Last refreshed 2026-06-15. Next refresh: weekly.
Why use Mistral 7B v0.1 on GCP Vertex AI?
GCP Vertex AI offers Mistral 7B v0.1 with pay-as-you-go pricing at $0.08/1M input tokens. Vertex AI is Google Cloud's managed AI platform, offering access to Gemini models and hundreds of partner models alongside tools for fine-tuning, grounding, vector search, and end-to-end MLOps pipelines.
Compare Mistral 7B v0.1 across 16 providers to find the best fit for your use caseSetup recipe
Python + curlpip install google-cloud-aiplatformexport GOOGLE_CLOUD_PROJECT=...import os
import vertexai
from vertexai.generative_models import GenerativeModel
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")mistral-7b-v0.1Request example
import os
import vertexai
from vertexai.generative_models import GenerativeModel
# Reads GOOGLE_CLOUD_PROJECT from env; authenticates via Application Default Credentials
vertexai.init(project=os.environ["GOOGLE_CLOUD_PROJECT"], location="us-central1")
model = GenerativeModel("mistral-7b-v0.1")
response = model.generate_content("Hello")
print(response.text)Gotchas
- For Google-published models use the model name directly, e.g. "gemini-2.0-flash-001". For third-party publishers (Anthropic, Meta, etc.) use the full publisher path, e.g. "publishers/anthropic/models/claude-3-5-sonnet-v2@20241022".
- The examples expect GOOGLE_CLOUD_PROJECT; rename it only if your application config maps the new variable.
Compare Mistral 7B v0.1 Across Providers
| Provider | Input (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 | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.08 |
| Output tokens | $0.24 |
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.