Ling-2.6-1T (free)
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inclusionAIinclusionAILingFreeReleased 2026-04-23

Ling-2.6-1T (free)

262K contextFreeFree1T

Ling-2.6-1T is an instruct model developed by inclusionAI as part of their trillion-parameter flagship. It is designed for real-world agents requiring fast execution and high efficiency at scale, utilizing a 'fast thinking' approach to reduce costs while maintaining top-tier performance. The model excels in advanced coding, complex reasoning, and large-scale agent workflows, achieving state-of-the-art results on benchmarks such as AIME26 and SWE-bench Verified.

What is Ling-2.6-1T (free)?

Ling-2.6-1T (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-2.6-1T (free) against other models for agent workflows and production deployments.

Model ID

Ling-2.6-1T is an instruct model developed by inclusionAI as part of their trillion-parameter flagship. It is designed for real-world agents requiring fast execution and high efficiency at scale, utilizing a 'fast thinking' approach to reduce costs while maintaining top-tier performance. The model excels in advanced coding, complex reasoning, and large-scale agent workflows, achieving state-of-the-art results on benchmarks such as AIME26 and SWE-bench Verified.

Architecture & Specifications
Parameters
1T
Tokenizer
Other
Released
2026-04-23
Modalities
Input
text
Output
text
Supported Parameters
frequency_penaltymax_tokenspresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_p
Strengths
  • Fast execution and high efficiency at scale
  • State-of-the-art performance on benchmarks
  • Advanced coding capabilities
  • Complex reasoning abilities
  • Optimized for large-scale agent workflows
Limitations
  • Limited information on training data sources
  • No mention of specific architecture details
  • Potentially high error rates in structured outputs and tool calls
  • Low performance in research-level physics reasoning (CritPt: 0.3%)
  • Moderate accuracy in omniscience tasks (21.4%)
Recommended Use Cases
Advanced coding tasks
Complex reasoning scenarios
Large-scale 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.