323 scored models
GPQA Diamond
55.8%Graduate-level science questions
Better than 13% of 291 scored models
Coding
SciCode
14.4%Scientific computing in Python
Better than 0% of 174 scored models
Legacy evaluations
AA-LCR (unversioned)
5.7%Long-context reasoning, earlier grading
Better than 3% of 309 scored models
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
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