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GLM-5-Turbo API: Endpoint & Pricing

GLM 5 Turbo is a fast, agent‑oriented large language model from Z.

Start Using API

What is GLM 5 Turbo?

GLM 5 Turbo is a closed‑source, speed‑optimized version of Z.ai’s GLM‑5 large language model, built for high‑throughput text generation and agentic workflows. It is mainly used to power software agents that perform long execution chains with complex instruction decomposition and multi‑step tool use. It is also applied in coding assistants and automated operations where stable behavior over long contexts and fast response times are critical. GLM 5 Turbo belongs to the GLM‑5 model family, continuing Z.ai’s GLM series developed after earlier GLM 4.x generations.

Can you self-host GLM 5 Turbo?

No. We found no public weights for GLM 5 Turbo on Hugging Face, so there is nothing to download, quantize or serve on your own GPUs: no VRAM budget, no vLLM or GGUF build to plan for. The only way to run it is over an API.

Skip the deploy — use LLM.API as your endpoint

No GPUs, no quantization trade-offs. Same model, OpenAI-compatible, up to 30% below list price.

Get API keySee API code ↓

Providers

Different companies host the same model. OpenRouter routes your request to one of them based on the routing mode you pick — Balanced (price + speed), Nitro (fastest), or Exacto (highest tool-calling accuracy).

List price by provider

InputOutput
llmapi-os$1.2 in
$4 out
zai$1.2 in
$4 out

Provider list prices; the LLM.API discount applies on top.

Provider Input Output Cache read /M Latency Throughput Uptime
llmapi-osin $1.2; out $4 per 1M tokens200K tokenstools, streaming, reasoning, JSON, structured
zaiin $1.2; out $4 per 1M tokens200K tokenstools, streaming, reasoning, JSON, structured

Prices and availability from the LLMAPI catalogue, updated nightly. Last updated 8 Oct 2026.

Try this model

Test GLM 5 Turbo right here — free to start.

GLM 5 Turbo
Hi! Want to test the model?

Suggestions for your first prompt

Code snippet

Call the model through the OpenAI-compatible API.

Switching from OpenAI? Change 2 lines.

- base_url="https://api.openai.com/v1"
- api_key="YOUR_OPENAI_KEY"
+ base_url="https://api.llmapi.ai/v1"
+ api_key="YOUR_LLMAPI_KEY"

Everything else stays the same — SDK, message format, tools, streaming. Set model to glm-5-turbo and the call runs unchanged.

bash
curl https://api.llmapi.ai/v1/chat/completions \
  -H "Authorization: Bearer $LLMAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "glm-5-turbo",
    "messages": [
      {"role": "user", "content": "Give me three crisp launch checklist items."}
    ]
  }'
from openai import OpenAI

client = OpenAI(
    api_key="YOUR_LLMAPI_KEY",
    base_url="https://api.llmapi.ai/v1",
)

resp = client.chat.completions.create(
    model="glm-5-turbo",
    messages=[
        {"role": "system", "content": "You are a precise product assistant."},
        {"role": "user", "content": "Give me three crisp launch checklist items."},
    ],
)
print(resp.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({
  apiKey: process.env.LLMAPI_KEY,
  baseURL: "https://api.llmapi.ai/v1",
});

const resp = await client.chat.completions.create({
  model: "glm-5-turbo",
  messages: [
    { role: "user", content: "Give me three crisp launch checklist items." },
  ],
});

console.log(resp.choices[0].message.content);
stream = client.chat.completions.create(
    model="glm-5-turbo",
    messages=[{"role": "user", "content": "Write a haiku about latency."}],
    stream=True,
)

for chunk in stream:
    delta = chunk.choices[0].delta.content
    if delta:
        print(delta, end="", flush=True)
{
  "model": "glm-5-turbo",
  "messages": [
    {"role": "system", "content": "You are a precise product assistant."},
    {"role": "user", "content": "Give me three crisp launch checklist items."}
  ]
}

ENDPOINTS

Supported endpoints & features

Base URL https://api.llmapi.ai/v1 · model ID glm-5-turbo · OpenAI-compatible.

