- Instruction Following
Seed-2.0-Mini is a compact multimodal large language model from ByteDance Seed optimized for latency-sensitive, high-concurrency, and cost-sensitive applications, offering long context and flexible reasoning modes.
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GLM 4.7 Flash
- Instruction Following
GLM 4.7 Flash is a 30B-class Mixture-of-Experts language model from Z.ai, optimized for speed and efficiency while maintaining strong performance on coding and agentic reasoning 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 4.7 Flash?
GLM 4.7 Flash is an efficient 30B A3B MoE large language model developed by Z.ai as the high-speed variant of its GLM-4.7 generation. It is mainly used for fast, high-quality code generation and software engineering workflows, and for agent-style applications that require tool use, planning, and long-context reasoning. It belongs to the GLM-4.7 family, a successor line in Z.ai’s GLM series that advances programming capability and multi-step reasoning over earlier GLM-4.x models.
Model capabilities
5 Core Capabilities
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Fast Text Chat
Generates and continues multi-turn text conversations with low latency, optimized for interactive chat, drafting, and instruction following.
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Code Generation
Produces source code, fixes bugs, and explains programming concepts, leveraging strong performance on coding and software engineering benchmarks.
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Tool-Use Reasoning
Plans and interprets tool calls for agentic workflows, demonstrating strong performance on benchmarks evaluating multi-step tool use.
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Long-Context Handling
Processes high-context text inputs efficiently using a Mixture-of-Experts architecture, maintaining quality over long prompts and histories.
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Multilingual Text
Understands and generates text in multiple languages, supporting cross-lingual tasks like drafting, Q&A, and basic translation assistance.
Use cases
6 Most Valuable Use Cases
- Real-time Chatbots
- Invoice Data Extraction
- Legal Case Research
- Compliance Case Monitoring
- Software Development Assistance
- Agentic Tool Orchestration
Transparent pricing
Cost Comparison
LLM API offers the lowest token prices and fastest responses for GLM 4.7 Flash–class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.05 | $0.15 | 256K |
| Z.ai | Global | ~150ms | ~80 tps | ~99.9% | ~$0.10 | ~$0.30 | ~200K |
| OpenAI (closest: gpt-4.1-mini) | Global | ~180ms | ~60 tps | 99.9% | ~$0.15 | ~$0.60 | 128K |
| Anthropic (closest: Claude 3.5 Haiku) | US East | ~200ms | ~50 tps | 99.9% | ~$0.12 | ~$0.48 | 200K |
| Moonshot (closest: Moonshot V1-Flash) | Asia Pacific | ~220ms | ~45 tps | ~99.9% | ~$0.09 | ~$0.36 | ~200K |
Performance benchmarks
Technical Specifications
| Metric | GLM 4.7 Flash (Z.ai) | GPT‑4.1 Mini (OpenAI) | Claude 3.5 Haiku (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M tokens) | $0.10 | $0.15 | $0.25 |
| Output Price ($/1M tokens) | $0.30 | $0.60 | $0.80 |
| Max Output Tokens | 4K | 8K | 8K |
| Throughput | 80 tps | 60 tps | 50 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 12.4B
- Prompt tokens processed (last 30 days)
- 3.1M
- API requests served (last 30 days)
- 15.8B
- Completion tokens generated (last 30 days)
- 99.8%
- Average uptime (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 AI Routing
Dynamically route requests across providers and models based on latency, reliability, and capability—no client changes required when your stack evolves.
One endpoint, all models. -
Cost-Aware Orchestration
Automatically pick cost-efficient models and enforce per-project budgets, so you can scale AI usage confidently without surprise bills or manual price tuning.
Control spend at scale. -
Resilient Fallbacks
Define multi-provider fallback chains that trigger on errors, slow responses, or quota limits, ensuring your AI features stay online even when vendors fail.
No single point of failure. -
Full-Stack Observability
Get centralized logs, traces, and metrics for every request across all models and providers, enabling fast debugging, regression detection, and performance tuning.
See every token, everywhere. -
Task-Level Abstractions
Define reusable task types—chat, RAG, tools, structured outputs—then swap models or providers underneath without changing how your application code is written.
Code to tasks, not models. -
High-Throughput Batch
Submit large batch jobs for embeddings, generations, or evaluations with automatic chunking, retries, and provider fan-out to maximize throughput and minimize latency.
Millions of calls, one job.
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a low-cost, fast chat-style model for straightforward question-answering workloads.
- You need to prototype general LLM features where occasional reasoning mistakes are acceptable.
- Your use case involves short-context customer support bots handling common, repetitive inquiries.
- Your use case involves simple text transformations like rewriting, summarizing, and format conversions.
- You need a lightweight assistant for code snippets, comments, and minor refactoring tasks.
- Your use case involves multilingual but simple interactions that do not demand nuanced cultural reasoning.
Avoid if...
- You need frontier-level reasoning performance for complex problem solving or scientific workflows.
- Your workload requires highly reliable legal, medical, or compliance-critical content generation.
- You need very long-context processing, such as whole-codebase analysis or book-length reviews.
- Your workload requires cutting-edge code synthesis, debugging, and architecture-level software design.
- You need top-tier instruction following, nuanced safety controls, and minimal hallucination risk.
- Your workload requires specialized vision, speech, or multimodal capabilities beyond text-only interactions.
FAQ
Frequently Asked Questions
-
What is GLM 4.7 Flash?
GLM 4.7 Flash is a fast, cost-efficient large language model from Z.ai optimized for general-purpose text generation and assistant-style interactions.
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What is GLM 4.7 Flash best suited for?
GLM 4.7 Flash is best for chatbots, rapid prototyping, lightweight agents, and high-throughput applications where latency and cost are critical.
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What context window does GLM 4.7 Flash support on LLM.API?
GLM 4.7 Flash supports a 128K-token context window on LLM.API, enabling long conversations and large prompt documents.
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How fast is GLM 4.7 Flash in terms of latency?
GLM 4.7 Flash is tuned for low-latency responses, typically returning first tokens in under a second for small to medium prompts.
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Which input and output modalities does GLM 4.7 Flash support?
GLM 4.7 Flash currently supports text input and text output only when accessed via LLM.API.
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How is GLM 4.7 Flash priced on LLM.API?
GLM 4.7 Flash uses a pay-per-token pricing model on LLM.API; check your LLM.API dashboard for the latest input and output token rates.
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How do I call GLM 4.7 Flash through LLM.API?
Specify the model name "glm-4.7-flash" (or the exact identifier in the catalog) in your LLM.API requests using the standard chat or completion endpoints.
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How does GLM 4.7 Flash compare to heavier GLM versions?
Compared to larger GLM variants, GLM 4.7 Flash trades some reasoning depth for significantly lower latency and cost.
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Does GLM 4.7 Flash support tools or function calling via LLM.API?
Yes, GLM 4.7 Flash can be used with LLM.API's tool or function calling interface when you define tools in the request schema.
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What limitations should I be aware of when using GLM 4.7 Flash?
GLM 4.7 Flash may hallucinate facts, struggle with highly specialized domains, and should not be used for safety-critical or compliance-critical decisions without human review.
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