- Instruction Following
Gemma 4 26B A4B (free) is a 26-billion-parameter variant in Google’s Gemma 4 family, offered with an A4B quantization profile for more efficient inference. It is…
Powered by inclusionAI
Ling-2.6-flash
- Instruction Following
Ling-2.6-flash is an open-weight, high-efficiency instruct language model from inclusionAI, optimized for fast responses, strong execution, and low token usage in real-world agent workflows.
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 Ling-2.6-flash?
Ling-2.6-flash is an instant (instruct) mixture-of-experts language model from inclusionAI with 104B total parameters and 7.4B active parameters, designed for high-throughput, token-efficient text generation. It is mainly used for real-world agent workflows such as coding assistance, document processing, and lightweight automation where fast turn-around and low token consumption matter. It also supports long-context chat, tool/function calling, and structured output for production agents and application backends. Ling-2.6-flash belongs to the Ling 2.6 model family, sitting as the efficient sibling of the larger Ling-2.6-1T flagship model.
Model capabilities
5 Core Capabilities
-
Conversational Chat
Supports interactive, multi-turn dialogue, answering questions and following instructions while maintaining context across messages for coherent conversations.
-
Image Interpretation
Analyzes input images to identify visual elements and provide textual descriptions of objects, scenes, and relationships.
-
Optical Character Recognition
Extracts machine-readable text from images or documents containing printed or handwritten characters for downstream processing and understanding.
-
Language Translation
Translates text between multiple languages while attempting to preserve meaning, tone, and style in the target language.
-
Content Monitoring
Assists with basic content review tasks, such as detecting potentially unsafe, sensitive, or policy-violating text segments.
Use cases
6 Most Valuable Use Cases
- Agentic task orchestration
- Long-document processing
- Tool-enabled data retrieval
- Workflow and job automation
- Code and terminal assistance
- Structured text generation
Transparent pricing
Cost Comparison
LLM API offers the lowest per-token costs and best performance for Ling-2.6-flash–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.03 | $0.06 | 128K |
| inclusionAI | US East | ~150ms | ~60 tps | ~99.9% | ~$0.08 | ~$0.16 | ~64K |
| OpenAI | Global | ~180ms | ~80 tps | 99.9% | ~$0.10 | ~$0.25 | 128K |
| Anthropic | US West | ~190ms | ~70 tps | 99.9% | ~$1.00 | ~$5.00 | 200K |
| AWS Bedrock | US East | ~220ms | ~50 tps | 99.9% | ~$0.12 | ~$0.24 | ~100K |
Performance benchmarks
Technical Specifications
| Metric | Ling-2.6-flash (inclusionAI) | gpt-4.1-mini (OpenAI) | Claude 3.5 Haiku (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M tokens) | $0.15 | $0.15 | $0.25 |
| Output Price ($/1M tokens) | $0.60 | $0.60 | $1.25 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 80 tps | 60 tps | 55 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 620M
- Prompt tokens processed (last 30 days)
- 5.4M
- API requests served (last 30 days)
- 780M
- Completion tokens generated (last 30 days)
- 98.9%
- Avg uptime (last 30 days)
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Intelligent Model Routing
Automatically route each request to the best model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One API, optimal model -
Cost-Aware Orchestration
Enforce budgets, compare provider pricing, and downshift to cheaper models when possible so you can scale usage without surprise bills or manual tuning.
Lower spend, same output -
Resilient Fallback Flows
Define automatic failover between models and providers so timeouts, rate limits, or outages don’t break your product—or your SLAs.
Stay online, even downstream -
Deep LLM Observability
Get per-call traces, metrics, and logs across all providers with a single view, making debugging, optimization, and safety monitoring straightforward.
See every token, everywhere -
Task-Level Abstractions
Describe tasks—chat, retrieval, tools, classification—once, and let LLM.API pick and wire models, prompts, and tools behind a stable interface.
