Kimi K2.5 vs GLM-4.7: Which Agentic LLM Is Better?

Agentic coding is quickly becoming the default interface for building software: you describe a goal, the model plans, calls tools, edits files, and iterates until the task is done. Two models showing up frequently in real-world dev stacks are Moonshot AI’s Kimi K2.5 and Z.AI’s GLM-4.7—both designed to be strong at long-context, tool use, and “ship-ready” coding.

This post compares benchmarks, speed & latency, and cost (Novita AI pricing)—and then shows how to try and deploy both models instantly on Novita AI.

Basic Introduction

Here’s the side-by-side comparison of GLM-4.7 and Kimi K2.5：

Feature	GLM-4.7	Kimi K2.5
Developer	Z.AI	Moonshot AI
Release Date	Dec 22, 2025	Jan 27, 2026
Architecture	358B Parameter Mixture-of-Experts (MoE)	1T total-parameter MoE model (32B active parameters per token, 384 experts, 8 activated per token) with native multimodal architecture
Context Window	200k Input / 128k Output	262,144 Input / 262,144 Output
Input Capabilities	Text-only	Text, Image, Video
Output Capabilities	Text	Text
Key Capabilities	Long-context understanding, code generation	Multimodal understanding, agent swarm collaboration (up to 100 sub-agents), visual programming, long-document processing, tool calling

Key Difference Breakdown

1. Model Scale: Kimi K2.5 has a far larger total parameter count (1T vs. 358B) and higher active parameters per token, which theoretically enables stronger knowledge capacity and performance.
2. Multimodal Support: Kimi K2.5 is a native multimodal model that can understand images, videos, and perform visual programming, while GLM-4.7 focuses solely on text capabilities.
3. Context Window: Kimi K2.5’s 256k input window is longer than GLM-4.7’s 200k, making it better suited for ultra-long documents like full legal contracts or academic papers.

Benchmark Comparison

Capability	Benchmark	Kimi K2.5	GLM-4.7	Result
Reasoning	GDPval-AA (ELO-500/2000)	41%	35%	6%
	AA-LCR (Long Context Reasoning)	66%	64%	2%
	Humanity’s Last Exam	29.40%	25.10%	4.3%
	GPQA Diamond (Scientific Reasoning)	88%	86%	2%
	CritPt (Physics Reasoning)	3%	2%	1%
Coding	SciCode	49%	45%	4%
	Terminal-Bench Hard (Agentic Coding)	35%	32%	3%
Tool / Agent	τ²-Bench Telecom (Agentic Tool Use)	96%	96%	0% (tie)
	IFBench (Instruction Following)	70%	68%	2%
	AA-Omniscience Non-Hallucination Rate	36%	10%	26%
Knowledge	AA-Omniscience Accuracy	33%	28%	5%

💡Interpretation:

• Overall: Kimi K2.5 leads in 10 / 11 benchmarks, with margins ranging from +1% to +26%.
• Largest advantage:
  • Non-Hallucination Rate: +26%, indicating substantially higher reliability in agent/tool-based settings.
• Reasoning & Coding:
  • Mostly small-to-moderate but consistent gains (+1% to +6%), suggesting broad but stable superiority rather than reliance on a single outlier.
• Tool Use:
  • Raw tool capability (τ²-Bench) is tied, but behavioral reliability strongly favors Kimi.

Speed & Latency Comparison

Performance isn’t just “tokens/sec.” For dev workflows, what users feel is:

• Time to first token (how fast the model starts responding)
• End-to-end time (how fast you get a usable chunk of output)
• Output throughput (how quickly it streams once it starts)

Metric	Kimi K2.5	GLM-4.7	What it means
Output speed (tokens/sec)	118	99	Kimi generally feels snappier in long generations (code, reports, multi-file diffs).
Time to first answer token (TTFA)	18.3s total (≈17.0s “thinking”)	20.9s total (≈20.2s “thinking”)	Kimi begins responding sooner in this test.
End-to-end response time (to 500 tokens)	22.6s	26.0s	Kimi completes a 500-token response faster in this run.

