- Instruction Following
Claude Haiku 4.5 is Anthropic’s fastest, most cost-efficient Claude 4.5-generation model, offering near-frontier intelligence with low latency and pricing optimized for large-scale use. It supports long-context,…
Powered by Z.ai
GLM 5
- Instruction Following
GLM 5 is Z.ai’s fifth-generation large language model, a large open-source Mixture-of-Experts foundation model focused on advanced reasoning and long-horizon agent workflows. It is notable for its frontier-scale parameter count (around 744–745B total, ~44B active) and very long context window of about 200K tokens.
Providers Comparison
Speed
Output tokens per second · Higher is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
End-to-End Response Time
Seconds · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
Price
USD per 1M tokens (blended) · Lower is better
L
LLM API
S
SiliconFlow (FP8)
G
GMI
C
CoreWeave
N
Novita
D
DeepSeek
P
Parasail (FP8)
D
DeepInfra (FP4)
About the model
What is GLM 5?
GLM 5 is an open-source flagship large language model from Z.ai designed as a Mixture-of-Experts system with roughly 744–745 billion total parameters and a ~200K token context window. It is mainly used for complex software and systems engineering, long-horizon agentic workflows, and production-grade coding assistance. It is also applied to advanced multi-step reasoning, planning, and creative or analytical text generation across general-purpose chat and knowledge-intensive tasks. GLM 5 belongs to Z.ai’s GLM (General Language Model) family and succeeds earlier generations such as GLM-4.5 and GLM-4.7.
Model capabilities
5 Core Capabilities
-
Advanced Chatting
Supports natural, context-aware conversations over long sessions, handling instructions, explanations, and multi-turn dialogue for diverse applications.
-
Long-Context Reasoning
Performs multi-step, long-horizon reasoning over large context windows, enabling complex analysis, planning, and problem decomposition tasks.
-
High-Level Coding
Generates and edits code, builds full-stack applications, and solves software engineering tasks with strong benchmark performance.
-
Multilingual Abilities
Understands and generates text in multiple languages, enabling cross-lingual question answering, content creation, and global applications.
-
Multimodal Processing
Processes and reasons over both text and visual inputs via related GLM variants, supporting integrated multimodal workflows.
Use cases
6 Most Valuable Use Cases
- Code Generation Assistance
- Multilingual Content Creation
- Customer Support Chatbots
- Document Summarization
- Legal Text Analysis
- Regulation Change Monitoring
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance access to GLM 5–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~140ms | ~220 tps | ~99.99% | ~$0.12 | ~$0.12 | ~256K |
| Z.ai | Global | ~220ms | ~120 tps | ~99.9% | ~$0.40 | ~$0.40 | ~128K |
| OpenAI (GPT-4.1-equivalent) | Global | ~300ms | ~160 tps | ~99.9% | ~$2.50 | ~$10.00 | ~128K |
| Anthropic (Claude 3.5-equivalent) | US East | ~320ms | ~150 tps | ~99.9% | ~$3.00 | ~$15.00 | ~200K |
| Google (Gemini 1.5 Pro-equivalent) | Global | ~280ms | ~140 tps | ~99.9% | ~$1.50 | ~$5.00 | ~1M |
Performance benchmarks
Technical Specifications
| Metric | GLM 5 | GPT-4.1 | Claude 3.5 Sonnet |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.60 | $5.00 | $3.00 |
| Output Price ($/1M) | $1.80 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 4K | 4K |
| Throughput | 120 tps | 100 tps | 90 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.4B
- Prompt tokens processed (30 days)
- 7.8B
- Completion tokens generated (30 days)
- 9.6M
- API requests served (30 days)
- 99.8%
- Avg uptime over last 30 days
Architecture & Integration
Why Build on LLM.API?
One unified API. Every major model. Built-in reliability, cost control, and observability.
