- Instruction Following
Voxtral Small 24B 2507 is a 24-billion-parameter audio-language model from Mistral that extends Mistral Small 3 with advanced speech understanding. It is notable for strong, cost-efficient…
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Kimi K2.6
- Instruction Following
Kimi K2.6 is MoonshotAI’s open-source, 1-trillion-parameter Mixture-of-Experts multimodal model optimized for long-horizon coding, agentic tool use, and image/video understanding. It is notable for its large ~262K-token context window and strong performance on complex software engineering and tool-using benchmarks.
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 Kimi K2.6?
Kimi K2.6 is a frontier open-weight multimodal Mixture-of-Experts model from MoonshotAI, designed for long-horizon coding, agent swarms, and advanced tool use. It is primarily used for complex end-to-end software development workflows, including building full applications and dashboards from a single prompt, and for orchestrating large multi-agent systems over thousands of coordinated steps. It is also applied to multimodal tasks that combine text with images or video for design, UI generation, and technical reasoning across long contexts. Kimi K2.6 belongs to the Kimi K2 family of MoE models and succeeds earlier releases such as Kimi K2 and Kimi K2.5.
Model capabilities
5 Core Capabilities
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Multimodal Input
Processes text and visual inputs using a native MoonViT vision encoder, enabling document understanding, UI analysis, and image-grounded reasoning.
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Text Conversation
Supports general-purpose chat, reasoning, and instruction following across diverse domains, with long-context understanding up to 256K tokens.
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Advanced Coding
Provides state-of-the-art coding support, generating full-stack applications, dashboards, and complex multi-file codebases from natural language prompts.
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Agentic Workflows
Coordinates large agent swarms for long-horizon tasks, enabling multi-step research, analysis, and autonomous execution over extended periods.
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Multilingual Usage
Handles multiple languages for reading and generation, suitable for cross-lingual coding, documentation, and global deployment scenarios.
Use cases
6 Most Valuable Use Cases
- Long-horizon Coding
- Agentic Task Automation
- Multimodal Document Analysis
- Code-driven UI Design
- Tool-using Research Agents
- Ongoing Workflow Monitoring
Transparent pricing
Cost Comparison
LLM API offers the lowest Kimi K2.6‑class pricing and up to ~60% lower cost than comparable premium LLMs.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.15 | $0.45 | 256K |
| MoonshotAI | Asia Pacific | ~220ms | ~45 tps | ~99.9% | ~$0.25 | ~$0.80 | ~200K |
| OpenAI (o3-mini equivalent) | Global | ~300ms | ~40 tps | 99.9% | ~$0.30 | ~$0.90 | 200K |
| Anthropic (Claude 3.7 Sonnet equivalent) | US East | ~280ms | ~35 tps | 99.9% | ~$0.35 | ~$1.00 | 200K |
| Google (Gemini 2.0 Pro equivalent) | Global | ~260ms | ~30 tps | 99.9% | ~$0.28 | ~$0.85 | 128K |
Performance benchmarks
Technical Specifications
| Metric | Kimi K2.6 (MoonshotAI) | GPT-4.1 Mini (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~700ms | ~800ms | ~900ms |
| Context Window | 200K | 128K | 200K |
| Input Price ($/1M) | $0.80 | $5.00 | $3.00 |
| Output Price ($/1M) | $2.40 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | 45 tps | 35 tps | 60 tps |
| Uptime | 99.5% | 99.9% | 99.9% |
30-day usage via LLM API
- 62B
- Prompt tokens processed (last 30 days)
- 21M
- Completion tokens generated (last 30 days)
- 3.4M
- API requests served (last 30 days)
- 99.95%
- Avg API uptime (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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Intelligent AI 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 endpoint, every model. -
Cost-Aware Execution
Control spend with per-request cost estimation, smart model selection, and centralized quotas so teams can experiment fast without runaway bills or manual tracking.
More performance, less spend. -
Resilient Fallback Flows
Define automatic, provider-agnostic fallbacks to keep your app up during outages, rate limits, or timeouts—no brittle failover logic scattered through your codebase.
Never go dark on users. -
Deep LLM Observability
Trace every call across providers with logs, metrics, and request replay so you can debug, tune prompts, and optimize model choices from one unified dashboard.
See every token, everywhere. -
Task-Level Orchestration
Describe tasks, not models. LLM.API maps them to the right tools, models, and prompts so you ship complex AI workflows with minimal glue code.
Think tasks, not models. -
High-Throughput Batch APIs
Process millions of inferences efficiently with optimized batch pipelines, concurrency controls, and retry logic—all behind the same simple interface you use for single calls.
Scale from 1 to millions.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong Chinese-centric LLM for web search, Q&A, and summarization.
- You need an assistant optimized for Chinese users with solid general reasoning capabilities.
- Your use case involves drafting or polishing Chinese content such as articles or reports.
- Your use case involves conversational agents for Chinese customer support and general assistance.
- You need an LLM that integrates well with Kimi’s ecosystem and tooling services.
- Your use case involves knowledge-intensive tasks focused on mainland Chinese web and sources.
Avoid if...
- You need guaranteed strong English performance comparable to the latest frontier global models.
- Your workload requires on-premise deployment or strict self-hosting beyond a Chinese cloud provider.
- You need a model with extensively documented, stable APIs and English-first developer support.
- Your workload requires globally distributed low-latency inference outside Asia with strict SLAs.
- You need fully transparent benchmarks, safety evaluations, and licensing terms for enterprise compliance.
- Your workload requires tight integration with Western ecosystem tools or US-based cloud marketplaces.
FAQ
Frequently Asked Questions
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What is Kimi K2.6?
Kimi K2.6 is a large language model from MoonshotAI focused on high-quality reasoning and chat-style assistance for general-purpose applications.
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What is Kimi K2.6 best suited for?
Kimi K2.6 is best for multilingual chatbots, reasoning-heavy assistance, and knowledge-intensive applications where answer quality matters more than raw generation speed.
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What is the context window of Kimi K2.6 via LLM.API?
Through LLM.API, Kimi K2.6 supports long-context conversations; check the model card for the current maximum tokens per request and response.
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How fast is Kimi K2.6 on LLM.API?
Kimi K2.6 typically returns the first tokens within a few seconds, with total latency depending on prompt size and requested output length.
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What modalities does Kimi K2.6 support?
Kimi K2.6 supports text input and text output; it does not natively process images, audio, or video through LLM.API at this time.
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How is Kimi K2.6 priced on LLM.API?
LLM.API bills Kimi K2.6 usage per input and output token; refer to the LLM.API pricing page for the latest rates.
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How do I call Kimi K2.6 through the LLM.API?
You select the Kimi K2.6 model name in the LLM.API chat or completions endpoint, pass your prompt, and authenticate with your LLM.API key.
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How does Kimi K2.6 compare to similar models on LLM.API?
Kimi K2.6 targets strong reasoning and conversation quality at competitive cost, while some alternative models may prioritize speed, tool integration, or multimodal capabilities.
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What are the main limitations of Kimi K2.6?
Kimi K2.6 can hallucinate facts, lacks real-time internet access, and may struggle with highly specialized, domain-specific or safety-sensitive tasks without careful prompting.
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Can I use Kimi K2.6 for streaming responses?
Yes, Kimi K2.6 supports streamed token output through LLM.API when you enable streaming on the corresponding chat or completion request.
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