- Instruction Following
Google Gemini Flash Latest is a fast, cost‑optimized variant of Google’s Gemini family, designed to deliver high-throughput, low-latency multimodal reasoning for everyday and agent-style workloads. It…
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Ring-2.6-1T
- Instruction Following
Ring-2.6-1T is a trillion-parameter-scale open-weight "thinking" language model from inclusionAI, designed for real-world agent and coding workflows that need strong reasoning with efficient execution.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is Ring-2.6-1T?
Ring-2.6-1T is a 1T-parameter-scale mixture-of-experts reasoning model with 63B active parameters, built by inclusionAI for agentic large language model workflows. It is primarily used for advanced coding agents, tool-using systems, and long-horizon task execution where deep chain-of-thought reasoning is required. It is also applied in complex business, research, and automation pipelines that must balance capability, latency, and token cost at large context scales (around 262K tokens). Within inclusionAI’s lineup, Ring-2.6-1T serves as the flagship deep-reasoning counterpart to the faster Ling-2.6-1T instruct models in the same 2.6 family.
Model capabilities
5 Core Capabilities
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Advanced Reasoning
Trillion-parameter thinking model with strong multi-step reasoning for complex tasks and decision-making in real-world agent workflows.
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Coding Assistance
Optimized for coding agents, providing code generation, editing, and debugging support across multi-file, long-horizon software engineering tasks.
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Agentic Workflows
Designed for long-horizon autonomous agents, coordinating multi-step plans, tool calls, and task execution efficiently over extended contexts.
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Tool Use Orchestration
Supports sophisticated tool-calling patterns, integrating external APIs and systems to solve tasks requiring dynamic information retrieval or actions.
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Long-Context Handling
Processes and reasons over up to 262K tokens, maintaining coherence across lengthy documents, conversations, and multi-stage workflows.
Use cases
6 Most Valuable Use Cases
- Autonomous Coding Agents
- Tool-Driven Workflows
- Complex Research Pipelines
- Long-Horizon Task Planning
- Cost-Efficient AI Integration
- Large-Context Text Processing
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for Ring-2.6-1T–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.50 | $0.50 | 256K |
| inclusionAI | US East | ~140ms | ~60 tps | ~99.9% | ~$0.80 | ~$0.80 | ~128K |
| OpenAI (comparable tier) | Global | ~160ms | ~50 tps | 99.9% | ~$1.20 | ~$1.20 | 128K |
| Anthropic (comparable tier) | US West | ~170ms | ~45 tps | 99.9% | ~$1.40 | ~$1.40 | 200K |
| Azure AI (comparable tier) | EU West | ~190ms | ~40 tps | 99.9% | ~$1.10 | ~$1.10 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Ring-2.6-1T (inclusionAI) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~240ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.70 | $5.00 | $3.00 |
| Output Price ($/1M) | $1.80 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 4K |
| Throughput | 60 tps | 40 tps | 35 tps |
| Uptime | 99.7% | 99.9% | 99.9% |
30-day usage via LLM API
- 62B
- Prompt tokens processed (last 30 days)
- 51B
- Completion tokens generated (last 30 days)
- 7.4M
- API requests served (last 30 days)
- 310K
- Unique developer accounts (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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Unified AI Routing
Automatically route each request to the optimal model across providers based on latency, cost, and quality—no client changes, just smarter traffic.
One endpoint, every model -
Intelligent Cost Controls
Define per-project budgets, price caps, and model allowlists so LLM.API enforces cost policies automatically while still choosing the best option in real time.
Predictable AI spend -
Resilient Fallback Logic
Configure automatic failover chains so if a model or region degrades, traffic instantly shifts to backups without user-visible errors or redeploys.
Zero-downtime AI -
End-to-End Observability
Get full traces, latency breakdowns, and provider-level metrics for every call, making it easy to debug prompts, compare models, and catch regressions early.
See every token -
Task-Level Abstractions
Describe tasks—chat, extraction, classification, tools—once, and let LLM.API map them to the best model and parameters so your code stays provider-agnostic.
Code to tasks, not models -
High-Throughput Batch
Submit massive batches of prompts or jobs over a single API, with automatic chunking, retries, and aggregation optimized for throughput and lower per-unit cost.
Millions of calls, one job
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose LLM from a smaller provider for vendor diversification.
- You need an experimental model to prototype inclusionAI-specific features or integrations.
- Your use case involves moderate-length chatbots where perfect state-of-the-art quality is unnecessary.
- Your use case involves back-office automation where occasional minor errors are acceptable.
- You need a secondary model to compare outputs against larger, more established LLMs.
- Your use case involves internal tools where explainability and traceability matter more than raw power.
Avoid if...
- You need proven, battle-tested performance on mission-critical workloads with strict SLAs.
- You need cutting-edge reasoning and coding ability comparable to leading frontier LLMs.
- Your workload requires extensive ecosystem support, plugins, and broad third-party integrations.
- You need established compliance attestations and audits for highly regulated enterprise environments.
- Your workload requires guaranteed low latency and high throughput under heavy global traffic.
- You need long-context processing for hundreds of pages with robust retrieval-augmented generation.
FAQ
Frequently Asked Questions
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What is Ring-2.6-1T?
Ring-2.6-1T is a large language model by inclusionAI available through LLM.API for high-quality text generation and reasoning workloads.
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What is Ring-2.6-1T best suited for?
Ring-2.6-1T is best for complex reasoning, multi-step tool-using agents, long-form content generation, and building robust production chat or copilots.
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What modalities does Ring-2.6-1T support?
Ring-2.6-1T currently supports text input and text output only when accessed via LLM.API.
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What is the context window of Ring-2.6-1T?
Ring-2.6-1T supports a 32K token context window for combined input and output through LLM.API.
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How is Ring-2.6-1T priced on LLM.API?
Ring-2.6-1T pricing on LLM.API is per-token for input and output, with exact rates shown in your LLM.API dashboard and pricing documentation.
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How fast is Ring-2.6-1T in terms of latency?
Ring-2.6-1T typically returns first tokens within a few hundred milliseconds, with total latency depending on prompt size and output length.
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How do I call Ring-2.6-1T via LLM.API?
Use the LLM.API chat or completions endpoint with the model parameter set to "inclusionai/Ring-2.6-1T" and your LLM.API key.
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How does Ring-2.6-1T compare to similar large models?
Ring-2.6-1T targets strong reasoning and long-context performance at a lower effective cost than many frontier proprietary models.
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Does Ring-2.6-1T support streaming responses on LLM.API?
Yes, Ring-2.6-1T supports token streaming via LLM.API by enabling the stream option in your request.
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What are the main limitations of Ring-2.6-1T?
Ring-2.6-1T can hallucinate facts, lacks real-time knowledge or web access by default, and may underperform on highly domain-specific technical datasets.
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