AI 모델 인텔리전스

GLM 4.5V Thinking

nano-gpt/glm-4-5v-thinking

제공: nano-gpt · 패밀리: glmv · 출시 2025-11-22

⚠ 이 모델은 커뮤니티 파인튜닝 / 파생본으로, 벤더 공식 릴리스가 아닙니다.

$0.600
입력 / 1M 토큰
$1.80
출력 / 1M 토큰
64K
컨텍스트 창
96K
최대 출력

Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.

기능

✗ 도구 호출✓ 추론✗ 구조화 출력✓ 첨부✗ 오픈 웨이트? 온도 제어
모달리티: 입력 text, image · 출력 text

Model fit scores

0–100 · higher is better

These 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.

Coding11
  • Tool calling0/40
  • Structured output0/20
  • Reasoning10/10
  • Context window (100K → 1M)0/20
  • Provider availability1/10
Agents31
  • Tool calling0/35
  • Structured output0/25
  • Reasoning15/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output15
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control0/10
  • Price-friendly for high-volume15/20
Cost efficiency53
  • Headline price (log-scaled)53/95
  • Has prompt-cache pricing0/5
Long context0
  • Context ≥ 100K0/100
Vision73
  • Accepts image input50/50
  • Context window (10K → 1M)12/30
  • Has published price10/10
  • Provider availability1/10
Production-readiness50
  • Number of independent providers5/40
  • Has published per-token price20/20
  • Context window ≥ 8K15/15
  • No data inconsistencies across providers10/10
  • Official model (not derivative)0/15

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.

ScenarioCostAssumption
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.

가격 상세

추천 가격 제공자: nano-gpt · z-ai/glm-4.5v:thinking

$0.600
입력
$1.80
출력

1곳 제공사에서 이용 가능

제공자제공자 모델 ID입력 / 1M출력 / 1M컨텍스트출시일
NanoGPT
nano-gpt
z-ai/glm-4.5v:thinking$0.600$1.8064K2025-11-22

Frequently asked questions

How much does GLM 4.5V Thinking cost?

GLM 4.5V Thinking costs $0.600 per 1M input tokens and $1.80 per 1M output tokens, sourced from nano-gpt. 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 Thinking?

GLM 4.5V Thinking has a context window of 64K tokens, with a max output of 96K tokens per reply. This is the total combined size of prompt + completion.

Does GLM 4.5V Thinking support tool calling?

No. GLM 4.5V Thinking does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.

Does GLM 4.5V Thinking support structured output / JSON mode?

No. GLM 4.5V Thinking does not support structured output / JSON-schema-constrained decoding. If your workflow requires it, look at the /capabilities/structured-output list for alternatives.

Can GLM 4.5V Thinking accept image input?

Yes. GLM 4.5V Thinking accepts both text and image input. Vision pricing per image is usually billed on top of the regular token rate — check nano-gpt's docs for the exact rule.

Is GLM 4.5V Thinking open-weight?

No. GLM 4.5V Thinking is a proprietary model — only nano-gpt (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.

What are the best alternatives to GLM 4.5V Thinking?

If GLM 4.5V Thinking doesn't fit, consider Brave (Answers), Exa (Research), Auto model (Basic). 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.

Explore more

More nano-gpt models

Capability lists this model is in

See also

마지막 업데이트:

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.