Code to tasks, not models -
High-Throughput Batch Jobs
Run massive batch workloads across providers with parallel execution, deduping, and retries handled for you, dramatically cutting processing time and operational overhead.
Batch at platform scale
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a fast, low-cost model for simple chatbots and FAQ assistants.
- You need lightweight classification or tagging for short texts at high volume.
- Your use case involves basic data extraction from well-structured documents or forms.
- Your use case involves quick content drafting where style and nuance are less critical.
- You need to prototype ideas rapidly without consuming significant inference budget.
- Your use case involves straightforward prompt-response flows with limited need for memory.
Avoid if...
- You need advanced multi-step reasoning, planning, or complex problem decomposition capabilities.
- Your workload requires handling very long contexts, such as entire books or codebases.
- You need highly specialized domain expertise, such as legal, medical, or financial analysis.
- Your workload requires nuanced creative writing, character consistency, or complex narrative control.
- You need precise tool use, multi-tool orchestration, or complex multi-turn agent behaviors.
- Your workload requires strong multilingual performance or reliable translation across many language pairs.
FAQ
Frequently Asked Questions
-
What is Ling-2.6-flash?
Ling-2.6-flash is a fast, cost-efficient text generation model by inclusionAI optimized for high-throughput chat, tooling, and lightweight reasoning workloads.
-
What is Ling-2.6-flash best suited for?
It is best for low-latency chatbots, high-volume customer support, quick data transformations, and latency-sensitive backends where cost and speed matter most.
-
How is Ling-2.6-flash priced on LLM.API?
LLM.API meters Ling-2.6-flash by tokens, with separate input and output rates; check the LLM.API pricing page for current per‑token costs.
-
What context window does Ling-2.6-flash support?
Ling-2.6-flash supports up to a 16K token context window, suitable for medium-length conversations, prompts, and documents.
-
How fast is Ling-2.6-flash in terms of latency and throughput?
Ling-2.6-flash is tuned for low first-token latency and high streaming throughput, making it suitable for real-time applications and batched workloads.
-
Which modalities does Ling-2.6-flash support?
Ling-2.6-flash currently supports text-only inputs and outputs; it does not handle images, audio, or structured tool outputs natively.
-
How do I call Ling-2.6-flash through LLM.API?
Use the LLM.API chat or completions endpoint, set provider to inclusionAI, and model to "Ling-2.6-flash" in your request payload.
-
Can I use Ling-2.6-flash with tools or function calling via LLM.API?
Yes, you can define tools or functions at the LLM.API layer and route decisions through Ling-2.6-flash outputs, even though tooling isn’t model-native.
-
How does Ling-2.6-flash compare to larger inclusionAI models?
Compared to larger inclusionAI models, Ling-2.6-flash is cheaper and faster but offers weaker reasoning depth, coding capabilities, and long-context comprehension.
-
How does Ling-2.6-flash compare to similar "flash" or "mini" models from other providers?
It targets similar use cases—high-speed, low-cost chat and utility tasks—while performance, safety tuning, and token pricing vary by provider and should be benchmarked.
-
What are the main limitations of Ling-2.6-flash?
It can struggle with complex multi-step reasoning, long technical documents, nuanced coding tasks, and may hallucinate facts without external verification.
-
Does Ling-2.6-flash support streaming responses on LLM.API?
Yes, you can enable streaming on LLM.API to receive Ling-2.6-flash outputs token-by-token for lower perceived latency.
Compare
Competitive Models
- Instruction Following
Qwen3 VL 235B A22B Instruct is a 235B-parameter Mixture-of-Experts vision-language model from Qwen, offering open-weight, long-context (≈256K) multimodal reasoning over text, images, and video. It is…
- Instruction Following
Hailuo 2.3 by MiniMax is a high-fidelity AI video generation model designed for realistic, cinematic 1080p clips from text or image prompts, with strong motion, physics,…