Ring-2.6-1T

Our Take

A workable 1000B-parameter MoE language model from inclusionAI. Pulls ahead on competition math (AIME 2026) (96/100), so reach for it when that's the dimension that matters. Newly released, so production-readiness is still being shaken out.

Generated from this model’s benchmarks and ranking signals. Editor reviews refine it over time.

Ring‑2.6‑1T uses a standard MoE transformer layout. Of the 1 trillion total parameters, exactly 63 billion are activated for each forward pass. This sparsity decouples memory footprint from compute cost: you still need to load all expert weights into VRAM, but the inference FLOPs are comparable to a 63B dense model.

• Context length: 262,144 tokens (extended from 128K via YaRN positional interpolation). This enables processing of entire codebases, long conversation histories, or multi‑turn agent traces without truncation.
• Reasoning effort mechanism: Two modes – high and xhigh. high uses a fixed reasoning budget; xhigh dynamically allocates more tokens for deep chain‑of‑thought, useful for math, logic, and multi‑step tool orchestration. You control this via the chat template parameter reasoning_effort.
• Training paradigm: Ant Group employed an asynchronous reinforcement learning scheme (Async RL) with the IcePop algorithm to stabilize long‑horizon training at the trillion‑parameter scale. This is why the model performs consistently across long agent sessions without degradation.

For local inference, the critical spec is total parameter count. At FP16, storing the full 1T weights requires about 2 TB of VRAM. With 4‑bit quantization (Q4_K_M), this drops to ~500 GB. Even at the lowest practical quantization, you are looking at multi‑GPU deployments—there is no single‑consumer‑GPU path for this model.

The model is optimized for four interconnected domains:

Agentic workflows – Ring‑2.6‑1T scores 87.60 on PinchBench (vs. GPT‑5.4 xHigh at ~84) and 63.82 on ClawEval, placing it among the top open‑source agents. It can decompose a user request into sub‑tasks, call tools (APIs, databases, file systems), handle errors, and revisit earlier steps.

Coding agents – Designed for multi‑file patches, repository‑level refactoring, and autonomous bug fixing. It understands diff formats, git history, and CI/CD triggers. On Tau2‑Bench (Telecom scenario) it scored 95.32, meaning it rarely fails in realistic software engineering loops.

Long‑horizon reasoning – Tasks that take minutes or hours—like scientific simulation planning, legal document analysis, or supply chain optimization—benefit from the 262K context and stable RL‑trained memory. The model maintains coherence across hundreds of tool calls or reasoning steps.

Function‑calling & instruction following – The chat template natively supports tool definitions in XML and multi‑step tool invocations. You can define a set of functions and the model will call them sequentially, re‑evaluating state after each return.

No consumer GPU (RTX 4090, M4 Max, etc.) can run this model even at Q2 because 250 GB exceeds single‑card memory. Use cases on consumer hardware are limited to CPU offloading (impractical at this scale) or cloud‑based inference.

The quickest path for multi‑GPU systems is vLLM with tensor parallelism:

For llama.cpp (with Q4_K_M):

Ollama support is not yet official as of mid‑2026, but you can create a custom Modelfile using the GGUF quantization.

Faster on H100s or with FP8 quantization (if supported by your inference engine).

Ring‑2.6‑1T vs DeepSeek‑V3 (671B total, 37B active)

• DeepSeek‑V3 has a smaller total model, so lower VRAM requirements (roughly half at the same quantization). For teams with 4‑ or 8‑GPU nodes, DeepSeek‑V3 is easier to deploy.
• Ring‑2.6‑1T has more active parameters (63B vs 37B) and significantly longer context (262K vs 128K). It also includes built‑in reasoning effort modes, while DeepSeek‑V3 requires manual prompting.
• On agent benchmarks (PinchBench, ClawEval), Ring‑2.6‑1T outperforms DeepSeek‑V3 by 5‑10%. For general chat and code completion, the two are comparable.

Ring‑2.6‑1T vs Llama 3.1 405B (dense)

• Llama 3.1 405B requires ~800 GB at FP16, so it fits on 4× A100 80GB—a more accessible setup. Ring‑2.6‑1T requires significantly more VRAM.
• Llama 3.1 405B is a dense model with no MoE active‑parameter savings, so per‑token compute is higher. Ring‑2.6‑1T’s MoE gives lower latency relative to parameter count.
• For agent tasks and long‑context reasoning, Ring‑2.6‑1T is the clear winner. For straightforward instruction following and multilingual tasks, Llama 3.1 405B remains a strong option.

When to choose Ring‑2.6‑1T: You need a unified model for agentic automation, high‑quality code generation, and multimodal-free reasoning over very long contexts. You have access to a multi‑GPU server (8× A100 or better) and accept the hardware cost in exchange for top‑tier open‑source agent performance.

When to choose an alternative: Your hardware is limited to 2‑4 GPUs, your tasks are mostly short‑format Q&A, or you need to deploy on consumer GPUs today.