Ant Ling flagship reasoning model
Ring 2.6 1T
Controllable reasoning for long-horizon agent work
Ring 2.6 1T is an open trillion-parameter reasoning model developed for complex analysis, coding agents, tool-centered workflows, and tasks that must continue across many steps. Its high and xhigh effort levels let applications trade reasoning depth against token use, latency, and cost.
Review the official Ring model guideRing 2.6 1T model facts
~1T
Total parameters
The shared 2.6 Mixture-of-Experts backbone is in the trillion-parameter class.
~63B
Active parameters
The official Ring guide reports approximately 63B parameters activated per token.
256K
Context window
The official model guide lists 256K context for long documents, code, and reasoning traces.
high / xhigh
Reasoning effort
High is the production default; xhigh allocates more reasoning space to difficult tasks.
Inside the Ring 2.6 1T architecture
Ring shares the Ling 2.6 base, then adds reasoning- and agent-focused post-training. The technical report attributes results to architecture, training environments, and serving systems together.
Sparse MoE capacity
The trillion-parameter backbone routes each token through about 63B active parameters. The report describes 256 routed experts per MoE layer with eight selected per token, so total size is not per-token compute.
7:1 hybrid attention
Each group pairs seven Lightning Attention layers with one Multi-head Latent Attention layer. Linear attention reduces long-sequence cost while MLA compresses the key-value cache. Scaling experiments selected this balance of quality and inference cost.
Agent-native reinforcement learning
KPop replaces an IcePop fixed-ratio constraint with binary KL divergence and works with asynchronous reinforcement learning. Decoupled rollouts and updates make long coding, search, tool, and workflow trajectories more tractable.
How Ring 2.6 1T reasoning effort works
Reasoning effort is a request control, not another model ID. The API accepts high and xhigh only for Ring-2.6-1T, with high as the default. Choose by measured task difficulty.
highProduction defaultHigh targets frequent agent workflows, multi-turn coordination, task decomposition, and tool collaboration. Official materials position it as the lower-overhead production default and the sensible starting point for evaluation.
xhighExtended reasoning budgetXhigh gives difficult mathematics, scientific analysis, logic, and multi-path exploration more room. It can consume significantly more tokens, so measured quality must justify the added latency and cost.
Xhigh leads several reasoning evaluations, while high is stronger on some function-calling results. Test both on the same tasks.
What Ring 2.6 1T is designed to execute
The target is complete work: planning, state tracking, recovery, and correct actions across a trajectory.
Repository-scale coding
Candidate tasks include diagnosis, multi-file changes, test-driven repair, and iterative engineering. SWE-bench uses a Claude Code scaffold, so the score reflects both model and environment.
Tools and business workflows
Environment-grounded training covers tools and workflows. Applications still need schemas, permissions, retries, state management, and independent verification.
Search and research analysis
Xhigh targets multi-hop research, mathematics, and difficult logic. Search quality still depends on retrieval and available tools; reasoning cannot repair missing evidence.
Reading Ring 2.6 1T benchmark results carefully
The June 2026 report covers reasoning, OpenClaw, coding, search, and function calling. Every score belongs to a named mode and protocol.
87.60
PinchBench, high
Average over three runs in the report's OpenClaw evaluation.
74.00
SWE-bench Verified, high
Resolved rate reported with Claude Code used as the scaffold.
66.18
ARC-AGI-2, xhigh
Pass@2 under the report's extended reasoning configuration.
77.90
GAIA-2 Search, xhigh
Pass@1, measured across three search evaluation runs.
These author-reported results do not establish a universal ranking, independent reproduction, latency guarantee, or success rate under different prompts, tools, data, or environments.
From Ring 2.5 to Ring 2.6 1T
The Ring-2.5-1T article is historical context, not direct evidence for 2.6. It documented TinyOS and OpenClaw demonstrations while acknowledging weaknesses in token efficiency, instruction following, and long-horizon delivery.
