- Text Generation
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GLM 4.6
- Text Generation
GLM 4.6 is Z.ai’s flagship mixture-of-experts large language model optimized for coding, reasoning, and agentic workflows. It is notable for its strong performance on code benchmarks and its very long ~200K token context window for complex tasks.
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 4.6?
GLM 4.6 is a mixture-of-experts large language model from Z.ai designed for advanced coding assistance, reasoning, and agent-style tool use. It is primarily used for software development workflows, including code generation, refactoring, and working over large repositories within integrated agents. It is also applied to general-purpose text generation and long-context tasks such as multi-step reasoning, data analysis, and orchestrated tool-calling pipelines. GLM 4.6 succeeds earlier GLM 4.x models such as GLM 4.5 in the broader GLM series developed by Zhipu AI (Z.ai).
Model capabilities
5 Core Capabilities
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Advanced Reasoning
Performs complex logical reasoning and multi-step problem solving, supporting tool use for sophisticated agentic workflows and decision-making tasks.
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Coding Assistance
Generates, analyzes, and debugs code across multiple languages, optimized for building coding agents and long-running software development workflows.
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Long-Context Handling
Processes and utilizes very long text contexts, enabling work with large documents, extended conversations, and multi-stage project instructions.
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Multilingual Text
Understands and generates text in multiple languages for general-purpose chat, knowledge querying, and content creation across diverse domains.
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Document Parsing
Extracts, interprets, and restructures information from long-form text documents, supporting summarization, reformatting, and targeted information retrieval.
Use cases
6 Most Valuable Use Cases
- Advanced code generation
- Software debugging agent
- Long-form document analysis
- Research question answering
- Workflow automation agents
- Tool-calling orchestration
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest performance for GLM 4.6–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.15 | $0.45 | 256K |
| Z.ai | Global | ~180ms | ~40 tps | ~99.9% | ~$0.60 | ~$1.80 | ~128K |
| OpenAI (closest: GPT-4.1) | Global | ~220ms | ~30 tps | 99.9% | ~$2.50 | ~$10.00 | 128K |
| Anthropic (closest: Claude 3.5 Sonnet) | US & EU | ~210ms | ~25 tps | 99.9% | ~$3.00 | ~$15.00 | 200K |
| Google (closest: Gemini 1.5 Pro) | Global | ~240ms | ~20 tps | ~99.9% | ~$2.00 | ~$8.00 | 1M |
Performance benchmarks
Technical Specifications
| Metric | GLM 4.6 (Z.ai) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.70 | $5.00 | $3.00 |
| Output Price ($/1M) | $2.10 | $15.00 | $15.00 |
| Max Output Tokens | 4K | 4K | 4K |
| Throughput | 70 tps | 50 tps | 45 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 7.8B
- Prompt tokens processed (30 days)
- 420M
- Completion tokens generated (30 days)
- 11.5M
- API requests served (30 days)
- 99.8%
- Average 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.
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Intelligent Model Routing
Automatically route each request to the optimal model by provider, latency, and capability so you ship faster without hard-coding vendor logic.
One endpoint, any model -
Cost-Aware Orchestration
Balance quality and price with per-request cost controls, policies, and mix-and-match providers so you never overspend on routine workloads.
More performance, less spend -
Resilient Fallback Flows
Define multi-provider fallback chains so timeouts, rate limits, or provider outages fail over automatically—keeping your AI features online.
Designed for failure modes -
End-to-End Observability
Get unified logs, traces, metrics, and per-provider analytics across all AI calls so you can debug prompts, tune latency, and track usage in one place.
One pane of glass -
Task-Level Abstractions
Define reusable tasks like chat, extraction, search, or tools once, then swap models underneath without touching application code.
Program tasks, not vendors -
High-Throughput Batch APIs
Send large batches of requests through a single pipeline with built-in retries, concurrency controls, and aggregation for massive throughput and lower effective cost.
Scale to millions of calls
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a capable general-purpose LLM for chatbots, Q&A, and virtual assistants.
- You need strong support for Chinese and English in a single unified model.
- Your use case involves building AI features into products targeting mainland China users.
- You need a commercial-friendly model with an API from a Chinese provider ecosystem.
- Your use case involves typical coding help, document drafting, and everyday office automation.
- You need a balance of capability and cost rather than absolute state-of-the-art performance.
Avoid if...
- You need frontier-level reasoning performance comparable to the very latest top-tier global models.
- Your workload requires guaranteed data residency and compliance strictly within US or EU jurisdictions.
- You need highly specialized domain models validated for medical, legal, or safety-critical decisions.
- Your workload requires extremely long-context processing of hundreds of thousands of tokens reliably.
- You need tight integration with Western enterprise platforms, tooling, and vendor support ecosystems.
- Your workload requires fully transparent open-source weights and on-premise self-hosting flexibility.
FAQ
Frequently Asked Questions
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What is GLM 4.6?
GLM 4.6 is a large language model from Z.ai focused on fast, general-purpose text generation and reasoning through the LLM.API gateway.
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What is GLM 4.6 best suited for?
GLM 4.6 is best for chatbots, code assistance, document summarization, and general reasoning where balanced quality and speed are important.
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What modalities does GLM 4.6 support via LLM.API?
Through LLM.API, GLM 4.6 currently supports text input and text output only.
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What is the context window of GLM 4.6?
GLM 4.6 supports up to a 128K token context window for prompts plus generated output combined.
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How fast is GLM 4.6 in terms of latency and throughput?
GLM 4.6 is optimized for low initial latency and high token throughput, making it suitable for interactive applications and batched backend workloads.
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How is GLM 4.6 priced when used through LLM.API?
LLM.API exposes GLM 4.6 with token-based pricing; see the LLM.API pricing page for current per‑million input and output token rates.
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How do I call GLM 4.6 using the LLM.API?
Specify the provider as "Z.ai" and the model name "GLM 4.6" in your LLM.API completion or chat endpoint request payload.
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How does GLM 4.6 compare to similar models on LLM.API?
Compared to similar general-purpose models, GLM 4.6 targets a balance of competitive reasoning quality, longer context, and cost efficiency.
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Does GLM 4.6 support tools or function calling via LLM.API?
If enabled by LLM.API, GLM 4.6 can consume structured function schemas and produce arguments for tool invocation like other compatible models.
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What are the main limitations of GLM 4.6?
GLM 4.6 can hallucinate facts, lacks real-time knowledge, and should not be used without human review for safety-critical or compliance-sensitive decisions.
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