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Kimi K2.5
- Text Generation
Kimi K2.5 is MoonshotAI’s flagship open-source multimodal Mixture-of-Experts model with native vision and strong agentic capabilities, designed for long-context reasoning and complex tool use.
About the model
What is Kimi K2.5?
Kimi K2.5 is a native multimodal, agentic large language model from MoonshotAI that can process text and images using a 1-trillion-parameter Mixture-of-Experts architecture. It is mainly used for advanced reasoning and coding assistance across long documents and complex projects, as well as for multimodal understanding of visual inputs like screenshots, documents, and diagrams. It also powers agentic workflows such as tool calling, research automation, and multi-step task execution via agent swarms. Kimi K2.5 is an upgrade in the Kimi K2 family, building on the Kimi K2 base model with added MoonViT vision capabilities and expanded agentic features.
Model capabilities
5 Core Capabilities
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Advanced Chat
Performs multi-turn dialogue, follows complex instructions, and generates coherent, context-aware responses for diverse conversational and writing tasks.
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Code Assistance
Understands and generates source code, helps with debugging, and explains programming concepts across multiple mainstream languages and frameworks.
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Multilingual Translation
Translates between major languages, preserving meaning and tone while handling informal expressions and domain-specific terminology where supported.
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Image Understanding
Accepts images as input, identifies objects and layout, and generates descriptions or answers questions about visual content when enabled.
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Text Extraction
Extracts readable text from images or document screenshots, enabling downstream search, analysis, and question answering over visual materials.
Use cases
6 Most Valuable Use Cases
- Multimodal Document Analysis
- Legal Case Research
- Compliance Case Monitoring
- AI Software Agent Orchestration
- Business Report Drafting
- Domain-Specific Text Tagging
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and latency for Kimi K2.5–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 120ms | 80 tps | 99.99% | $0.20 | $0.40 | 200K |
| MoonshotAI (Kimi K2.5) | Global | ~220ms | ~45 tps | ~99.9% | ~$0.40 | ~$0.80 | ~128K |
| OpenAI (o4-mini-equivalent) | Global | ~250ms | ~40 tps | 99.9% | ~$0.50 | ~$1.00 | 128K |
| Anthropic (Claude 3.5 Sonnet-equivalent) | US East | ~260ms | ~35 tps | 99.9% | ~$0.60 | ~$1.20 | 200K |
| Google (Gemini 1.5 Pro-equivalent) | Global | ~280ms | ~30 tps | 99.9% | ~$0.70 | ~$1.40 | 1M |
Performance benchmarks
Technical Specifications
| Metric | Kimi K2.5 (MoonshotAI) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~800ms | ~900ms | ~1s |
| Context Window | 200K | 128K | 200K |
| Input Price ($/1M) | $1.0 | $5.0 | $3.0 |
| Output Price ($/1M) | $3.0 | $15.0 | $15.0 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 40 tps | 50 tps | 35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 12.4B
- Prompt tokens processed (last 30 days)
- 7.8B
- Completion tokens generated (last 30 days)
- 29.6M
- API requests served (last 30 days)
- 99.8%
- 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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Unified AI Routing
Automatically route each request to the optimal model across providers based on cost, latency, and quality—no code changes when your model mix evolves.
One endpoint, every model. -
Cost-Aware Optimization
Control spend with per-route pricing rules, model caps, and dynamic downshifts so teams can experiment freely without surprise bills or manual spreadsheet policing.
Smarter usage, lower spend. -
Automatic Fallback Logic
Define provider-agnostic failover chains so timeouts, rate limits, and provider outages seamlessly fail over to backups—keeping your production apps online by default.
Resilient by design. -
End-to-End Observability
Trace every call across providers with unified logs, metrics, and latency breakdowns so you can debug prompts, tune routing, and prove reliability from a single view.
See every token. -
Task-Centric Abstractions
Call models by task—chat, embedding, moderation, tools—instead of vendor-specific APIs, so you can swap providers without rewriting business logic or prompt contracts.
Code to tasks, not vendors. -
High-Throughput Batch
Ship millions of calls as managed batches with concurrency control, retries, and partial result handling, turning large-scale experimentation and backfills into a single API call.
Scale experiments effortlessly.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a strong Chinese-first general model for chat, Q&A, and writing.
- You need good performance on Chinese web search grounding and current-events queries.
- You need a capable generalist model for coding help, data analysis, and automation.
- Your use case involves cost-sensitive workloads where Kimi offers competitive Chinese-market pricing.
- Your use case involves integrating a popular China-based model into local enterprise stacks.
- You need an assistant optimized for Chinese users’ habits, tone, and content ecosystems.
Avoid if...
- You need guaranteed support, uptime, and SLAs comparable to top US hyperscalers globally.
- Your workload requires best-in-class English reasoning and benchmark-leading long-context performance.
- You need a model with fully documented, stable international APIs and compliance guarantees.
- Your workload requires strict data residency outside mainland China due to regulatory constraints.
- You need tight integration with major Western cloud platforms and enterprise governance tooling.
- Your workload requires highly specialized industry fine-tunes not publicly available for Kimi K2.5.
FAQ
Frequently Asked Questions
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What is Kimi K2.5?
Kimi K2.5 is a large language model from MoonshotAI focused on fast, general-purpose reasoning and coding assistance through the LLM.API platform.
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What is Kimi K2.5 best suited for?
Kimi K2.5 is best for chat-style assistants, code generation, and general reasoning tasks where fast responses and good instruction-following are important.
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What is the context window of Kimi K2.5?
Kimi K2.5 supports a long-context window suitable for multi-turn conversations and large documents, but exact token limits may vary by LLM.API configuration.
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How fast is Kimi K2.5 in terms of latency?
Kimi K2.5 is optimized for low latency, typically returning initial tokens quickly for interactive applications, though exact latency depends on your LLM.API plan.
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What modalities does Kimi K2.5 support via LLM.API?
Kimi K2.5 is exposed as a text-in, text-out model on LLM.API, without native image or audio input in the standard setup.
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How is Kimi K2.5 priced on LLM.API?
Kimi K2.5 pricing on LLM.API is usage-based per input and output tokens, with exact rates defined in your LLM.API account and documentation.
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How do I call Kimi K2.5 using the LLM.API?
Use the standard LLM.API chat or completion endpoint, specifying the Kimi K2.5 model name in the request and authenticating with your LLM.API key.
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How does Kimi K2.5 compare to similar models?
Kimi K2.5 targets a balance of quality, speed, and cost competitive with strong mid-to-high tier general-purpose models from major providers.
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What are the main limitations of Kimi K2.5?
Kimi K2.5 can hallucinate, may lag behind very latest world events, and should not be used without human review for safety-critical decisions.
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Does Kimi K2.5 support function calling or tool use via LLM.API?
If enabled in LLM.API for this model, you can define tools/functions in the request; otherwise function calling is not available for Kimi K2.5.