Visual Understanding
Processes images, charts, documents, and video natively, achieving scores of 90.1 on MathVista, 92.3 on OCRBench, and 87.4 on VideoMME.
DeepSeek
Kimi K2.5
Kimi K2.5 is an open-source multimodal model developed by Moonshot AI and released in January 2026. It uses a Mixture-of-Experts architecture with 1 trillion total parameters and approximately 32 billion active at inference time, trained on roughly 15 trillion mixed visual and text tokens. Unlike models that add vision as a secondary capability, Kimi K2.5 was trained natively on both image and text data, enabling integrated understanding of charts, documents, video, and code. The model supports two operating modes — Instant Mode for direct responses and Thinking Mode for step-by-step reasoning on complex problems — within a 256,000-token context window. It introduces an Agent Swarm paradigm that can coordinate up to 100 parallel sub-agents, reducing execution time by 4.5x on parallelizable tasks. Kimi K2.5 is released under a modified MIT license, making it available for local deployment, fine-tuning, and commercial use, and is particularly suited for visual programming, document analysis, automated research, and multi-step agentic workflows.
Jan 27, 2026
262,144 context
16,384 tokens output
Visual Understanding
Advanced Coding
Mathematical Reasoning
Agent Swarm Execution
Long Context Processing
Dual Inference Modes
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
DeepSeek
Model ID
The routed model identifier exposed by upstream providers.
moonshotai/kimi-k2.5
Input Context Window
The number of tokens supported by the input context window.
262,144
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
16,384 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Jan 27, 2026
4 months ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
January 2026
API Providers
The providers that offer this model. This is not an exhaustive list.
ModelRun, Chutes, DeepInfra, SiliconFlow, AtlasCloud, DigitalOcean, StreamLake, Venice, Novita, Parasail, Phala, Moonshot AI, Fireworks
Modalities
Types of data this model can process.
Text
Image
Video
Code
What is Kimi K2.5
A fuller summary of positioning, capabilities, and source-specific details for Kimi K2.5.
Kimi K2.5 is an open-source multimodal model developed by Moonshot AI and released in January 2026. It uses a Mixture-of-Experts architecture with 1 trillion total parameters and approximately 32 billion active at inference time, trained on roughly 15 trillion mixed visual and text tokens. Unlike models that add vision as a secondary capability, Kimi K2.5 was trained natively on both image and text data, enabling integrated understanding of charts, documents, video, and code.
The model supports two operating modes — Instant Mode for direct responses and Thinking Mode for step-by-step reasoning on complex problems — within a 256,000-token context window. It introduces an Agent Swarm paradigm that can coordinate up to 100 parallel sub-agents, reducing execution time by 4.5x on parallelizable tasks. Kimi K2.5 is released under a modified MIT license, making it available for local deployment, fine-tuning, and commercial use, and is particularly suited for visual programming, document analysis, automated research, and multi-step agentic workflows.
Capabilities
What Kimi K2.5 supports
Advanced Coding
Handles real-world software engineering tasks, scoring 76.8% on SWE-Bench Verified and 85.0% on LiveCodeBench v6.
Mathematical Reasoning
Applies step-by-step reasoning to math and science problems, scoring 96.1% on AIME 2025 and 87.6% on GPQA-Diamond.
Agent Swarm Execution
Coordinates up to 100 parallel sub-agents for complex workflows, achieving a 4.5x reduction in execution time on parallelizable tasks and 78.4% on BrowseComp.
Long Context Processing
Supports a 256,000-token context window, enabling analysis of long documents, extended codebases, and lengthy video content in a single pass.
Dual Inference Modes
Offers Instant Mode for fast, direct responses and Thinking Mode for deep, iterative reasoning on complex problems.
MoE Architecture
Uses a 1 trillion parameter Mixture-of-Experts design with ~32 billion parameters active per forward pass, balancing capacity with inference efficiency.
Pricing for Kimi K2.5
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.45
Per million tokens
Output tokens
$1.90
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
Cache read
$0.09
maxTemperature
1
maxResponseSize
16,384 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
ModelRun
Chutes
DeepInfra
SiliconFlow
AtlasCloud
DigitalOcean
StreamLake
Venice
Novita
Parasail
Phala
Moonshot AI
Fireworks
Provider Endpoints
Endpoint-level provider data currently available for this model.
ModelRun
Chutes
DeepInfra
SiliconFlow
AtlasCloud
DigitalOcean
StreamLake
Venice
Novita
Parasail
Phala
Moonshot AI
Fireworks
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2025
American math olympiad problems (2025)
|
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BrowseComp
Complex web browsing and information retrieval
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
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OSWorld-Verified
Autonomous computer use and desktop tasks
|
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SciCode
Scientific research coding and numerical methods
|
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SWE-bench Pro
Challenging real-world software engineering tasks
|
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SWE-bench Verified
Real GitHub issues requiring multi-file code fixes
|
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Terminal-Bench 2.0
Agentic coding and terminal command tasks
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about Kimi K2.5
Kimi K2.5 discussions are most active in r/LocalLLaMA, r/SillyTavernAI, r/opencodeCLI.
Top Reddit threads cluster around benchmark and model-comparison threads, safety and censorship questions, coding workflow discussions. The strongest match in this snapshot has 4668 upvotes and 361 comments.
These are all the models that i am interested in using, and they are all that i can afford at the moment. Would be great if you can also suggest other models as well!
I aim for a more emotional, less descriptive and flowery type of dialogues.
Hey everyone, I've read all the threads about the Kimi K2.5, but I haven't found any temperature recommendations anywhere. What settings do you use?
For me kimi k2.6 compared to k2.5 more struggles with multiple characters bots and it's prose is much more idealized, it also often struggles to stay in character, and we can not forget the "wait" "actually" in it's reasoning making a response up to 60k tokens, while kimi k2.5 is much better where K2.6 struggles and costs twice less
they really cooked
New SOTA in Agentic Tasks!!!!
Blog: [https://www.kimi.com/blog/kimi-k2-5.html](https://www.kimi.com/blog/kimi-k2-5.html)
FAQ
Common questions about Kimi K2.5
What is the context window for Kimi K2.5?
Kimi K2.5 supports a context window of 262,144 tokens (256K), allowing it to process long documents, extended codebases, and lengthy video content in a single session.
Is Kimi K2.5 open-source and can it be used commercially?
Yes. Kimi K2.5 is released under a modified MIT license, which permits local deployment, fine-tuning, and integration into commercial applications.
What is the training data cutoff for Kimi K2.5?
Based on the available metadata, Kimi K2.5 was released in January 2026. A specific training data cutoff date is not stated in the provided metadata.
How does the Agent Swarm feature work?
Kimi K2.5 introduces an Agent Swarm paradigm that can coordinate up to 100 parallel sub-agents to execute complex, multi-step tasks. On parallelizable workloads, this reduces execution time by approximately 4.5x compared to sequential execution.
What are the two inference modes available in Kimi K2.5?
Kimi K2.5 supports Instant Mode, which provides fast and direct responses suited for everyday tasks, and Thinking Mode, which performs deep step-by-step reasoning for complex problems such as advanced math or multi-stage coding challenges.
How many parameters does Kimi K2.5 have, and how many are active at inference?
Kimi K2.5 has 1 trillion total parameters in a Mixture-of-Experts architecture, with approximately 32 billion parameters active at any given inference step.
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