- Text Generation
MiniMax M2.7 is a 230B-parameter Mixture-of-Experts large language model from MiniMax, with 10B active parameters and a 204,800-token context window, optimized for coding, agentic tool use,…
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GLM 5.1
- Text Generation
GLM 5.1 is Z.ai’s flagship open-weight Mixture-of-Experts large language model optimized for long-horizon agentic coding and software engineering tasks. It is notable for its very large context window, strong SWE-Bench Pro performance, and open-source MIT licensing.
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 5.1?
GLM 5.1 is a 754B-parameter open-weight Mixture-of-Experts large language model from Z.ai, designed primarily for agentic engineering and long-horizon coding workflows. It is mainly used for autonomous software development tasks such as repository-scale code generation, refactoring and bug fixing, and for agents that must plan, execute, and iteratively evaluate complex workflows over many hours. It is also applied in general-purpose long-context reasoning, tool use, and coding assistants where cost-efficient open-source deployment is important. GLM 5.1 succeeds GLM 5 and earlier GLM-series models from Zhipu AI/Z.ai, extending the family with improved long-horizon agent performance and state-of-the-art SWE-Bench Pro results.
Model capabilities
5 Core Capabilities
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Long-Horizon Coding
Executes complex software engineering tasks over many steps, including planning, implementation, testing, and iterative refinement for hours.
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Agentic Tool Use
Invokes tools and functions via function calling and MCP, coordinating multi-step workflows in autonomous or semi-autonomous agent setups.
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Long-Context Reasoning
Processes very large text inputs, such as full codebases or document collections, while maintaining coherence and reference over long contexts.
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Structured Text Output
Generates well-structured text and JSON-formatted outputs suitable for downstream automation, data pipelines, and application integration.
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Multilingual Text Support
Understands and generates text in multiple languages, enabling cross-lingual tasks, explanations, and content creation across diverse locales.
Use cases
6 Most Valuable Use Cases
- Long-Horizon Coding
- Agentic Workflows
- Software Debugging Support
- Tool-Use Orchestration
- Developer Productivity Assistant
- Reasoning Benchmarks Analysis
Transparent pricing
Cost Comparison
LLM API offers the lowest cost and highest limits for GLM 5.1-class models.
| Provider | Region | Latency | Throughput | Uptime | Input ($/1M) | Output ($/1M) | Context |
|---|---|---|---|---|---|---|---|
| LLM API BEST | Global | 80ms | 120 tps | 99.99% | $0.05 | $0.05 | 256K |
| Z.ai | Global | ~180ms | ~40 tps | ~99.9% | ~$0.20 | ~$0.20 | ~128K |
| OpenAI (closest: GPT-4.1 mini / o3-mini) | Global | ~150ms | ~80 tps | 99.9% | ~$0.15 | ~$0.60 | 128K |
| Anthropic (closest: Claude 3.5 Sonnet) | US East | ~200ms | ~50 tps | 99.9% | ~$3.00 | ~$15.00 | 200K |
| Google Cloud (closest: Gemini 1.5 Pro) | Global | ~190ms | ~60 tps | 99.9% | ~$1.50 | ~$5.00 | 128K |
Performance benchmarks
Technical Specifications
| Metric | GLM 5.1 (Z.ai) | GPT-4.1 (OpenAI) | Claude 3.5 Sonnet (Anthropic) |
|---|---|---|---|
| Avg Latency | ~180ms | ~220ms | ~250ms |
| Context Window | 128K | 128K | 200K |
| Input Price ($/1M) | $0.50 | $5.00 | $3.00 |
| Output Price ($/1M) | $1.50 | $15.00 | $15.00 |
| Max Output Tokens | 8K | 8K | 4K |
| Throughput | 48 tps | 40 tps | 35 tps |
| Uptime | 99.9% | 99.9% | 99.9% |
30-day usage via LLM API
- 11.8B
- Prompt tokens processed (last 30 days)
- 7.4B
- Completion tokens generated (last 30 days)
- 9.6M
- API requests served (last 30 days)
- 98.9%
- Average uptime over the 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
Automatically route each request to the optimal model across providers based on latency, cost, and quality—without changing your integration or redeploying code.
