Mistral NeMo Instruct (2407) on DeepInfra

Mistral NeMo · MistralAI

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

Why use Mistral NeMo Instruct (2407) on DeepInfra?

DeepInfra offers Mistral NeMo Instruct (2407) with pay-as-you-go pricing at $0.02/1M input tokens. DeepInfra is a cloud inference platform offering cost-effective access to open-source AI models.

Compare Mistral NeMo Instruct (2407) across 7 providers to find the best fit for your use case
Input / 1M
$0.020
Output / 1M
$0.040
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
mistralai/Mistral-Nemo-Instruct-2407

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="mistralai/Mistral-Nemo-Instruct-2407",
    messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)

Gotchas

  • Use provider model ID "mistralai/Mistral-Nemo-Instruct-2407", not the LLMReference slug "mistral-nemo-instruct".
  • 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 NeMo Instruct (2407) Across Providers

ProviderInput (per 1M)Output (per 1M)
NVIDIA NIM——
Microsoft Foundry$0.30$0.30
DeepInfra$0.02$0.04
Fireworks AI$0.90$0.90
Arcee AI$0.15$0.45
View all 7 providers →

Pricing

TypePrice (per 1M)
Input tokens$0.02
Output tokens$0.04

Capabilities

No model capability flags are currently sourced.

About Mistral NeMo Instruct (2407)

Mistral NeMo Instruct (2407) is MistralAI's Mistral NeMo model. It offers a 128K-token context window and scores 57.1 on GPQA.

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