AIモデルインテリジェンス

GLM-4

nano-gpt/glm-4

提供: nano-gpt · リリース 2024-01-16

⚠ これはコミュニティのファインチューン / 派生モデルで、ベンダーの公式リリースではありません。

$14.99
入力 / 100万トークン
$14.99
出力 / 100万トークン
128K
コンテキスト長
4K
最大出力

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

機能一覧

ツール呼び出し推論構造化出力添付オープンウェイト? 温度制御
モダリティ: 入力 text · 出力 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.

Coding3
  • Tool calling0/40
  • Structured output0/20
  • Reasoning0/10
  • Context window (100K → 1M)2/20
  • Provider availability1/10
Agents1
  • Tool calling0/35
  • Structured output0/25
  • Reasoning0/15
  • Output token limit0/15
  • Provider availability1/10
JSON / structured output0
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control0/10
  • Price-friendly for high-volume0/20
Cost efficiency26
  • Headline price (log-scaled)26/95
  • Has prompt-cache pricing0/5
Long context45
  • Context window (100K → 2M)35/90
  • Has published price for full window10/10
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
$82.47
$0.08 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
$165
$0.02 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
$37.48
$0.04 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
$135
$0.13 per request
8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads.
Agent step
per 1,000 steps
$189
$0.19 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

料金詳細

推奨料金 (提供元): nano-gpt · glm-4

$14.99
入力
$14.99
出力

1 か所で利用可能

プロバイダープロバイダーモデルID入力 / 1M出力 / 1Mコンテキストリリース日
NanoGPT
nano-gpt
glm-4$14.99$14.99128K2024-01-16

Frequently asked questions

How much does GLM-4 cost?

GLM-4 costs $14.99 per 1M input tokens and $14.99 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?

GLM-4 has a context window of 128K tokens, with a max output of 4K tokens per reply. This is the total combined size of prompt + completion.

Does GLM-4 support tool calling?

No. GLM-4 does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.

Does GLM-4 support structured output / JSON mode?

No. GLM-4 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 accept image input?

No. GLM-4 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is GLM-4 open-weight?

No. GLM-4 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?

If GLM-4 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.

More nano-gpt models

最終更新:

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.