Mistral AI - API - verified
Codestral Embed embeddings
Codestral Embed is a current embedding model from Mistral AI, called by API as codestral-embed. It returns 1,536-dimension vectors by default and accepts up to 8,000 input tokens, for text and code input. The first-party list price is $0.15 per 1M input tokens.
CurrentModel id: codestral-embed
- Default dims
- 1,536
- Max input
- 8,000 tok
- Price / 1M
- $0.15
- Inputs
- Text, Code
Dimensions
Flexible output size- 3,072
- 1,536default
Default output: 1,536 dimensions.
Listed sizes: 3,072 and 1,536.
Mistral AI documents shorter output vectors for this model (Matryoshka-style), so you can store smaller embeddings by asking for fewer dimensions instead of re-embedding with another model.
Pricing
- Per 1M input tokens
- $0.15
- Batch, per 1M
- Not stated
Input $0.15 per 1M tokens, cached input $0.015; versioned id codestral-embed-2505.
Usage
Copied verbatim from the page linked below. Placeholders such as project ids and API keys are the source's own.
import os
from mistralai.client import Mistral
api_key = os.environ["MISTRAL_API_KEY"]
model = "codestral-embed"
client = Mistral(api_key=api_key)
embeddings_batch_response = client.embeddings.create(
model=model,
# output_dtype="binary",
# output_dimension=512,
inputs=[
"Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target. You may assume that each input would have exactly one solution, and you may not use the same element twice. You can return the answer in any order. Example 1: Input: nums = [2,7,11,15], target = 9 Output: [0,1] Explanation: Because nums[0] + nums[1] == 9, we return [0, 1]. Example 2: Input: nums = [3,2,4], target = 6 Output: [1,2] Example 3: Input: nums = [3,3], target = 6 Output: [0,1] Constraints: 2 <= nums.length <= 104 -109 <= nums[i] <= 109 -109 <= target <= 109 Only one valid answer exists.",
"class Solution: def twoSum(self, nums: List[int], target: int) -> List[int]: d = {} for i, x in enumerate(nums): if (y := target - x) in d: return [d[y], i] d[x] = i"
],
)Source: docs.mistral.ai/studio/knowledge-rag/embeddings/code_embeddings
Worth knowing
output_dimension defaults to 1536 with a maximum of 3072 (any leading n dimensions can be kept), and output_dtype supports float, int8, uint8, binary and ubinary for code retrieval and code search indexes.
Released . Multilingual: not stated.
FAQ
How many dimensions does Codestral Embed output?
Default output: 1,536 dimensions. Listed sizes: 3,072 and 1,536. Mistral AI documents shorter output vectors for this model (Matryoshka-style), so you can store smaller embeddings by asking for fewer dimensions instead of re-embedding with another model.
What is the max input length of Codestral Embed?
8,000 tokens per input, per Mistral AI's documentation. Longer text has to be chunked before you embed it.
How much does Codestral Embed cost?
The first-party list price is $0.15 per 1M input tokens. Input $0.15 per 1M tokens, cached input $0.015; versioned id codestral-embed-2505. Source: https://docs.mistral.ai/inference/pricing.
What can Codestral Embed embed?
Text and code input, per the provider.
Sources
Last verified from the pages below.
- Codestral Embed model page - docs.mistral.ai
- Mistral code embeddings docs - docs.mistral.ai
- Mistral API pricing - docs.mistral.ai
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Default dimensions, max input tokens and list price per 1M tokens.
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