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GLM 5.1

Input:$1.12/M
Output:$3.528/M
Context:20.0k
Max Output:128,000
Released:Apr 15, 2026

TEST GLM-5.1 (released April 2026), purpose-built for long-horizon autonomous tasks. Unlike traditional models optimized for short interactions, GLM-5.1 excels at maintaining goal alignment, reducing strategy drift, and delivering production-grade results over extended periods โ€” up to 8 hours of continuous autonomous work on a single complex task. It represents a major leap in agentic engineering, shifting evaluation from single-turn intelligence to real-world sustained execution.

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Playground for GLM 5.1

Explore GLM 5.1's Playground โ€” an interactive environment to test models, run queries in real time. Try prompts, adjust parameters, and iterate instantly to accelerate development and validate use cases.

Technical Specifications of GLM-5.1

SpecificationDetails
DeveloperZ.ai (Zhipu AI)
Model VersionGLM-5.1 (post-training refinement of GLM-5)
ArchitectureMixture-of-Experts (MoE); ~744โ€“754 billion total parameters, ~40 billion active per token; incorporates Multi-head Latent Attention and DeepSeek Sparse Attention for long-context efficiency
Context Length200Kโ€“203K tokens (up to 202,752โ€“204.8K in some configurations)
Maximum Output Tokens128K tokens
ModalitiesText-only (input/output); no native vision or audio support
Key CapabilitiesThinking modes, streaming output, function calling/tool use (MCP integration), context caching, structured JSON output
LicenseMIT (fully open-source weights)
Deployment OptionsOfficial API, local inference (vLLM, SGLang), Hugging Face / ModelScope
Training HardwareHuawei Ascend chips (no Nvidia dependency)

What is GLM-5.1

GLM-5.1 is Z.aiโ€™s frontier-class language model optimized for long-horizon autonomous tasks. Unlike traditional LLMs that excel at short, single-turn interactions, it is engineered for sustained execution loopsโ€”planning, coding, testing, benchmarking, debugging, and iterative optimizationโ€”over extended periods without human intervention.

Key Features of GLM-5.1

1. Long-Horizon Autonomous Work

8-Hour Sustained Execution: GLM-5.1 is Z.AIโ€™s latest flagship model for long-horizon tasks, and the official docs say it can work continuously and autonomously on a single task for up to 8 hours. It is positioned to handle the full loop from planning and execution to iterative optimization and final delivery.

Closed-Loop Optimization: A core feature of GLM-5.1 is its ability to keep iterating through an โ€œexperiment โ†’ analyze โ†’ optimizeโ€ cycle, rather than stopping at one-shot output. Z.AI describes this as a major step toward autonomous engineering and long-horizon coding agents.

2. Strong Coding and Reasoning Ability

Broad Capability Balance: GLM-5.1 is broadly aligned with Claude Opus 4.6 in general capability and coding performance, and that it shows a balanced profile across reasoning, coding, agents, tool use, and browsing benchmarks.

Advanced Engineering Workflows: GLM-5.1 is designed for real-world development workflows, including complex engineering optimization, debugging, and production-grade delivery. Z.AI positions it as a foundation for autonomous agents and long-horizon coding agents.

3. Better Support for Complex Tasks

Larger Context and Output: The migration guide lists GLM-5.1โ€™s maximum context length as 200K and maximum output as 128K, which makes it more suitable for large tasks and extended sessions.

Deep Thinking and Tool Streaming: GLM-5.1 supports deep thinking mode, and Z.AI also adds streaming output during tool calls with tool_stream=true, which helps expose tool-call parameters in real time.

4. Built for Agentic Engineering

From Code Generation to Autonomous Delivery: Z.AIโ€™s positioning for GLM-5.1 is not just โ€œgenerate code,โ€ but โ€œdeliver engineering work.โ€ The docs describe it as a new-generation flagship model for โ€œAgentic Engineering,โ€ emphasizing planning, execution, optimization, and delivery in one workflow.

Stronger Stability Over Long Tasks: The release notes say GLM-5.1 improves stability, consistency, and tool use over extended tasks, supported by multi-turn SFT, RL, and process-quality evaluation.

GLM-5.1 vs Other Models

GLM-5.1 stands out as one of the strongest open-source options and a direct competitor to closed frontier models in coding and agentic scenarios:

  • vs. Claude Opus 4.6: ~94โ€“100% of coding performance on SWE-Bench Pro (58.4 vs. 57.3); superior long-horizon autonomy and lower cost via open weights/aggregators.
  • vs. GPT-5.4: Outperforms on SWE-Bench Pro (58.4 vs. 57.7); competitive or slightly behind in some pure reasoning tasks.
  • vs. GLM-5 (predecessor): 28% coding uplift and dramatically better sustained execution.
  • vs. Llama 3.1 / Qwen / DeepSeek: Stronger agentic and long-horizon results; open MIT license provides greater customization freedom than many alternatives.

