E5 Multi-Lingual Large Embeddings 0.6B
evroc/multilingual-e5-large-instructBy evroc · family: text-embedding · released 2024-06-01
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
Capabilities
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 efficiency78
- Headline price (log-scaled)78/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness50
- 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)15/15
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 | $0.66 < $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 | $1.32 < $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 | $0.30 < $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 | $1.08 < $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 | $1.51 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
Pricing detail
Recommended pricing from evroc · intfloat/multilingual-e5-large-instruct
Available on 1 providers
| Provider | Provider model id | Input / 1M | Output / 1M | Context | Released |
|---|---|---|---|---|---|
| evroc evroc | intfloat/multilingual-e5-large-instruct | $0.120 | $0.120 | 512 | 2024-06-01 |
Frequently asked questions
How much does E5 Multi-Lingual Large Embeddings 0.6B cost?
E5 Multi-Lingual Large Embeddings 0.6B costs $0.120 per 1M input tokens and $0.120 per 1M output tokens, sourced from evroc. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of E5 Multi-Lingual Large Embeddings 0.6B?
E5 Multi-Lingual Large Embeddings 0.6B has a context window of 512 tokens, with a max output of 512 tokens per reply. This is the total combined size of prompt + completion.
Does E5 Multi-Lingual Large Embeddings 0.6B support tool calling?
No. E5 Multi-Lingual Large Embeddings 0.6B does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does E5 Multi-Lingual Large Embeddings 0.6B support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for E5 Multi-Lingual Large Embeddings 0.6B in our data source. Verify with evroc's official documentation before relying on it in production.
Can E5 Multi-Lingual Large Embeddings 0.6B accept image input?
No. E5 Multi-Lingual Large Embeddings 0.6B only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is E5 Multi-Lingual Large Embeddings 0.6B open-weight?
Yes. E5 Multi-Lingual Large Embeddings 0.6B'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 E5 Multi-Lingual Large Embeddings 0.6B?
If E5 Multi-Lingual Large Embeddings 0.6B doesn't fit, consider KB Whisper. 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.
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Capability lists this model is in
Last updated:
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.