Cost Comparison

Cost takeaway: If you’re optimizing for output token cost, GLM-4.7 is materially cheaper at the same input rate. If you’re optimizing for higher benchmark ceilings + faster throughput, Kimi K2.5 may justify the premium.

Quickstart: Try Both Models Instantly on Playground

The fastest way to feel the difference between Kimi K2.5 and GLM-4.7 is the Novita AI Playground—no code, no setup.

In Playground, you can:

• Switch models instantly between moonshotai/kimi-k2.5 and zai-org/glm-4.7
• Run the exact same prompt to compare answer quality, reasoning style, and response speed
• Validate production-ready prompting (e.g., strict JSON, tool-style outputs, formatting constraints) before moving to the API

How to Deploy: API, SDK, and Third-Party Integrations

Option A: API

Getting Your API Key on Novita AI

• Step 1: Create or Login to Your Account: Visit https://novita.ai and sign up or log in.
• Step 2: Navigate to Key Management: After logging in, find “API Keys”.
• Step 3: Create a New Key: Click the “Add New Key” button.
• Step 4: Save Your Key Immediately: Copy and store the key as soon as it is generated; it is shown only once.

Call Novita via endpoint

Just change:

• base_url: https://api.novita.ai/openai
• api_key: your Novita key
• model: moonshotai/kimi-k2.5 or zai-org/glm-4.7

from openai import OpenAI

client = OpenAI(
    api_key="<Your API Key>",
    base_url="https://api.novita.ai/openai"
)

response = client.chat.completions.create(
    model="moonshotai/kimi-k2.5",
    messages=[
        {"role": "system", "content": "You are a helpful assistant."},
        {"role": "user", "content": "Hello, how are you?"}
    ],
    max_tokens=262144,
    temperature=0.7
)

print(response.choices[0].message.content)

Option B: SDK

If you’re building agentic workflows (routing, handoffs, tool/function calls), Novita works with OpenAI-compatible SDKs with minimal changes:

• Drop-in compatible: keep your existing client logic; just change base_url + model
• Orchestration-ready: easy to implement routing (Flash default → GLM-4.7 escalation)
• Setup: point to https://api.novita.ai/openai, set NOVITA_API_KEY, select moonshotai/kimi-k2.5 or zai-org/glm-4.7

Option C: Third-Party Platforms

You can also run Novita-hosted models through popular ecosystems:

• Agent frameworks & app builders: Follow Novita’s step-by-step integration guides to connect with popular tooling such as Continue, AnythingLLM, LangChain, and Langflow.
• Hugging Face Hub: Novita is listed as an Inference Provider on Hugging Face, so you can run supported models through Hugging Face’s provider workflow and ecosystem.
• OpenAI-compatible API: Novita’s LLM endpoints are compatible with the OpenAI API standard, making it easy to migrate existing OpenAI-style apps and connect many OpenAI-compatible tools ( Cline, Cursor, Trae and Qwen Code) .
• Anthropic-compatible API: Novita also provides Anthropic SDK–compatible access so you can integrate Novita-backed models into Claude Code style agentic coding workflows.
• OpenCode: Novita AI is now integrated directly into OpenCode as a supported provider, so users can select Novita in OpenCode without manual configuration.

Conclusion

Choose Kimi K2.5 if you want the strongest overall capability profile in this benchmark set—especially for reliability/non-hallucination, plus better throughput and faster end-to-end generation.

Choose GLM-4.7 if you want a highly capable long-context flagship optimized for agentic coding at a lower output-token cost, and you’re operating at scale where unit economics dominate.

Either way, Novita AI makes it easy to run both models side-by-side—same platform, same billing surface, and quick model switching—so you can make the choice with real workload data instead of guesses.

Novita AI is an AI cloud platform that offers developers an easy way to deploy AI models using our simple API, while also providing the affordable and reliable GPU cloud for building and scaling.

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