Powered by inclusionAI
Ling-2.6-1T
- Text Generation
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.
About the model
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.
Model capabilities
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.
Use cases
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
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Ling-2.6-1T–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.30 | $0.60 | 256K |
| inclusionAI | US East | ~140ms | ~70 tps | ~99.9% | ~$0.40 | ~$0.80 | ~128K |
| OpenAI | Global | ~150ms | ~80 tps | 99.9% | ~$0.50 | ~$1.00 | 128K |
| Anthropic | US West | ~160ms | ~60 tps | ~99.9% | ~$0.55 | ~$1.10 | 200K |
| Google Cloud AI | Global | ~170ms | ~65 tps | 99.9% | ~$0.45 | ~$0.90 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Ling-2.6-1T (inclusionAI) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~210ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M tokens) | $1.20 | $5.00 | $3.00 |
| Output Price ($/1M tokens) | $3.60 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 4K |
| Throughput | 60 tps | 30 tps | 4K |
| Throughput | ~80 tps | ~60 tps | ~50 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 7.8B
- Prompt tokens processed (last 30 days)
- 6.1B
- Completion tokens generated (30 days)
- 24.5M
- API requests served (30 days)
- 99.8%
- Average uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
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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.
Decision guide
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.
FAQ
Frequently Asked Questions
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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.
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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.