Embed v3 English
azure/embed-v3-englishVon azure · Familie: cohere-embed · veröffentlicht 2023-11-07
⚠ Dies ist ein Community-Finetune oder Derivat — keine offizielle Anbieter-Veröffentlichung.
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Fähigkeiten
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 efficiency86
- Headline price (log-scaled)86/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness35
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K0/15
- 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 | $0.50 < $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 | $0.20 < $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 | $0.80 < $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 | $1.20 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Preis-Details
Empfohlene Preise von azure · cohere-embed-v3-english
Bei 1 Anbietern verfügbar
| Anbieter | Anbieter-Modell-ID | Eingabe / 1M | Ausgabe / 1M | Kontext | Veröffentlicht |
|---|---|---|---|---|---|
| Azure azure | cohere-embed-v3-english | $0.100 | Unknown | 512 | 2023-11-07 |
Frequently asked questions
How much does Embed v3 English cost?
Embed v3 English costs $0.100 per 1M input tokens and Unknown per 1M output tokens, sourced from azure. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Embed v3 English?
Embed v3 English has a context window of 512 tokens, with a max output of 1K tokens per reply. This is the total combined size of prompt + completion.
Does Embed v3 English support tool calling?
No. Embed v3 English does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Embed v3 English support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Embed v3 English in our data source. Verify with azure's official documentation before relying on it in production.
Can Embed v3 English accept image input?
No. Embed v3 English only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Embed v3 English open-weight?
Yes. Embed v3 English'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 Embed v3 English?
If Embed v3 English doesn't fit, consider Codex Mini, Ministral 3B, text-embedding-3-large. 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 azure models
- Codex Mini$1.50 in / $6.00 out
- Ministral 3B$0.04 in / $0.04 out
- text-embedding-3-large$0.13 in / $0.00 out
- text-embedding-ada-002$0.10 in / $0.00 out
- o1$15.00 in / $60.00 out
Capability lists this model is in
Zuletzt aktualisiert:
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.