Kimi K2.5
Earlier Kimi frontier model for long-context agents, coding, and multimodal work
Kimi K2.5
Earlier Kimi frontier model for long-context agents, coding, and multimodal work
Hardware & Execution ParametersVideo
Genealogical Graph & Evolutionary Provenance
Tracing foundational base architecture ancestry, architectural successors, scale siblings, and reasoning distillation derivatives.
Kimi K2.5 operates as an autonomous foundation model architecture without direct precursor derivatives in this catalog.
Architecture Engineering & Capability Deep-Dive
An objective architectural evaluation of Kimi K2.5 by Moonshot AI, analyzing underlying compute dynamics, memory constraints, and deployment economics.
Topology & Attention Mechanics
A robust autoregressive transformer utilizing standard attention patterns for predictable and coherent token generation.
Evaluation Profile & Reasoning
Exhibits frontier-tier behavior in reasoning and coding.
LLM Hardware Sizing & Serving
For open deployments via vLLM/SGLang, quantization (INT4/AWQ) is heavily recommended to fit dense memory constraints, or multi-GPU pipeline parallelism for full FP16.
Inference Economics & Workflows
Well-suited for enterprise pipelines where capability is balanced against per-million token costs.
Architectural Strengths vs. Considerations
An objective balance sheet analyzing the operational advantages and production constraints of deploying Kimi K2.5.
Key Architectural Strengths
- Massive 262,144-token context allows full-repository and book-length ingestion.
- Demonstrated SWE-Bench Verified evaluation score of 70.8% in verified benchmarks.
Operational Considerations
- 128k+ token prefill stages become heavily compute-bound and balloon KV cache without PagedAttention chunking.
Inference Runtimes & Hardware Sizing
Deployment targets, inference engines, and memory requirements for Kimi K2.5.
python3 -m vllm.entrypoints.openai.api_server --model Kimi K2.5 --tensor-parallel-size 1 --gpu-memory-utilization 0.95 --max-model-len 4096 --enable-chunked-prefillollama run kimi k2.5python3 -m sglang.launch_server --model-path Kimi K2.5 --tp 1 --trust-remote-codetext-generation-launcher --model-id Kimi K2.5 --num-shard 1 --max-batch-prefill-tokens 32000High-throughput PagedAttention server
One-click CLI & local desktop serving
Fast multi-turn structured decoding
Text Generation Inference
GGUF CPU/Apple Silicon execution
~0.0 GB VRAM required
~0.0 GB VRAM (Hopper speedup)
~0.0 GB VRAM
CPU RAM / Apple Silicon optimized
LLM Benchmark Database & Performance Metrics
4 TestedStandardized evaluation results across reasoning, agentic coding, computer use, and alignment.
API & Deployment Pricing
Open-weights model available for local and private cloud deployment. Compute costs depend on the target GPU hardware instance.
| Deployment Tier | Pricing Structure |
|---|---|
| Open Checkpoint Weights | $0.00 (Free Download) |
| Inference Token Consumption | $0.00 / Token |
Comparable Foundation Architectures
Alternative models in the Video class with similar capabilities, context windows, or deployment profiles.
Qwen3.8 Flash Next
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ.get("MOONSHOT_AI_KEY", "EMPTY"),
base_url="http://localhost:8000/v1"
)
response = client.chat.completions.create(
model="moonshotai-kimi-k2.5",
messages=[{"role": "user", "content": "Explain quantum superposition in 2 sentences."}]
)
print(response.choices[0].message.content)Frequently Asked Questions about Kimi K2.5
Essential facts, architectural specs, hardware constraints, and pricing answers for Kimi K2.5.
To run Kimi K2.5 (Open Weights) locally, you generally need Depends on quantization. We recommend using quantized GGUF/AWQ formats with Ollama or vLLM to optimize memory footprint.
All technical specifications, parameter distributions, context architectures, and benchmark evaluations for Kimi K2.5 are audited against primary source release documentation, research whitepapers, and verified vendor API endpoints.