GLM-4.5V
zai/glm-4-5vVon Z.AI / Zhipu · Familie: glm · veröffentlicht 2025-08-11 · Wissensstand: 2025-04
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Fähigkeiten
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding60
- Tool calling40/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)0/20
- Provider availability10/10
Agents70
- Tool calling35/35
- Structured output0/25
- Reasoning15/15
- Output token limit10/15
- Provider availability10/10
JSON / structured output45
- Structured output / JSON mode0/50
- Tool calling20/20
- Temperature control10/10
- Price-friendly for high-volume15/20
Cost efficiency53
- Headline price (log-scaled)53/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Vision82
- Accepts image input50/50
- Context window (10K → 1M)12/30
- Has published price10/10
- Provider availability10/10
Production-readiness94
- Number of independent providers40/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers4/10
- Official model (not derivative)15/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $3.90 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $7.80 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $2.10 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $6.60 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $8.28 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Preis-Details
Empfohlene Preise von zai · glm-4.5v
Günstigster Anbieter: siliconflow-cn · $0.140 Eingabe + $0.860 Ausgabe
Bei 12 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| Z.AI zai | glm-4.5v | $0.600 | $1.80 | 64K | 2025-08-11 |
| Zhipu AI zhipuai | glm-4.5v | $0.600 | $1.80 | 64K | 2025-08-11 |
| OpenRouter openrouter | z-ai/glm-4.5v | $0.600 | $1.80 | 64K | 2025-08-11 |
| Vercel AI Gateway vercel | zai/glm-4.5v | $0.600 | $1.80 | 66K | 2025-08-11 |
| 302.AI 302ai | glm-4.5v | $0.290 | $0.860 | 64K | 2025-08-12 |
| NanoGPT nano-gpt | z-ai/glm-4.5v | $0.600 | $1.80 | 64K | 2025-11-22 |
| SiliconFlow (China) siliconflow-cn | zai-org/GLM-4.5V | $0.140 | $0.860 | 66K | 2025-08-13 |
| Jiekou.AI jiekou | zai-org/glm-4.5v | $0.600 | $1.80 | 66K | 2026-01 |
| NovitaAI novita-ai | zai-org/glm-4.5v | $0.600 | $1.80 | 66K | 2025-08-11 |
| Kilo Gateway kilo | z-ai/glm-4.5v | $0.600 | $1.80 | 66K | 2025-08-11 |
| SiliconFlow siliconflow | zai-org/GLM-4.5V | $0.140 | $0.860 | 66K | 2025-08-13 |
| LLM Gateway llmgateway | glm-4.5v | $0.600 | $1.80 | 64K | 2025-08-11 |
Datenunterschiede zwischen Anbietern
- context_window varies: 64000, 65536, 66000
- release_date varies (span 143d): 2025-08-11, 2025-08-12, 2025-08-13, 2025-11-22, 2026-01
- modalities varies across offerings
Anbieter melden unterschiedliche Werte für dieses Modell. Die Schnellinfos oben nutzen den repräsentativen Anbieter; pro Anbieter siehe Tabelle.
Frequently asked questions
How much does GLM-4.5V cost?
GLM-4.5V costs $0.600 per 1M input tokens and $1.80 per 1M output tokens, sourced from zai. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of GLM-4.5V?
GLM-4.5V has a context window of 64K tokens, with a max output of 16K tokens per reply. This is the total combined size of prompt + completion.
Does GLM-4.5V support tool calling?
Yes. GLM-4.5V supports tool calling (function calling). This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.
Does GLM-4.5V support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for GLM-4.5V in our data source. Verify with Z.AI / Zhipu's official documentation before relying on it in production.
Can GLM-4.5V accept image input?
Yes. GLM-4.5V accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check Z.AI / Zhipu's docs for the exact rule.
Is GLM-4.5V open-weight?
Yes. GLM-4.5V's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to GLM-4.5V?
If GLM-4.5V doesn't fit, consider GLM-5, GLM-4.7, GLM-5.1. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
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Capability lists this model is in
Zuletzt aktualisiert:
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.