AI 模型情报

gpt-oss-20b

amazon-bedrock/gpt-oss-20b-1-0

出品方: amazon-bedrock · 系列: gpt-oss · 发布 2024-12-01

⚠ 本模型为社区微调 / 衍生版本,非厂商官方发布。

$0.070
输入 / 1M token
$0.300
输出 / 1M token
128K
上下文长度
4K
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 · 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.

Coding63
  • Tool calling40/40
  • Structured output20/20
  • Reasoning0/10
  • Context window (100K → 1M)2/20
  • Provider availability1/10
Agents61
  • Tool calling35/35
  • Structured output25/25
  • Reasoning0/15
  • Output token limit0/15
  • Provider availability1/10
JSON / structured output99
  • Structured output / JSON mode50/50
  • Tool calling20/20
  • Temperature control10/10
  • Price-friendly for high-volume19/20
Cost efficiency73
  • Headline price (log-scaled)73/95
  • 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
$0.50
< $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
$1.00
< $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
$0.29
< $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
$0.86
< $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
$1.02
< $0.01 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 amazon-bedrock · openai.gpt-oss-20b-1:0

$0.070
输入
$0.300
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
Amazon Bedrock
amazon-bedrock
openai.gpt-oss-20b-1:0$0.070$0.300128K2024-12-01

Frequently asked questions

How much does gpt-oss-20b cost?

gpt-oss-20b costs $0.070 per 1M input tokens and $0.300 per 1M output tokens, sourced from amazon-bedrock. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.

What is the context window of gpt-oss-20b?

gpt-oss-20b 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 gpt-oss-20b support tool calling?

Yes. gpt-oss-20b 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 gpt-oss-20b support structured output / JSON mode?

Yes. gpt-oss-20b 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 gpt-oss-20b accept image input?

No. gpt-oss-20b only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.

Is gpt-oss-20b open-weight?

No. gpt-oss-20b is a proprietary model — only amazon-bedrock (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.

What are the best alternatives to gpt-oss-20b?

If gpt-oss-20b doesn't fit, consider Palmyra X5, Nova Pro, Nova Micro. 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 amazon-bedrock models

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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.