Ling-2.6-1T
Ling-2.6-1T is inclusionAI’s trillion-parameter flagship instruction model optimized for fast, efficient execution in real-world agentic, coding, and complex reasoning workflows.
What is Ling-2.6-1T?
Ling-2.6-1T is a 1-trillion-parameter flagship language model from inclusionAI designed as a high-efficiency instant/instruct model for complex real-world tasks. It is mainly used for advanced coding, large-scale agent workflows, and long-context applications that require both strong reasoning and high throughput. It is also used for everyday language tasks such as writing, summarization, and explanation where low latency and tool use/structured outputs are important. Ling-2.6-1T belongs to the Ling 2.6 family of open-weight models, alongside variants like Ling-2.6-Flash and the reasoning-focused Ring-2.6-1T.
Providers
Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).
| Provider | Input | Output | Cache read /M | Latency | Throughput | Uptime |
|---|---|---|---|---|---|---|
| inclusionAI | ~$0.40 | ~$0.80 | — | ~140ms | ~70 tps | ~99.9% |
| OpenAI | ~$0.50 | ~$1.00 | — | ~150ms | ~80 tps | 99.06% |
| Anthropic | ~$0.55 | ~$1.10 | — | ~160ms | ~60 tps | 99.28% |
| Google Cloud AI | ~$0.45 | ~$0.90 | — | ~170ms | ~65 tps | 99.9% |
Try this model
Test Ling-2.6-1T right here — free to start.
Suggestions for your first prompt
Code snippet
Call the model through the OpenAI-compatible API.
from openai import OpenAI
client = OpenAI(
api_key="YOUR_API_KEY",
base_url="https://inference.example.com/v1"
)
response = client.chat.completions.create(
model="inclusionai/ling-2-6-1t",
messages=[
{
"role": "user",
"content": "Describe this image in one sentence."
}
],
)
print(response.to_json())
{
"model": "inclusionai/ling-2-6-1t",
"messages": [
{
"role": "user",
"content": "Describe this image in one sentence."
}
]
}
5 Core Capabilities
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Conversational Assistance
Engages in multi-turn, context-aware chat, answering questions, following instructions, and maintaining coherent dialogue across various topics.
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Multilingual Translation
Translates text between multiple languages, preserving meaning and tone for general-purpose content and everyday communication.
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Text Interpretation
Understands and summarizes written content, extracting key points, intent, and sentiment from diverse text sources.
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Visual Recognition
Analyzes images to recognize objects, people, and scenes, generating concise descriptions of visual content.
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Document OCR
Extracts machine-readable text from scanned documents and photos of text, enabling downstream search, editing, and analysis.
6 Most Valuable Use Cases
- Agentic Workflows Orchestration
- Advanced Code Generation
- Complex Reasoning Tasks
- Long-Context Document Analysis
- Scalable Production Assistants
- Structured Tool-Using Agents
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Smarter Model Routing
Automatically send each request to the best-fit model across providers based on latency, cost, or quality—without changing your integration or redeploying code.
One API, any model. -
Cost-Aware Orchestration
Optimize spend with policy-based routing, budget guards, and granular usage controls so you can experiment freely without surprise bills or vendor lock-in.
Max control, minimal spend. -
Resilient Fallback Flows
Define automatic failover and degradation paths when a provider is down, slow, or rate-limited so your production workloads stay online and predictable.
Fail gracefully, not silently. -
Full-Stack Observability
Get unified logs, traces, metrics, and structured payloads across all providers to debug prompts, compare models, and tune performance from one place.
See every token, everywhere. -
Task-Level Abstractions
Define high-level tasks like chat, embeddings, tools, or RAG once, then swap underlying models and vendors without touching application logic.
Code to tasks, not models. -
High-Throughput Batch Jobs
Run large-scale batch workloads with queueing, concurrency control, and automatic retries so you can process millions of tasks reliably and cost-efficiently.
From prototype to millions.
When to Use — When NOT to Use
Use it if...
- You need a general-purpose mid-sized language model for everyday application backends.
- You need cost-effective inference for chatbots, helpers, or basic task automation.
- You need to prototype features quickly without relying on frontier-scale proprietary models.
- Your use case involves summarizing short to medium-length documents and knowledge snippets.
- Your use case involves classification, tagging, or routing of user text inputs.
- You need an English-first model for instructions, simple reasoning, and content generation.
Avoid if...
- You need cutting-edge reasoning or performance comparable to the very latest frontier models.
- Your workload requires guaranteed low latency at massive scale with strict SLAs.
- You need highly specialized domain performance validated by extensive benchmarks and certifications.
- You need strong multimodal capabilities like image, audio, or video understanding and generation.
- Your workload requires very long-context processing of hundreds of pages in a single call.
- You need battle-tested ecosystem integrations, tooling, and broad community support today.
Frequently Asked Questions
-
What is Ling-2.6-1T?
Ling-2.6-1T is a large language model from inclusionAI focused on high-quality text generation and reasoning, accessible through the LLM.API unified gateway.
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What is Ling-2.6-1T best suited for?
Ling-2.6-1T is best for complex reasoning, multi-step data processing, and robust code and text generation across a wide range of developer use cases.
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What is the context window of Ling-2.6-1T?
Ling-2.6-1T supports a context window of up to 32,000 tokens for combined input and output through LLM.API.
-
What modalities does Ling-2.6-1T support via LLM.API?
Ling-2.6-1T currently supports text-in, text-out interactions only when accessed through LLM.API.
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How is Ling-2.6-1T priced on LLM.API?
Ling-2.6-1T uses a pay-per-token billing model on LLM.API, with separate input and output token rates defined in your LLM.API pricing plan.
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How fast is Ling-2.6-1T in typical LLM.API requests?
Typical end-to-end latencies for Ling-2.6-1T are usually in the low-seconds range, depending on prompt size and concurrent load.
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How do I call Ling-2.6-1T through the LLM.API?
You specify the model name "inclusionai/ling-2.6-1T" in your LLM.API completion or chat request, plus your API key and usual parameters.
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How does Ling-2.6-1T compare to similar large models?
Ling-2.6-1T aims to balance strong reasoning and generation quality with more predictable costs than many similarly sized frontier models.
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What are the main limitations of Ling-2.6-1T?
Ling-2.6-1T can hallucinate facts, reflect training-data biases, and should not be relied on for safety-critical or legally binding decisions.
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Can Ling-2.6-1T handle streaming responses on LLM.API?
Yes, Ling-2.6-1T supports token streaming on LLM.API when you enable the streaming option in your request parameters.
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-
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