Text Embedding 005
vercel/text-embedding-005제공: vercel · 패밀리: text-embedding · 출시 2024-08
⚠ 이 모델은 커뮤니티 파인튜닝 / 파생본으로, 벤더 공식 릴리스가 아닙니다.
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
기능
Model fit scores
0–100 · higher is betterThese 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.
Coding1
- Tool calling0/40
- Structured output0/20
- Reasoning0/10
- Context window (100K → 1M)0/20
- Provider availability1/10
Agents1
- Tool calling0/35
- Structured output0/25
- Reasoning0/15
- Output token limit0/15
- Provider availability1/10
JSON / structured output20
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control0/10
- Price-friendly for high-volume20/20
Cost efficiency95
- Headline price (log-scaled)95/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness43
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K8/15
- 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.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $0.15 < $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 | $0.30 < $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 | $0.06 < $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 | $0.24 < $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 | $0.36 < $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: vercel · google/text-embedding-005
1곳 제공사에서 이용 가능
| 제공자 | 제공자 모델 ID | 입력 / 1M | 출력 / 1M | 컨텍스트 | 출시일 |
|---|---|---|---|---|---|
| Vercel AI Gateway vercel | google/text-embedding-005 | $0.030 | Unknown | 8K | 2024-08 |
Frequently asked questions
How much does Text Embedding 005 cost?
Text Embedding 005 costs $0.030 per 1M input tokens and Unknown per 1M output tokens, sourced from vercel. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Text Embedding 005?
Text Embedding 005 has a context window of 8K tokens, with a max output of 2K tokens per reply. This is the total combined size of prompt + completion.
Does Text Embedding 005 support tool calling?
No. Text Embedding 005 does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Text Embedding 005 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Text Embedding 005 in our data source. Verify with vercel's official documentation before relying on it in production.
Can Text Embedding 005 accept image input?
No. Text Embedding 005 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Text Embedding 005 open-weight?
No. Text Embedding 005 is a proprietary model — only vercel (and any approved hosting partners) can serve it. The pricing above reflects the cheapest API access.
What are the best alternatives to Text Embedding 005?
If Text Embedding 005 doesn't fit, consider Trinity Mini, Trinity Large Thinking, Trinity Large Preview. 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 vercel models
- Trinity Mini$0.05 in / $0.15 out
- Trinity Large Thinking$0.25 in / $0.90 out
- Trinity Large Preview$0.25 in / $1.00 out
- INTELLECT 3$0.20 in / $1.10 out
- Titan Text Embeddings V2$0.02 in / $0.00 out
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.