Ling-2.6-1T Intelligence, Performance & Price Analysis
Model summary
Intelligence
Speed
Input Price
USD per 1M tokens
Output Price
Verbosity
Ling-2.6-1T is amongst the leading models in intelligence, but somewhat expensive when comparing to other open weight non-reasoning models of similar size. The model supports text input, outputs text, and has a 262k tokens context window.
Ling-2.6-1T scores 34 on the Artificial Analysis Intelligence Index, placing it well above average among comparable models (averaging 23). When evaluating the Intelligence Index, it generated 16M tokens, which is very verbose in comparison to the average of 11M.
Pricing for Ling-2.6-1T is $0.30 per 1M input tokens (moderately priced, average: $0.40) and $2.50 per 1M output tokens (somewhat expensive, average: $1.60). In total, it cost $95.05 to evaluate Ling-2.6-1T on the Intelligence Index.
| Reasoning | No This page shows the non-reasoning version of this model. A reasoning variant may also exist. |
|---|---|
| Input modality | Supports: text This information is still being updated |
| Output modality | Supports: text This information is still being updated |
| Context window | 262k ~393 A4 pages of size 12 Arial font |
| Total parameters | 1026B |
| Active parameters | 63B Number of parameters active per token during inference |
| License | Mit |
| Model weights | Hugging Face |
Metrics are compared against models of the same class:
- Non-reasoning models → compared only with other non-reasoning models
- Reasoning models → compared across both reasoning and non-reasoning
- Open weights models → compared only with other open weights models of the same size class:
- Tiny: ≤4B parameters
- Small: 4B–40B parameters
- Medium: 40B–150B parameters
- Large: >150B parameters
- Proprietary models → compared across proprietary and open weights models of the same price range, using a blended 3:1 input/output price ratio:
- <$0.15 per 1M tokens
- $0.15–$1 per 1M tokens
- >$1 per 1M tokens
Highlights
Intelligence
Artificial Analysis Intelligence Index
Artificial Analysis Intelligence Index by Open Weights / Proprietary
Intelligence Evaluations
Agentic real-world work tasks, (ELO-500)/2000
Agentic coding & terminal use
Agentic tool use
Long context reasoning
Knowledge
1 - hallucination rate
Reasoning & knowledge
Scientific reasoning
Coding
Instruction following
Physics reasoning
Long-horizon agentic tasks
Visual reasoning
Openness
Artificial Analysis Openness Index: Results
Intelligence Index Comparisons
Intelligence vs. Price
Intelligence Index Token Use & Cost
Output Tokens Used to Run Artificial Analysis Intelligence Index
Cost to Run Artificial Analysis Intelligence Index
Context Window
Context Window
Pricing
Pricing now includes a “Cache Hit Price” alongside Input and Output pricing, with new blend ratios.
Pricing: Cache Hit, Input, and Output
Speed
Measured by Output Speed (tokens per second)
Output Speed
Output Speed vs. Price
Latency
Measured by Time (seconds) to First Token
Latency: Time To First Answer Token
End-to-End Response Time
Seconds to output 500 tokens, calculated based on time to first token, 'thinking' time for reasoning models, and output speed
End-to-End Response Time
Model Size (Open Weights Models Only)
Model Size: Total and Active Parameters
Frequently Asked Questions
Common questions about Ling-2.6-1T
Ling-2.6-1T was released on April 23, 2026.
Ling-2.6-1T was created by InclusionAI.
Ling-2.6-1T scores 34 on the Artificial Analysis Intelligence Index, placing it well above average among other open weight non-reasoning models of similar size (median: 23).
Ling-2.6-1T costs $0.30 per 1M input tokens (very competitive, median: $0.59) and $2.50 per 1M output tokens (somewhat higher than average, median: $2.20), based on the median across providers serving the model.
Ling-2.6-1T costs $0.30 per 1M input tokens and $2.50 per 1M output tokens (based on the median across providers serving the model). For a blended rate (3:1 input to output ratio), this is $0.85 per 1M tokens. Pricing may vary by provider. Compare provider pricing
When evaluated on the Intelligence Index, Ling-2.6-1T generated 16M output tokens, which is at the higher end compared to other open weight non-reasoning models of similar size (median: 11M).
No, Ling-2.6-1T is not a reasoning model. It provides direct responses without extended chain-of-thought reasoning.
Ling-2.6-1T supports text input.
Ling-2.6-1T supports text output.
No, Ling-2.6-1T does not support image input. It can only process text.
No, Ling-2.6-1T is not multimodal. It only supports text input.
Ling-2.6-1T has a context window of 260k tokens. This determines how much text and conversation history the model can process in a single request.
Yes, Ling-2.6-1T is open weights. The model weights are publicly available and can be downloaded for self-hosting.
Ling-2.6-1T has 1.0 trillion parameters (63 billion active).
Ling-2.6-1T is a Mixture of Experts (MoE) model with 1.0 trillion total parameters, but only 63 billion active parameters are used during inference.
Ling-2.6-1T is released under the Mit license. This license allows commercial use. View license
Ling-2.6-1T achieves a score of 34 on the Artificial Analysis Intelligence Index. This composite benchmark evaluates models across reasoning, knowledge, mathematics, and coding.
Ling-2.6-1T is an open weights model that can be self-hosted. View providers
Ling-2.6-1T is an open weights model that can be downloaded and self-hosted. Compare providers