- Ring 2.5 introduced Hybrid Linear Attention and 128K context.
- Ring 2.6 documents 256K context plus high and xhigh effort.
- KPop replaces the earlier IcePop fixed-ratio constraint with binary KL divergence.
- Only 2.6 documentation and evaluations support current capability claims here.
When to choose Ring 2.6 1T
Ant Ling separates instant Ling models from deeper-thinking Ring. Route by measured workload needs.
Ring-2.6-1T
Choose Ring for deliberate execution
Use Ring for complex reasoning, long-horizon agents, difficult coding, and research. Start with high; promote only cases that benefit from xhigh.
Ling-2.6-1T
Choose Ling for direct responses
Use Ling when fast, token-efficient answers matter more than extended reasoning. The models share a base but differ in post-training and request controls.
Practical Ring 2.6 1T workloads
Good candidates have objective completion criteria and enough complexity to benefit from planning or extended reasoning.
Engineering agents
Repository investigation, implementation, debugging, test execution, and review across several iterations.
Research workflows
Multi-source analysis, literature synthesis, difficult mathematics, and evidence-backed technical comparison.
Process automation
Structured business procedures that combine tool calls, changing state, validation, and recovery paths.
Long-context analysis
Large codebases, long documents, and multi-stage decisions that must retain earlier constraints.
Call Ring 2.6 1T through the API
Use the exact model ID and optional reasoning object. High is the default; select xhigh only when deeper reasoning merits extra tokens.
Required model ID
Ring-2.6-1T- OpenAI-compatible reasoning.effort with high or xhigh
- Claude-style output_config.effort on the Messages endpoint
- $0.08 input and $0.65 output per million compute units
- One prepaid balance shared with the other supported models
curl https://ling-1t.ai/api/v1/chat/completions \
-H "Authorization: Bearer $LING1T_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "Ring-2.6-1T",
"messages": [
{ "role": "user", "content": "Analyze this multi-step migration plan." }
],
"reasoning": { "effort": "high" },
"stream": true
}'Open weights and Ring 2.6 1T deployment
The MIT-licensed checkpoint includes SGLang guidance and BF16 or FP8 paths. A trillion-parameter checkpoint requires distributed infrastructure, compatible kernels, capacity planning, and validation.
- Official Hugging Face and ModelScope checkpoints are available.
- The model card lists 128K extended to 256K with YaRN.
- SGLang examples show multi-node startup, not a universal minimum.
- Hosted and self-hosted behavior can differ by limits, quantization, prompts, and runtime.
Ring 2.6 1T capability boundaries
The official report identifies unresolved reliability and evaluation gaps that product controls must address.
Long-horizon reliability still declines
The report says robustness remains weaker over extended workflows, changing tool states, and heterogeneous environments. Strong local decisions do not guarantee the final trajectory.
Benchmarks depend on scaffolds
Published results use named protocols, modes, and scaffolds. Different agent loops, tools, retries, or context can change outcomes.
Model capability is not an API contract
This service exposes only its documented fields. Do not assume every upstream tool or search feature passes through the proxy.
Context and output need safeguards
A 256K window does not remove service limits or factual risk. Validate sources, restrict permissions, cap spend, and handle partial streams.
How to evaluate Ring 2.6 1T responsibly
Evaluate completed work, not attractive reasoning text, through one production-like path.
01
Separate high and xhigh traffic
Run both modes with fixed prompts, tools, and criteria. Require verified gains before accepting xhigh token and latency costs.
02
Score the full trajectory
Track actions, state, recovery, artifacts, and corrections. Run code tests and verify research claims against supplied sources.
03
Measure operating behavior
Record tokens, latency, retries, limits, and cost. Recheck after model, prompt, scaffold, or serving changes.
Evaluate Ring 2.6 1T on real work
Start with high effort in the online chat, compare xhigh on the hardest verified cases, and move only measured workloads into production API traffic.