GLM-5 (Non-reasoning)
Zhipu AI • llm
Grande modelo de linguagem (llm) desenvolvido pela Zhipu AI — Intelligence Index 41/100 no Artificial Analysis; US$ 1.00/1M tokens de entrada; 81 tokens/s de velocidade.
Specifications
Context Window
—
Input Price/1M
$1.00
Output Price/1M
$3.20
Parameters
—
Speed
67 tok/s
Latency (TTFT)
1.2s
Benchmarks
GLM-5 (Non-reasoning) results on the main AI model evaluation benchmarks. Higher scores indicate better performance.
Agentic
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Terminal-Bench Hard | 39.0 | 100.0 | — |
Coding
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Coding Index | 39.0 | 100.0 | Artificial Analysis official API |
| SciCode | 38.0 | 100.0 | — |
Long Context
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA-LCR | 37.0 | 100.0 | — |
overall
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| AA Intelligence Index | 40.6 | 100.0 | Artificial Analysis official API |
Reasoning
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| GPQA Diamond | 67.0 | 100.0 | Artificial Analysis official API |
| IFBench | 55.0 | 100.0 | — |
| HLE | 7.0 | 100.0 | — |
Tool Use
| Benchmark | Score | Maximum | Methodology |
|---|---|---|---|
| Tau²-Bench | 97.0 | 100.0 | — |
Information
- Release date
- February 11, 2026
- Tool Calling
- ❌ Not supported
- Vision
- ❌ Not supported
- Audio
- ❌ Not supported
Full Analysis: GLM-5 (Non-reasoning)
What is GLM-5 (Non-reasoning)?
GLM-5 (Non-reasoning) is an AI model developed by Zhipu AI, classified as a llm model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Zhipu AI's cloud API.
Pricing & Costs in 2026
GLM-5 (Non-reasoning) is usage-based, priced at $1/1M input tokens and $3.2/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The mid-range pricing balances quality and cost for most professional applications.
Benchmarks & Performance
GLM-5 (Non-reasoning) was evaluated on 9 different benchmarks, covering categories like Agentic, Coding, Long Context, overall, Reasoning, Tool Use. Results show exceptional 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-5 (Non-reasoning) is suitable for a wide range of AI applications: text generation, summarization, translation, and general assistance.
Comparison with Alternatives
In the 2026 AI model ecosystem, GLM-5 (Non-reasoning) competes directly with similarly capable models. Zhipu 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-5 (Non-reasoning)?
GLM-5 (Non-reasoning) is an AI model developed by Zhipu AI. It is a llm model.
How much does GLM-5 (Non-reasoning) cost?
GLM-5 (Non-reasoning) costs $1/1M input tokens and $3.2/1M 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-5 (Non-reasoning) compare with other models?
In available benchmarks, GLM-5 (Non-reasoning) scored: Terminal-Bench Hard: 39/100, AA Coding Index: 39/100, SciCode: 38/100. See the full table above for a detailed comparison.
Is GLM-5 (Non-reasoning) open source?
No, GLM-5 (Non-reasoning) is a proprietary model from Zhipu AI. It is available via cloud API. For open source alternatives, check our open source model ranking.
What is GLM-5 (Non-reasoning) best for?
GLM-5 (Non-reasoning) excels at general-purpose language tasks.
Last updated: June 01, 2026 • View methodology →