- Text Generation
Rerank 4 Fast is Cohere’s fourth-generation multilingual reranking model optimized for low-latency, high-throughput retrieval with a context window of around 32K–33K tokens. It is designed to…
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GLM 4.7
- Text Generation
GLM 4.7 is Z.ai’s flagship large language model, optimized for strong coding performance and stable multi-step reasoning. It is notable for its very large context window and open-source availability under Apache 2.0.
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 4.7?
GLM 4.7 is a frontier-scale large language model from Z.ai, designed as a general-purpose assistant with particular strengths in software development and complex reasoning tasks. It is widely used for code generation, debugging, and agent-style tools that require reliable multi-step execution, as well as for advanced chat, analysis, and content creation workloads across long contexts. It belongs to Z.ai’s GLM model family as a successor to earlier GLM-4.x releases and is provided as an open-source Apache 2.0 MoE-based model.
Model capabilities
5 Core Capabilities
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Natural Language Chat
Engages in multi-turn, natural conversations, following instructions and maintaining context over long text-only interactions with high coherence.
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Advanced Coding
Generates, edits, and explains source code, supporting complex programming workflows and real-world development environments with strong reliability.
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Structured Outputs
Produces well-formed JSON and other structured formats, supporting function calling and tool invocation for agentic applications.
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Multi-step Reasoning
Handles complex, long-horizon tasks with stable multi-step reasoning, suitable for agents and tool-using workflows.
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Multilingual Text
Understands and generates text in multiple languages, enabling cross-lingual tasks, content creation, and language transformation.
Use cases
6 Most Valuable Use Cases
- Agentic code generation
- Complex reasoning workflows
- Long-context document analysis
- Software debugging assistant
- Developer productivity tooling
- Application prototyping support
Transparent pricing
Cost Comparison
Save up to 70% on GLM‑class models with LLM API’s optimized pricing.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | ~150ms | ~120 tps | 99.99% | $0.20 | $0.30 | 256K |
| Z.ai | Global | ~220ms | ~80 tps | ~99.9% | ~$0.35 | ~$0.60 | ~128K |
| OpenAI (closest: GPT-4.1-mini / o3-mini class) | Global | ~250ms | ~90 tps | 99.9% | ~$0.50 | ~$1.50 | 128K |
| Anthropic (closest: Claude 3.5 Sonnet) | US & EU | ~260ms | ~70 tps | ~99.9% | ~$3.00 | ~$15.00 | 200K |
| Azure AI (closest: GPT-4.1 via Azure) | US East / EU West | ~280ms | ~85 tps | 99.9% | ~$2.80 | ~$11.20 | 128K |
Performance benchmarks
Technical Specifications
| Metric | GLM 4.7 (Z.ai) | GPT-4.1 Mini (OpenAI) | Claude 3 Haiku (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.20 | $0.15 | $0.25 |
| Output Price ($/1M) | $0.60 | $0.60 | $0.80 |
| Max Output Tokens | 8K | 4K | 8K |
| Throughput | ~80 tps | ~100 tps | ~70 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 7.8B
- Prompt tokens processed (30 days)
- 5.3B
- Completion tokens generated (30 days)
- 22.5M
- 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.
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Unified AI Routing
Intelligently route each request across models and providers based on latency, cost, and quality. One API, dynamic routing rules, no client rewrites.
One endpoint, any model -
Cost-Aware Controls
Enforce per-project and per-model budgets, caps, and policies at the gateway. Optimize spend automatically without touching your application logic.
Control cost per call -
Resilient Fallback Flows
Automatically fail over to backup models or providers on errors, timeouts, or rate limits. Keep your AI features online without custom retry logic.
No single point of failure -
End-to-End Observability
Trace every request across models with metrics, logs, and timelines. Debug latency, failures, and behavior from a single observability layer.
See every token hop -
Task-Level Orchestration
Define higher-level tasks—like classification or extraction—then plug in any compatible model. Swap models without rewriting business logic.
Code to tasks, not models -
High-Throughput Batch
Send massive batches through one optimized pipeline with concurrency, backoff, and retries built in. Maximize throughput while respecting provider limits.
Millions of calls, one pipe
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a general-purpose LLM from Z.ai for common chat and Q&A.
- You need reasonably capable coding assistance for scripting, debugging, and small software components.
- Your use case involves multilingual text understanding and generation across major world languages.
- Your use case involves building chatbots or agents that handle routine business queries.
- You need mid-tier reasoning for data extraction, classification, and short-form content drafting.
- Your use case involves experimentation with Z.ai’s ecosystem and related model tooling.
Avoid if...
- You need frontier-level reasoning performance comparable to the very latest flagship models.
- Your workload requires highly specialized domain expertise, such as medical or legal decision-making.
- You need robust, extensively audited enterprise controls, governance, and compliance certifications today.
- Your workload requires extremely long context windows for book-length documents or transcripts.
- You need cutting-edge multimodal capabilities across text, images, and complex tools in one model.
- Your workload requires proven large-scale production benchmarks and broad industry adoption as of now.
FAQ
Frequently Asked Questions
-
What is GLM 4.7?
GLM 4.7 is a large language model from Z.ai focused on strong general-purpose reasoning and code generation, accessible via the LLM.API gateway.
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What is GLM 4.7 best suited for?
GLM 4.7 is best for multi-step reasoning, code assistance, and building chat-style assistants that require stable, predictable behavior.
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How is GLM 4.7 priced when used through LLM.API?
GLM 4.7 usage is billed per token through LLM.API, with exact input and output token rates defined in your LLM.API pricing plan.
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What is the context window of GLM 4.7?
GLM 4.7 supports a large context window suitable for multi-turn chats and long documents; check LLM.API docs for the exact token limit.
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How fast is GLM 4.7 in terms of latency?
Typical latencies are comparable to other mid-to-large LLMs, with streaming responses available to reduce perceived delay for end users.
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Which modalities does GLM 4.7 support via LLM.API?
Through LLM.API, GLM 4.7 supports text input and output; additional modalities depend on the capabilities LLM.API exposes for this model.
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How do I call GLM 4.7 using the LLM.API?
Use the LLM.API chat or completion endpoint with the model identifier for GLM 4.7, including your API key and standard request parameters.
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How does GLM 4.7 compare to similar LLMs?
GLM 4.7 targets a balance of quality, speed, and cost comparable to mainstream general-purpose LLMs in its size and capability class.
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What are the main limitations of GLM 4.7?
GLM 4.7 can hallucinate facts, lacks real-time knowledge or browsing, and should not be used without human review for high-stakes decisions.
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Does GLM 4.7 support function calling or tool use?
Function calling and tool-use support depend on LLM.API’s integration for GLM 4.7; check the model features table in the LLM.API documentation.
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