Try the new DeepSeek V4 Pro 0813 today. Frontier intelligence at a fraction of the cost. Here
LLM

Z AI LogoGLM-5.3

GLM-5.3 is Z.AI's latest model for agentic engineering, with stronger coding, long-horizon agentic capabilities, and emergent cybersecurity analysis.

Model details

GLM-5.3 is Z.AI's latest model for agentic engineering, with stronger coding, long-horizon agentic capabilities, and emergent cybersecurity analysis: all achieved through scaled post-training on the same base model as GLM-5.2.

GLM-5.3 runs on the same 744B-A40B MoE base as GLM-5.2, with every capability gain coming from scaled post-training across more diverse, realistic task environments: full codebases, documentation, testing tools, and multi-step workflows. The result is Z.AI's strongest coding model to date, with the largest jumps on the longest-horizon benchmarks: Terminal-Bench 3.0 moves from 4.6% to 28.3% over GLM-5.2, in addition to notable gains in vulnerability discovery and security analysis.

GLM-5.3 supports three thinking effort levels (low, high, and max, with max recommended for coding), making it a strong fit for agentic coding workflows that require sustained planning, implementation, and verification at production scale.

GLM-5.3 Agentic Coding PerformanceGLM-5.3 Agentic Coding Performance

Input
1# You can use this model with any of the OpenAI clients in any language!
2# Simply change the API Key to get started
3
4from openai import OpenAI
5
6client = OpenAI(
7    api_key="YOUR_API_KEY",
8    base_url="https://inference.baseten.co/v1"
9)
10
11response = client.chat.completions.create(
12    model="zai-org/GLM-5.3",
13    messages=[
14        {
15            "role": "user",
16            "content": "Implement Hello World in Python"
17        }
18    ],
19    stream=True,
20    stream_options={
21        "include_usage": True,
22        "continuous_usage_stats": True
23    },
24    top_p=1,
25    max_tokens=1000,
26    temperature=1,
27    presence_penalty=0,
28    frequency_penalty=0
29)
30
31for chunk in response:
32    if chunk.choices and chunk.choices[0].delta.content is not None:
33        print(chunk.choices[0].delta.content, end="", flush=True)
JSON output
1{
2    "id": "143",
3    "choices": [
4        {
5            "finish_reason": "stop",
6            "index": 0,
7            "logprobs": null,
8            "message": {
9                "content": "[Model output here]",
10                "role": "assistant",
11                "audio": null,
12                "function_call": null,
13                "tool_calls": null
14            }
15        }
16    ],
17    "created": 1741224586,
18    "model": "",
19    "object": "chat.completion",
20    "service_tier": null,
21    "system_fingerprint": null,
22    "usage": {
23        "completion_tokens": 145,
24        "prompt_tokens": 38,
25        "total_tokens": 183,
26        "completion_tokens_details": null,
27        "prompt_tokens_details": null
28    }
29}

🔥 Trending models