-
Unified AI Routing
Define routing rules once, then dynamically steer traffic across providers and models via a single endpoint—no client changes, just smarter utilization.
One endpoint, every model -
Cost-Aware Execution
Balance price and performance automatically with per-request policies that pick the cheapest model meeting your latency and quality constraints.
Spend less, ship more -
Resilient Fallbacks
Automatically retry requests on backup models or providers when failures or timeouts occur, so your apps stay responsive even when vendors don’t.
No single point of failure -
Full-Stack Observability
Get centralized logs, traces, and metrics across every provider to debug latency spikes, monitor spend, and tune prompts in one place.
See every token, everywhere -
Task-Level Abstractions
Describe tasks like “chat”, “embed”, or “moderate” and let LLM.API pick and orchestrate the right models and tools behind the scenes.
Think tasks, not models -
High-Throughput Batch
Submit massive batches through a unified API with built-in concurrency control, retries, and cost tracking for offline jobs and backfills.
Millions of calls, one pipeline
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a capable general-purpose LLM from a major Chinese model provider.
- You need solid performance on Chinese and English tasks like chat or Q&A.
- Your use case involves typical enterprise workloads such as summarization, extraction, and rewriting.
- Your use case involves integrating with the Zhipu or Z.ai ecosystem and tooling.
- You need a modern foundation model likely optimized for cost-effective large-scale deployments.
- Your use case involves experimenting with frontier Chinese models for research or benchmarking comparisons.
Avoid if...
- You need guaranteed best-in-class reasoning performance compared to top proprietary Western frontier models.
- Your workload requires tightly validated support for niche languages beyond Chinese and English.
- You need detailed, battle-tested documentation and community examples in English-only developer ecosystems.
- Your workload requires strong assurances about US or EU data residency and compliance.
- You need seamless integration with specific US cloud-native AI services or proprietary tooling.
- Your workload requires extensively audited safety profiles and third-party red-teaming in Western markets.
FAQ
Frequently Asked Questions
-
What is GLM 5?
GLM 5 is a large language model from Z.ai accessible via LLM.API for general-purpose text generation and understanding tasks.
-
What is the context window of GLM 5?
GLM 5 supports a context window of up to 32,000 tokens for each request, including input and output tokens.
-
Which modalities does GLM 5 support?
GLM 5 currently supports text-only input and output when accessed through LLM.API.
-
How is GLM 5 priced on LLM.API?
GLM 5 usage on LLM.API is billed per input and output token, with exact rates shown in your LLM.API pricing dashboard.
-
How fast is GLM 5 in terms of latency?
GLM 5 typically returns first tokens within a few hundred milliseconds, depending on prompt size, load, and your LLM.API region.
-
How do I call GLM 5 via LLM.API?
Specify provider "zai" and model "glm-5" in your LLM.API request, then send a standard chat or completion payload.
-
What is GLM 5 best suited for?
GLM 5 is best for cost-efficient code assistance, general chat, and tool-using agents that need a balanced capability-to-price ratio.
-
How does GLM 5 compare to similar models?
Compared to similar mid-tier models, GLM 5 targets lower cost while maintaining competitive reasoning and coding quality for most production workloads.
-
Does GLM 5 support function calling or tools via LLM.API?
Yes, GLM 5 supports structured tool or function calling when you define tools in your LLM.API request schema.
-
What are the main limitations of GLM 5?
GLM 5 can hallucinate facts, struggle with very long multi-step reasoning, and should not be used without human review for safety-critical decisions.
Compare
Competitive Models
- Instruction Following
Phi 4 Mini Instruct is a lightweight, 3.8B-parameter open large language model from Microsoft focused on strong reasoning, long-context understanding, and efficient deployment on modest hardware.
- Instruction Following
Gemini 3.1 Flash Lite is Google’s ultra-fast, low-cost Gemini 3-series language model optimized for high-volume, latency-sensitive applications. It prioritizes speed and cost-efficiency while still supporting multimodal…