  • POST /v1/chat/completionsChat Completions — supported
FeatureStatusHow to set it
Function calling (tools)Supportedtools, tool_choice
Parallel tool callsNot supportedparallel_tool_calls
JSON modeSupportedresponse_format: {"type": "json_object"}
Structured outputsSupportedresponse_format: {"type": "json_schema", …}
StreamingSupportedstream: true
Vision (image input)Not supportedimage_url content parts
Web searchNot supportedweb_search

ERRORS

Errors & fallback routing

CodeWhat it meansWhat to do
401Missing or invalid API key.Send Authorization: Bearer YOUR_LLMAPI_KEY and check the key is active.
429Rate limited, or the upstream provider is throttling the request.Back off and retry with jitter; the gateway also retries the request on another provider where one is available.
5xxUpstream provider error or timeout.Retry; fallback routing sends the retry to the next healthy provider for this model.

When a provider fails or throttles, the request is routed to the next provider serving this model — the ones listed in the providers table above.

GLM-5-Turbo in production

What the model is good at.

  • Conversational Chat

    Handles multi-turn conversations, follows instructions, and maintains context over long dialogues with fast responses optimized for production use.

  • Reasoning Tasks

    Performs multi-step logical reasoning, decomposing complex problems and synthesizing structured answers across scientific, mathematical, and strategic domains.

  • Code Generation

    Generates and edits code, supports agent-style coding workflows, and assists with debugging across multiple programming languages and frameworks.

  • Long-Form Writing

    Produces coherent long-form content such as articles, documentation, and narratives while following provided style, tone, and structural guidelines.

  • Multilingual Support

    Understands and generates text in multiple languages, enabling cross-lingual communication, content creation, and language adaptation tasks.

What you get when you call GLM-5-Turbo through LLM.API.

  • Unified AI Routing

    Dynamically route each request to the optimal model or provider based on latency, price, and quality—without changing your code or redeploying services.

    One endpoint, every model.

  • Cost-Aware Orchestration

    Automatically steer low-risk traffic to cheaper models while reserving premium models for critical paths, keeping performance high and infra spend predictable.

    Optimize tokens, not hacks.

  • Automatic Smart Fallbacks

    Define provider- and model-level fallback chains so outages, rate limits, or slow regions fail over seamlessly—no more brittle, provider-specific error handling.

    Resilience by default.

  • Full-Stack Observability

    Get unified traces, logs, latency, and cost metrics across all providers and models, wired into your existing APM and dashboards for real-time debugging.

    See every token hop.

  • Task-Level Abstractions

    Define tasks like chat, tools, embeddings, or rerank once and swap models underneath without changing payloads, glue code, or calling conventions.

    Code to tasks, not vendors.

  • High-Throughput Batch APIs

    Submit massive inference batches through a single pipeline with concurrency control, retry semantics, and cost visibility baked in for training data, evals, and backfills.

    Ship millions of calls safely.

When to Use — When NOT to Use

Use it if...

  • You need a cost-efficient general-purpose LLM for everyday chat, coding, and writing.
  • You need strong Chinese and English support for multilingual consumer or enterprise applications.
  • Your use case involves integrating an LLM via a simple HTTP API with familiar patterns.
  • You need fast inference for interactive assistants, chatbots, or basic agentic workflows.
  • Your use case involves typical software development tasks like code completion, refactoring, and debugging.
  • You need a commercially usable model with standard enterprise terms from a major Chinese provider.

Avoid if...

  • You need state-of-the-art reasoning on complex math, proofs, or adversarial benchmarks.
  • Your workload requires guaranteed compatibility with OpenAI-specific APIs, tools, or ecosystem features.
  • You need highly specialized domain performance validated by peer-reviewed benchmarks and regulatory certifications.
  • Your workload requires on-premise deployment with fully air-gapped infrastructure and offline updates.
  • You need tightly integrated vision, audio, and multimodal support beyond primarily text-based capabilities.
  • Your workload requires extremely long context handling comparable to the very latest frontier models.

LLM API rating for GLM-5-Turbo

★ 2.7/ 5.0LLM API rating
50% production adoption · 30% category benchmark · 20% tooling & value✓ Verified benchmark · Artificial Analysis
2.5Production adoptionNot yet tracked — no public usage data for this release
2.7Benchmark qualityArtificial Analysis Intelligence Index 26.6
3.0Developer ecosystemContext window, tool and JSON support, price against the category median
Reasoning & chat 2.7Coding 2.7Tool & function calling 4.0Long context 2.9
How is this rating calculated?

