Ant Ling flagship general model
Ling 2.6 1T
Fast execution for complex, long-context work
Ling 2.6 1T is a trillion-parameter Mixture-of-Experts model built for coding, multi-step agent tasks, long documents, and production workflows without relying on verbose reasoning traces.
Review the official model guideLing 2.6 1T model facts
~1T
Total parameters
Approximately 63B parameters are activated per token.
256K
Hosted API context
The official API currently exposes a 256K context window.
1M
Native context capability
The released model is designed for up to a 1M context window.
MIT
Open-source license
The published instruction model is available under the MIT license.
How Ling 2.6 1T is constructed
The official release describes a sparse Mixture-of-Experts model that preserves trillion-parameter capacity without activating the full network for every token. Its architecture and post-training target long-context efficiency and lower output overhead.
Sparse capacity with 63B active parameters
The model contains approximately one trillion total parameters while activating about 63 billion for each token. Expert routing directs a token toward a relevant subset of the network, so the full parameter count should not be interpreted as the computation used for every inference step.
MLA and Linear Attention in one hybrid design
Ling combines Multi-head Latent Attention with Linear Attention. InclusionAI reports lower latency and VRAM pressure for long contexts while retaining model expressivity. Hosted context limits still depend on the serving platform.
Fast thinking instead of verbose reasoning traces
The model card attributes lower token overhead to Contextual Process Redundancy Suppression during post-training. Its goal is to reduce unnecessary chain-of-thought verbosity. This is a model behavior objective, not an adjustable reasoning-effort control for Ling API callers.
The supported model has changed
Ling-1T requests stopped being served after its official retirement. Existing API keys and account balances remain valid here, but requests must now use the exact model ID Ling-2.6-1T.
Retired
Ling-1T
Supported
Ling-2.6-1T
What Ling 2.6 1T is built to do
The model combines a sparse MoE backbone with hybrid attention for demanding execution workloads. Ant Ling positions it for complex tasks, long documents, coding, and Agent task chains.
Coding and project delivery
Generate code, investigate defects, and carry implementation tasks through multiple steps. The official materials specifically identify code completion, project delivery, and bug fixing as intended uses.
Agent and tool workflows
Follow multi-constraint instructions and coordinate tool calls across longer task chains. The official guide names Claude Code, OpenCode, and OpenClaw among compatible Agent environments.
Long-context analysis
Review long documents, knowledge bases, and large code contexts. Ant Ling reports retrieval across information placed near the beginning, middle, or end, and lists legal review, literature synthesis, and codebase comprehension as representative scenarios.
Reports and visual deliverables
Plan and produce reports, presentations, and data visualization projects. These are named use cases in the release materials, but factual accuracy and source quality still require application-level validation.
Flagship intelligence with lower token overhead
Ling 2.6 1T uses a hybrid MLA and Linear Attention architecture. InclusionAI describes its post-training approach as fast thinking: the model aims to complete tasks directly instead of producing unnecessarily long reasoning chains.
- Approximately 1 trillion total parameters with about 63B activated per token.
- The official model card reports lower long-context latency and VRAM use from the hybrid attention architecture.
- Published evaluations emphasize execution-heavy benchmarks for coding, instruction following, and tool use.
- Compatible with mainstream agent environments including Claude Code, OpenCode, and OpenClaw.
When to choose Ling 2.6 1T
Ant Ling offers a flagship 1T model and a smaller flash model. Its selection guide favors the flagship when context length, multi-step execution, or complex constraints matter more than inference cost.
Choose the flagship for demanding workflows
Use the 1T model as the primary candidate for ultra-long documents, multi-hop work, large codebase comprehension, or Agent task chains. Benchmark the complete workflow, including tool selection and final-task success, rather than judging only a short answer.
Evaluate flash for routine, high-throughput work
Ant Ling recommends Ling-2.6-flash for many general and latency-sensitive online scenarios. It has fewer activated parameters and a 256K native context window. Evaluate flash for suitable traffic instead of routing every prompt to the largest model.
Where Ling 2.6 1T fits best
Software engineering
Repository comprehension, code generation, bug fixing, review, and multi-file delivery.
Document intelligence
Contract review, research synthesis, policy analysis, and knowledge-base workflows.
Operational agents
Multi-tool automations that require stable instruction following across several actions.
Structured deliverables
Reports, slides, implementation plans, and data-backed presentation workflows.
Call Ling 2.6 1T with familiar APIs
ling-1t.ai keeps its OpenAI-compatible chat completions endpoint and Claude-style messages endpoint. Only the model ID changes; legacy Ling-1T IDs return a 400 error.
Required model ID
Ling-2.6-1T- OpenAI-compatible chat completions with SSE streaming
- Claude-style non-streaming messages endpoint
- Usage metering and the existing prepaid account balance
- Explicit rejection of retired and unsupported model IDs
curl https://ling-1t.ai/api/v1/chat/completions \
-H "Authorization: Bearer $LING1T_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "Ling-2.6-1T",
"messages": [
{ "role": "user", "content": "Review this implementation plan." }
],
"stream": true
}'Important capability boundaries
Native and hosted context are different
The model is designed for up to 1M context, while the official hosted API currently exposes 256K. Requests through this service are also subject to its configured safety limits.
Reasoning effort belongs to Ring
The high and xhigh reasoning.effort controls documented by Ant Ling apply to Ring-2.6-1T, not Ling-2.6-1T.
Evaluation is still required
Published benchmark results do not guarantee correctness for a specific production workload. Validate factuality, safety, latency, and tool behavior before release.
Hosted APIs impose operating limits
Ant Ling documents a global maximum of 2 requests per second and a 90-second timeout for synchronous calls, recommending streaming for longer generations. ling-1t.ai also applies its own concurrency, token, content-size, and balance safeguards.
How to evaluate Ling 2.6 1T responsibly
Evaluate with representative inputs, explicit success criteria, and production-like constraints. Published benchmarks cannot establish quality, latency, or cost for a particular application.
01
Build a workload-specific test set
Include ordinary cases, difficult cases, long inputs, ambiguous instructions, and known product failures. For coding, measure whether changes compile and pass tests. For documents, verify citations and extracted facts against the supplied sources.
02
Measure execution, not presentation alone
Score task completion, instruction compliance, valid tool arguments, recovery after tool errors, and consistency across repeated runs. A fluent response is not evidence that an Agent completed every action or respected every constraint.
03
Test the actual serving path
Record time to first token, total latency, token use, retry behavior, and rate-limit handling through the production API path. Streaming can avoid the synchronous timeout, but clients must still handle interrupted streams and partial output safely.
Ling 2.6 1T FAQ
Build with Ling 2.6 1T
Use the online chat for evaluation, then move the same workloads to the metered API when you are ready to integrate.