Ling-2.6-flash (free)
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inclusionAIinclusionAILingFreeReleased 2026-04-21

Ling-2.6-flash (free)

262K contextFreeFree104B total, 7.4B active

Ling-2.6-flash is an instant instruct model developed by inclusionAI, featuring 104 billion total parameters and 7.4 billion active parameters. It is optimized for real-world agents requiring fast responses, strong execution, and high token efficiency. The model delivers performance comparable to state-of-the-art models at a similar scale while significantly reducing token usage, making it suitable for coding, document processing, and lightweight agent workflows.

What is Ling-2.6-flash (free)?

Ling-2.6-flash (free) is an AI model from inclusionAI that Agent Mag tracks for pricing, context window, modalities, benchmarks, and API compatibility. Builders can use this page to compare Ling-2.6-flash (free) against other models for agent workflows and production deployments.

Model ID

Ling-2.6-flash is an instant instruct model developed by inclusionAI, featuring 104 billion total parameters and 7.4 billion active parameters. It is optimized for real-world agents requiring fast responses, strong execution, and high token efficiency. The model delivers performance comparable to state-of-the-art models at a similar scale while significantly reducing token usage, making it suitable for coding, document processing, and lightweight agent workflows.

Architecture & Specifications
Parameters
104B total, 7.4B active
Tokenizer
Other
Released
2026-04-21
Modalities
Input
text
Output
text
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Strengths
  • Fast response times
  • Strong execution capabilities
  • High token efficiency
  • Performance comparable to state-of-the-art models
  • Optimized for lightweight agent workflows
Limitations
  • Limited information on training data
  • No mention of architecture specifics
  • Benchmarks indicate weaknesses in research-level physics reasoning (CritPt: 0.0%)
  • Lower performance in economically valuable tasks (GDPval-AA: 14.2%)
  • Moderate accuracy in omniscience-related tasks (AA-Omniscience Accuracy: 15.4%)
Recommended Use Cases
Coding tasks
Document processing
Lightweight agent workflows
Scientific computing with Python
Conversational AI in dual-control scenarios

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Data enriched Apr 24, 2026. Pricing from OpenRouter API.