AI 模型情报

Grok 4

nano-gpt/grok-4-07-09

出品方: nano-gpt · 系列: grok · 发布 2025-07-09

⚠ 本模型为社区微调 / 衍生版本,非厂商官方发布。

$3.00
输入 / 1M token
$15.00
输出 / 1M token
256K
上下文长度
131K
最大输出

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

能力清单

工具调用推理结构化输出附件开放权重? 温度可调
支持模态: 输入 text, image · 输出 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.

Coding19
  • Tool calling0/40
  • Structured output0/20
  • Reasoning10/10
  • Context window (100K → 1M)8/20
  • Provider availability1/10
Agents31
  • Tool calling0/35
  • Structured output0/25
  • Reasoning15/15
  • Output token limit15/15
  • Provider availability1/10
JSON / structured output0
  • Structured output / JSON mode0/50
  • Tool calling0/20
  • Temperature control0/10
  • Price-friendly for high-volume0/20
Cost efficiency31
  • Headline price (log-scaled)31/95
  • Has prompt-cache pricing0/5
Long context60
  • Context window (100K → 2M)50/90
  • Has published price for full window10/10
Vision82
  • Accepts image input50/50
  • Context window (10K → 1M)21/30
  • 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
$22.50
$0.02 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
$45.00
< $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
$13.50
$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
$39.00
$0.04 per request
8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads.
Agent step
per 1,000 steps
$45.00
$0.04 per request
12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step.

定价详情

推荐定价来自 nano-gpt · x-ai/grok-4-07-09

$3.00
输入
$15.00
输出

在 1 家渠道可用

服务商服务商模型 ID输入 / 1M输出 / 1M上下文发布日期
NanoGPT
nano-gpt
x-ai/grok-4-07-09$3.00$15.00256K2025-07-09

Frequently asked questions

How much does Grok 4 cost?

Grok 4 costs $3.00 per 1M input tokens and $15.00 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 Grok 4?

Grok 4 has a context window of 256K tokens, with a max output of 131K tokens per reply. This is the total combined size of prompt + completion.

Does Grok 4 support tool calling?

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

Does Grok 4 support structured output / JSON mode?

No. Grok 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 Grok 4 accept image input?

Yes. Grok 4 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 Grok 4 open-weight?

No. Grok 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 Grok 4?

If Grok 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

Capability lists this model is in

最近更新:

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.