Mistral 7B v0.1 on DeepInfra

Mistral 7B · MistralAI

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Last refreshed 2026-06-15. Next refresh: weekly.

Why use Mistral 7B v0.1 on DeepInfra?

DeepInfra offers Mistral 7B v0.1 with pay-as-you-go pricing at $0.05/1M input tokens. DeepInfra is a cloud inference platform offering cost-effective access to open-source AI models.

Compare Mistral 7B v0.1 across 16 providers to find the best fit for your use case
Input / 1M
$0.050
Output / 1M
$0.15
Cache
Not sourced
Batch
Not sourced

Setup recipe

Python + curl
Install
pip install openai
Auth
export DEEPINFRA_API_KEY=...
Call
import os
from openai import OpenAI
client = OpenAI(
    api_key=os.environ["DEEPINFRA_API_KEY"],
Model ID
mistral-7b-v0.1

Request example

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["DEEPINFRA_API_KEY"],
    base_url="https://api.deepinfra.com/v1/openai"
)
response = client.chat.completions.create(
    model="mistral-7b-v0.1",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Gotchas

  • DeepInfra uses "organization/model-name" format, e.g. "meta-llama/Meta-Llama-3-8B-Instruct" or "mistralai/Mistral-7B-Instruct-v0.3". See the DeepInfra model catalog for exact IDs.
  • The examples expect DEEPINFRA_API_KEY; rename it only if your application config maps the new variable.

Compare Mistral 7B v0.1 Across Providers

ProviderInput (per 1M)Output (per 1M)
GCP Vertex AI$0.08$0.24
OctoAI API (Deprecated)——
DeepInfra$0.05$0.15
Mistral AI Studio$0.25$0.25
Baseten API——
View all 16 providers →

Pricing

TypePrice (per 1M)
Input tokens$0.05
Output tokens$0.15

Capabilities

No model capability flags are currently sourced.

About Mistral 7B v0.1

Mistral 7B v0.1 is an advanced open-source large language model built by Mistral AI, consisting of 7 billion parameters. It's designed to deliver high performance and efficiency, outperforming many similar-sized models in various benchmarks. The model employs a transformer architecture with innovative features like Sliding Window Attention, Grouped-Query Attention, and a Byte-fallback BPE tokenizer, enhancing speed, reducing computational costs, and improving robustness. Capable of generating human-like text, following instructions effectively, and excelling in areas such as reasoning and mathematics, Mistral 7B v0.1 does have limitations, such as a lack of built-in moderation and a potential for hallucinations.

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