Ling-2.5-1T / Ring-2.5-1T NEW
Ant Group's Trillion-Parameter Twins
Ant Group's first major frontier release — two siblings at 1 trillion parameters each, released early 2026. Ling is positioned as an agent-native foundation model; Ring is a thinking model with a novel hybrid linear attention architecture. The Ring variant reportedly cleared IMO 2025 gold-medal level on math.
Intelligence
Good
Cost
Moderate
$0.30 in / $2.50 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on
Artificial Analysis. Cost buckets are fixed dollar
thresholds keyed off output-token price ($/M out).
Intelligence
- Top 1%≤ 1%
- Top 5%≤ 5%
- Top 10%≤ 10%
- Good≤ 25%
- Medium≤ 50%
- Below avg> 50%
Speed
- Top 1%≥ 345 tok/s
- Top 5%≥ 237 tok/s
- Top 10%≥ 196 tok/s
- Good≥ 146 tok/s
- Medium≥ 90 tok/s
- Slow< 90 tok/s
Cost
- Freeopen weights · self-host
- Low< $1 / M out
- Moderate$1–5 / M out
- High≥ $5 / M out
Why it matters
Two trillion-parameter open models from a non-traditional player, one of which (Ring) makes a credible math reasoning claim against Gemini Deep Think and AlphaProof.
Core Capabilities
Long Documents
Handles entire codebases, books, and multi-doc RAG.
Generative
Produces images, video, audio, or other media.
Reasoning
Solves complex math, logic, and planning tasks.
Agent Workflows
Built for tool use and autonomous tasks.
Context Window
128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M
Availability
API
Available
Product / App
Not available
Open Source
Released
Enterprise
Contact sales
Pricing Model
Free / self-host
Open weights — pay only for compute.
Self-host Capability / Performance
Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).
Lower 20% Upper 80% This model
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this
Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.
What it feels like
- Language model from Ant Group — see the linked sources below for benchmark and review coverage
- Tool-use and agent loops are the typical fit per the published model card
Best use cases
- Agent / tool-use workflows that match the model's published benchmarks
- See the model spec and sources block for benchmarked use cases
Not ideal for
- Tasks far outside the modalities listed in this model's spec
- Workflows where a more recent successor in the same family scores higher