Every model gets a separate 1.0–5.0 score for each category it can be used in, so a model can rate high for coding and lower for vision. The score combines three parts:

  1. Production adoption — 50%. How widely the model is actually used in production: provider traffic, LiteLLM and gateway usage, open-weight downloads and its LMArena rank. Proven, widely used models score highest.
  2. Category benchmark — 30%. The public benchmark that matters for that category — the Artificial Analysis Intelligence Index and LMArena Elo for reasoning, LMArena coding Elo for coding, image and document support plus vision Elo for vision, tool and JSON support for function calling, measured speed for voice.
  3. Tooling & value — 20%. Context window, support for tools, structured outputs and streaming, and price compared with the category median.

Scores of 4.0 and above are shown in green, 3.0–3.9 in yellow, below 3.0 in red. When a model has no public data for a part, it gets a neutral value instead of an invented number.

A “Verified benchmark” mark (✓) means an independent lab — LMArena or Artificial Analysis — has actually tested that model, and the source figure is shown. Models without that mark are labelled “Estimated from published specs”: the score comes from the published context window, tool and schema support, price and adoption only, because no independent lab has tested them yet.

One exception: a brand-new top-tier model that scores very high on the independent benchmarks but has no usage history yet is rated on the benchmark (60%) and tooling & value (40%) alone, so a missing adoption figure can't hold it back. All inputs come from public sources and are refreshed weekly.

Adoption and benchmark inputs come from public sources — LMArena, Artificial Analysis Intelligence Index and published model specs. Updated weekly.

COMMUNITY

What developers say about GLM 5 Turbo

LMArena—Speed—Sources: LMArena, Artificial Analysis

Summarised from public developer discussions and write-ups. Opinions are the sources’, not LLM.API’s.

  • Developers on Reddit and Bswen report that GLM-5-Turbo provides a more stable experience than the base model, which was prone to thinking loops, gibberish outputs, and inconsistent quality across prompts.
  • Users note that GLM-5-Turbo is specifically optimized for 'agentic' workflows and the OpenClaw framework, excelling at tool calling and multi-step task chaining with noticeably faster inference speeds.
  • Benchmark testing by developers reveals a 'Jekyll and Hyde' performance pattern; while the model achieves perfect scores in backend coding and system prompt adherence, it can fail completely at general-purpose tasks like JSON generation.

SOURCES

Frequently Asked Questions

  • What is GLM 5 Turbo?

    GLM 5 Turbo is a Z.ai language model accessible via LLM.API, designed for fast, cost‑efficient text generation and reasoning workloads.

  • What is GLM 5 Turbo best suited for?

    GLM 5 Turbo is best for general chat, code assistance, tool-using agents, and production workloads needing low latency and good reasoning at moderate context sizes.

  • What context window does GLM 5 Turbo support on LLM.API?

    GLM 5 Turbo supports a context window up to 32K tokens on LLM.API, suitable for moderately long conversations and documents.

  • How fast is GLM 5 Turbo in terms of latency?

    GLM 5 Turbo is optimized for low latency, typically returning first tokens within a few hundred milliseconds for short prompts, excluding network overhead.

  • What modalities does GLM 5 Turbo support through LLM.API?

    Through LLM.API, GLM 5 Turbo currently supports text-only input and output; it does not natively process images, audio, or video.

  • How is GLM 5 Turbo priced on LLM.API?

    GLM 5 Turbo uses a pay-as-you-go token-based pricing model on LLM.API, with separate per‑token rates for input and output usage.

  • How do I call GLM 5 Turbo via the LLM.API?

    You select the GLM 5 Turbo model name in your LLM.API request and send standard Chat Completions-style messages with your API key.

  • How does GLM 5 Turbo compare to similar turbo-class models?

    Compared to similar turbo-class models, GLM 5 Turbo targets a balance of strong reasoning, competitive pricing, and responsive throughput for mainstream applications.

  • What are the main limitations of GLM 5 Turbo?

    GLM 5 Turbo can hallucinate facts, struggles with very long multi-step reasoning beyond its context, and does not provide real-time or guaranteed correct information.

  • Can I fine-tune GLM 5 Turbo through LLM.API?

    Direct fine-tuning of GLM 5 Turbo is not supported on LLM.API; instead, you should use prompt engineering and system prompts to specialize behavior.

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    • Get one key to every model

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