GLM-4.5V (Non-reasoning)

GLM-4.5V (Non-reasoning)

Z.aitext

API Available

Specifications

Context Window

Input Price/1M

$0.60

Output Price/1M

$1.80

Parameters

Speed

41 tok/s

Latency (TTFT)

36.2s

Benchmarks

GLM-4.5V (Non-reasoning) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.

Agentic

BenchmarkScoreMaximumMethodology
Terminal-Bench Hard7.0100.0

Coding

BenchmarkScoreMaximumMethodology
LiveCodeBench35.2100.0Artificial Analysis official API
SciCode19.0100.0
AA Coding Index10.8100.0Artificial Analysis official API

Knowledge

BenchmarkScoreMaximumMethodology
MMLU-Pro75.0100.0

Long Context

BenchmarkScoreMaximumMethodology
AA-LCR0.0100.0

Math

BenchmarkScoreMaximumMethodology
AA Math Index15.3100.0Artificial Analysis official API
AIME 202515.3100.0Artificial Analysis official API

overall

BenchmarkScoreMaximumMethodology
AA Intelligence Index7.0100.0Artificial Analysis official API

Reasoning

BenchmarkScoreMaximumMethodology
MMLU Pro75.1100.0Artificial Analysis official API
GPQA Diamond57.3100.0Artificial Analysis official API
IFBench29.0100.0
HLE4.0100.0

Tool Use

BenchmarkScoreMaximumMethodology
Tau²-Bench20.0100.0

Information

Release date
August 11, 2025
Tool Calling
❌ Not supported
Vision
❌ Not supported
Audio
❌ Not supported

Full Analysis: GLM-4.5V (Non-reasoning)

What is GLM-4.5V (Non-reasoning)?

GLM-4.5V (Non-reasoning) is an AI model developed by Z.ai, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Z.ai's cloud API.

Pricing & Costs in 2026

GLM-4.5V (Non-reasoning) is usage-based, priced at $0.6/1M input tokens and $1.8/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.

Benchmarks & Performance

GLM-4.5V (Non-reasoning) was evaluated on 14 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

GLM-4.5V (Non-reasoning) specializes in text, offering advanced capabilities for creating and processing text content.

Comparison with Alternatives

In the 2026 AI model ecosystem, GLM-4.5V (Non-reasoning) competes directly with similarly capable models. Z.ai competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is GLM-4.5V (Non-reasoning)?

GLM-4.5V (Non-reasoning) is an AI model developed by Z.ai. It is a text model.

How much does GLM-4.5V (Non-reasoning) cost?

GLM-4.5V (Non-reasoning) costs $0.6/1M input tokens and $1.8/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does GLM-4.5V (Non-reasoning) compare with other models?

In available benchmarks, GLM-4.5V (Non-reasoning) scored: Terminal-Bench Hard: 7/100, LiveCodeBench: 35.2/100, SciCode: 19/100. See the full table above for a detailed comparison.

Is GLM-4.5V (Non-reasoning) open source?

No, GLM-4.5V (Non-reasoning) is a proprietary model from Z.ai. It is available via cloud API. For open source alternatives, check our open source model ranking.

What is GLM-4.5V (Non-reasoning) best for?

GLM-4.5V (Non-reasoning) excels at general-purpose language tasks.

Last updated: June 21, 2026 View methodology →