Its primary advantages are open-source accessibility, cost efficiency at scale, and specialized optimization for real-world engineering agents.

Use Cases

GLM-5.1 excels wherever long-running, iterative intelligence is required:

  • Autonomous Software Engineering: Full-stack feature development, code migration, large-scale refactoring, and end-to-end testing with minimal oversight.
  • Performance Optimization: Kernel-level improvements, database tuning, and multi-iteration benchmarking (e.g., 6.9ร— vector query speedup).
  • Agentic Workflows: Integration into coding agents (Claude Code, OpenClaw) for repository-scale tasks or complex system building.
  • Enterprise Productivity: Long-document analysis, report generation, and structured office artifacts.
  • Research & Prototyping: Rapid iteration on ambiguous problems requiring hundreds of self-correcting steps.

How to Access GLM-5.1 via CometAPI

CometAPI, a unified AI model aggregator, provides immediate, OpenAI-compatible access to GLM-5.1 (and GLM-5) alongside 500+ other models. Developers simply sign up at cometapi.com, obtain an API key, and route requests to the GLM-5.1 endpoint(glm-5.1) using standard OpenAI SDKs or Chat Completions. No infrastructure setup is requiredโ€”CometAPI handles inference routing, load balancing, and failover.

Current CometAPI Pricing (approximate, as of mid-April 2026):

  • Input: $0.8 per million tokens
  • Output: $3.2 per million tokens

This is significantly lower than Z.aiโ€™s direct rates (~$1.4 / $4.4) and a fraction of equivalent Western frontier models.

FAQ

Pricing for GLM 5.1

Explore competitive pricing for GLM 5.1, designed to fit various budgets and usage needs. Our flexible plans ensure you only pay for what you use, making it easy to scale as your requirements grow. Discover how GLM 5.1 can enhance your projects while keeping costs manageable.

Comet Price (USD / M Tokens)Official Price (USD / M Tokens)Discount
Input:$1.12/M
Output:$3.528/M
Input:$1.4/M
Output:$4.41/M
-20%

Sample code and API for GLM 5.1

Access comprehensive sample code and API resources for GLM 5.1 to streamline your integration process. Our detailed documentation provides step-by-step guidance, helping you leverage the full potential of GLM 5.1 in your projects.

#!/bin/bash

# Get your CometAPI key from https://www.cometapi.com/console/token
# Export it as: export COMETAPI_KEY="your-key-here"

response=$(curl -s https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -d '{
    "model": "glm-5.1",
    "messages": [
      {
        "role": "user",
        "content": "Hello! Tell me a short joke."
      }
    ]
  }')

printf '%s
' "$response" | python -c 'import json, sys; print(json.load(sys.stdin)["choices"][0]["message"]["content"])'

cURL Code Example

#!/bin/bash

# Get your CometAPI key from https://www.cometapi.com/console/token
# Export it as: export COMETAPI_KEY="your-key-here"

response=$(curl -s https://api.cometapi.com/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $COMETAPI_KEY" \
  -d '{
    "model": "glm-5.1",
    "messages": [
      {
        "role": "user",
        "content": "Hello! Tell me a short joke."
      }
    ]
  }')

printf '%s\n' "$response" | python -c 'import json, sys; print(json.load(sys.stdin)["choices"][0]["message"]["content"])'

Python Code Example

from openai import OpenAI
import os

# Get your CometAPI key from https://www.cometapi.com/console/token
COMETAPI_KEY = os.environ.get("COMETAPI_KEY") or "<YOUR_COMETAPI_KEY>"
BASE_URL = "https://api.cometapi.com/v1"

client = OpenAI(base_url=BASE_URL, api_key=COMETAPI_KEY)

completion = client.chat.completions.create(
    model="glm-5.1",
    messages=[{"role": "user", "content": "Hello! Tell me a short joke."}],
)

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

JavaScript Code Example

import OpenAI from "openai";

// Get your CometAPI key from https://www.cometapi.com/console/token
const COMETAPI_KEY = process.env.COMETAPI_KEY || "<YOUR_COMETAPI_KEY>";
const BASE_URL = "https://api.cometapi.com/v1";

const client = new OpenAI({
  apiKey: COMETAPI_KEY,
  baseURL: BASE_URL,
});

const completion = await client.chat.completions.create({
  model: "glm-5.1",
  messages: [{ role: "user", content: "Hello! Tell me a short joke." }],
});

console.log(completion.choices[0].message.content);

Uptime

Request success rate over the last 30 days, reflecting the reliability of each model provider. CometAPI monitors all connected providers in real time, 24/7.

RespondLIVE
3363msAvg. Response
UptimeLIVE
100.0%Avg. Uptime