Интерфейс моделей ИИ

GLM 4.7 TEE

chutes/glm-4-7-tee

От chutes · семейство: glm · выпуск 2025-12-29

⚠ Это сообществом дообученная / производная модель — не официальный релиз вендора.

$0.390
Вход / 1M токенов
$1.75
Выход / 1M токенов
203K
Окно контекста
66K
Макс. вывод

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

Возможности

✓ Tool calling✓ Рассуждение✓ Структурированный вывод✗ Вложения✓ Открытые веса✓ Управление температурой
Модальности: вход text · выход 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.

Coding77
  • Tool calling40/40
  • Structured output20/20
  • Reasoning10/10
  • Context window (100K → 1M)6/20
  • Provider availability1/10
Agents91
  • Tool calling35/35
  • Structured output25/25
  • Reasoning15/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output96
  • Structured output / JSON mode50/50
  • Tool calling20/20
  • Temperature control10/10
  • Price-friendly for high-volume16/20
Cost efficiency59
  • Headline price (log-scaled)54/95
  • Has prompt-cache pricing5/5
Long context55
  • Context window (100K → 2M)45/90
  • 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
$2.82
< $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
$5.65
< $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
$1.66
< $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
$4.87
< $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
$5.73
< $0.01 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

Детализация цен

Рекомендованная цена от chutes · zai-org/GLM-4.7-TEE

$0.390
Вход
$1.75
Выход
$0.195
Чтение из кеша

Доступна у 1 провайдеров

ПровайдерID модели провайдераВход / 1MВыход / 1MКонтекстВыпуск
Chutes
chutes
zai-org/GLM-4.7-TEE$0.390$1.75203K2025-12-29

Frequently asked questions

How much does GLM 4.7 TEE cost?

GLM 4.7 TEE costs $0.390 per 1M input tokens and $1.75 per 1M output tokens, sourced from chutes. 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 4.7 TEE?

GLM 4.7 TEE has a context window of 203K tokens, with a max output of 66K tokens per reply. This is the total combined size of prompt + completion.

Does GLM 4.7 TEE support tool calling?

Yes. GLM 4.7 TEE 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 4.7 TEE support structured output / JSON mode?

Yes. GLM 4.7 TEE supports structured output / JSON-schema-constrained decoding. This makes it suitable for production agent and automation workloads where the model has to invoke external functions reliably.

Can GLM 4.7 TEE accept image input?

No. GLM 4.7 TEE only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is GLM 4.7 TEE open-weight?

Yes. GLM 4.7 TEE'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 4.7 TEE?

If GLM 4.7 TEE doesn't fit, consider Hermes 4 14B, MiMo V2 Flash TEE, dots.ocr. 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 chutes models

Capability lists this model is in

See also

Последнее обновление:

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.