GLM 4.5v

Z.ai chat

GLM 4.5v is a Z.ai chat model.It supports a 128,000-token context windowwith up to 32,000 output tokens.Input is priced at $0.600/M tokens and output at $1.80/M tokens. Capabilities include function calling, vision. Route GLM 4.5v via Future AGI's Agent Command Center for unified observability, caching, and 15 routing strategies including cost-optimized fallback.

Pricing source: litellm Last verified: Oct 22, 2026 View source ↗
Cost calculator

Estimate GLM 4.5v spend

Pick a workload, fine-tune the sliders, and see the monthly bill.

~3K in / ~400 out · 5K req/day
3,000
0128,000
400
032,000
5,000
01,000,000
Per request
$0.002520
in $0.001800 · out $0.000720
Per day
$12.60
5,000 requests
Per month
$384
152,188 requests

Estimate uses $0.6000/M input · $1.80/M output. Provider pricing changes. Production costs vary with retries, streaming overhead, and tool-call rounds.
Want this for free? Cache + route via Agent Command Center — first 100K requests and 100K cache hits free every month.

Pricing

Per-token rates, expressed in USD per 1M tokens. Verified Oct 22, 2026.

Input $0.600/M
Output $1.80/M
Blended (3:1 in:out) $0.900/M
Cross-model comparable price

Context & limits

Context window
128,000 tokens
Max input
128,000 tokens
Max output
32,000 tokens
Modalities
vision, text

Capabilities

  • Function calling ✓ supported
  • Parallel tool calls — not advertised
  • Vision input ✓ supported
  • Audio input — not advertised
  • Audio output — not advertised
  • PDF input — not advertised
  • Streaming ✓ supported
  • Structured output — not advertised
  • Prompt caching — not advertised
  • Reasoning — not advertised

Strengths & caveats — when to pick GLM 4.5v

Data-driven from GLM 4.5v's percentile within chat peers.

Where it's strong

  • +agentic workflows that depend on reliable tool calls
  • +document, chart, and screenshot understanding

Watch out for

  • !strict structured output — no JSON-schema enforcement, expect retry loops

Benchmark scores

Reported public benchmark numbers. Each row links to the source. Faded bar shows 6-peer average for context.

Arena ELO (coding)code· style control↓3% vs peers
Captured Oct 20, 2026
Chatbot Arena ELOgeneral· style control↓3% vs peers
Captured Oct 20, 2026
Arena Visionmultimodal· style control↓3% vs peers
Captured Oct 19, 2026
Try it

Call GLM 4.5v via Agent Command Center

One OpenAI-compatible endpoint. Routing, fallback, semantic caching, guardrails, and cost tracking come along for the ride. First 100K requests + 100K cache hits free every month.

SDK
Native Future AGI client (agentcc / @agentcc/client). Per-call metadata — provider, cost, latency, cache hit, request id — is returned on x-agentcc-* response headers, so any HTTP client can read it.
# GLM 4.5v via the Agent Command Center Python SDK
# pip install agentcc
import os
from agentcc import AgentCC

client = AgentCC(
    api_key=os.environ["AGENTCC_API_KEY"],   # from app.futureagi.com → Settings → API Keys
    base_url="https://gateway.futureagi.com/v1",
)

resp = client.chat.completions.create(
    model="zai/glm-4-5v",
    messages=[{"role": "user", "content": "Hello, GLM 4.5v!"}],
)

print(resp.choices[0].message.content)
print(f"Tokens: {resp.usage.total_tokens}")

# Per-call gateway metadata is returned on x-agentcc-* response headers.
# When you need it programmatically, use .with_raw_response to get them:
raw = client.chat.completions.with_raw_response.create(
    model="zai/glm-4-5v",
    messages=[{"role": "user", "content": "Same call, but I want the headers."}],
)
print("Provider:", raw.headers.get("x-agentcc-provider"))
print("Latency:", raw.headers.get("x-agentcc-latency-ms"), "ms")
print("Cost:   ", raw.headers.get("x-agentcc-cost"), "USD")
print("Cache:  ", raw.headers.get("x-agentcc-cache"))
Set AGENTCC_API_KEY with a key fromapp.futureagi.com.Gateway docs ↗
Advanced: fallback + cache config (YAML) ▸
strategy: cost-optimized
targets:
  - model: glm-4-5v
    provider: zai
    weight: 80
fallbacks:
  - model: grok-4
    provider: azure-ai-foundry
  - model: grok-3
    provider: azure-ai-foundry
guardrails: [pii, prompt-injection, secrets]
cache: { exact: true, semantic: true }

Explore similar models

Grouped by Chatbot Arena tier (GLM 4.5v sits at 1352 ELO). Side-by-side links appear where a comparison page exists; otherwise, open the model profile.

FAQ

How much does GLM 4.5v cost?

Input is priced at $0.600 per 1M tokens and output at $1.80 per 1M tokens (Z.ai, last verified Oct 22, 2026).

What is the context window of GLM 4.5v?

GLM 4.5v supports a 128,000-token context window with up to 32,000 output tokens.

Does GLM 4.5v support function calling?

Yes — GLM 4.5v supports function (tool) calling.

Is GLM 4.5v good for production?

GLM 4.5v is well-suited for agentic workflows that depend on reliable tool calls and document, chart, and screenshot understanding. Consider alternatives if you need strict structured output — no JSON-schema enforcement, expect retry loops.

How can I route to GLM 4.5v with fallback?

Use Agent Command Center: a single OpenAI-compatible endpoint that supports cost-optimized routing, latency-aware retries, model fallback, and shadow traffic. Configure once, swap models without app changes.

Useful links for GLM 4.5v

Official sources, independent benchmarks, and pricing aggregators — no random search-engine guesses.