One endpoint, any model -
Cost-Aware Control
Enforce budgets and cost ceilings with per-project policies and dynamic model selection, so you never get surprised by a runaway bill in production.
Predictable AI spend -
Automatic Fallbacks
Define multi-provider failover trees that seamlessly retry on outages, timeouts, or rate limits to keep your AI features online when vendors go down.
Resilient by default -
Deep Observability
Centralize logs, traces, costs, and model metrics across every provider, giving your team one place to debug prompts, compare models, and tune performance.
See every token -
Task-Native Abstractions
Use high-level task APIs—chat, tools, RAG, evals—instead of vendor-specific formats, so you can swap models or providers without rewriting application logic.
Code to tasks, not vendors -
High-Throughput Batch
Run massive prompt batches through a unified pipeline with automatic chunking, concurrency control, and retries to maximize throughput and minimize per-request overhead.
Millions of calls, one API
Decision guide
When to Use — When NOT to Use
Use it if...
- You need a capable general-purpose LLM from the Zhipu GLM ecosystem for experimentation.
- You need Chinese-English bilingual support for chatbots, content generation, or productivity tools.
- Your use case involves building assistants that integrate GLM-style instruction following and dialogue.
- You need an alternative to Western-centric LLMs for regional compliance or diversification.
- Your use case involves prototyping multi-modal or tool-using agents on Z.ai’s infrastructure.
- You need a modern, frontier-level model for coding help, debugging, and code explanation.
Avoid if...
- You need guarantees about state-of-the-art performance on complex mathematical or scientific reasoning.
- Your workload requires tight integration with specific Western cloud ecosystems and managed services.
- You need long-term stability of APIs and versions already standardized in your stack.
- Your workload requires detailed, audited documentation and benchmarks in English for regulated industries.
- You need strict model behavior compatibility with OpenAI or Anthropic APIs and response formats.
- Your workload requires fully transparent information on training data sources and licensing constraints.
FAQ
Frequently Asked Questions
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What is GLM 5.1?
GLM 5.1 is a large language model from Z.ai accessible via LLM.API, designed for general-purpose text generation and reasoning tasks.
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What is GLM 5.1 best suited for?
GLM 5.1 is best for building chatbots, agents, and backend reasoning services that need strong instruction-following, tool use, and code understanding.
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How is GLM 5.1 priced on LLM.API?
LLM.API usage-based pricing for GLM 5.1 is set by LLM.API and may differ from Z.ai’s native pricing; check your LLM.API dashboard for current rates.
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What context window does GLM 5.1 support on LLM.API?
The effective context window for GLM 5.1 on LLM.API is defined by LLM.API’s configuration; see the model details in the LLM.API docs.
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How fast is GLM 5.1 when called through LLM.API?
Typical end-to-end latency depends on your region and request size, but GLM 5.1 is optimized on LLM.API for low-latency interactive workloads.
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Which modalities does GLM 5.1 support on LLM.API?
On LLM.API, GLM 5.1 currently accepts text input and returns text output; additional modalities depend on future LLM.API integrations.
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How do I call GLM 5.1 via the LLM.API?
Specify the GLM 5.1 model identifier in your LLM.API request, include your API key, and send standard chat or completion-style payloads.
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How does GLM 5.1 compare to similar models on LLM.API?
GLM 5.1 targets a balance of quality and cost, often cheaper than top-tier frontier models but stronger than many lightweight open-source baselines.
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What are the main limitations of GLM 5.1?
GLM 5.1 can hallucinate facts, may lack the very latest world knowledge, and should not be used without safeguards for high-stakes decisions.
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Does GLM 5.1 support streaming responses on LLM.API?
If streaming is enabled for this model in LLM.API, you can receive partial tokens incrementally by setting the streaming flag in your request.
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