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liquid · Open weights

LFM2.5 2.6B

Added Aug 12, 2026Open weights

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Use in the app

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.

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LFM2.5 2.6BliquidThis modelGPT-4oOpenAIRemove
ProviderliquidOpenAI
Discovery tierOpen weightsFrontier
AddedAug 12, 2026May 13, 2025
Limits
Context window128K128K
Max output32.77KBest16.38K
Average output459 tokens101 tokens
Pricing, USD per 1M tokens
Input$0.10Best$2.50
Output$0.20Best$10.00
Cache read—$1.25
Performance
Request success (24h)—100.0%
Benchmark indexes
Intelligence Index8.48.4
Coding Index7.7—
Math Index—6.0
Capabilities
ReasoningYesNo
ToolsYesYes
VisionNoYes
Structured outputYesNo
Reasoning effortminimal, 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
  • GPT-4o

Intelligence Index

  • LFM2.5 2.6B8.4
  • GPT-4o8.4

LFM2.5 2.6B: Better than 13% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • LFM2.5 2.6B: 7.7
    • GPT-4o: —

  • Math Index

    Composite of the math evaluations

    • LFM2.5 2.6B: —
    • GPT-4o: 6.0

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • LFM2.5 2.6B: 5.7%
    • GPT-4o: 49.3%Top

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • LFM2.5 2.6B: 6.2%Top
    • GPT-4o: 2.4%

  • GPQA Diamond

    Graduate-level science questions

    • LFM2.5 2.6B: 55.8%Top
    • GPT-4o: 54.3%

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • LFM2.5 2.6B: —
    • GPT-4o: 74.8%

  • IFBench

    Following precise output instructions

    • LFM2.5 2.6B: —
    • GPT-4o: 34.3%

Coding

  • SciCode

    Scientific computing in Python

    • LFM2.5 2.6B: 14.4%
    • GPT-4o: —

  • LiveCodeBench

    Recent competitive programming problems

    • LFM2.5 2.6B: —
    • GPT-4o: 30.9%

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • LFM2.5 2.6B: —
    • GPT-4o: 6.0%

  • AIME

    Competition mathematics

    • LFM2.5 2.6B: —
    • GPT-4o: 15.0%

  • MATH-500

    Competition math problems

    • LFM2.5 2.6B: —
    • GPT-4o: 75.9%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • LFM2.5 2.6B: —
    • GPT-4o: 8.3%

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • LFM2.5 2.6B: —
    • GPT-4o: 25.1%

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • LFM2.5 2.6B: 5.7%
    • GPT-4o: 49.3%Top

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.

Read the quickstart
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)

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