Last refreshed 2026-05-05. Next refresh: weekly.
Why use Mistral 7B v0.1 on Replicate API?
Replicate API offers Mistral 7B v0.1 with pay-as-you-go pricing at $0.05/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare Mistral 7B v0.1 across 16 providers to find the best fit for your use caseSetup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"mistralai/mistral-7b-v0.1",
input={"prompt": "Hello"}mistralai/mistral-7b-v0.1Request example
import replicate
# reads REPLICATE_API_TOKEN from env
# mistralai/mistral-7b-v0.1 format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"mistralai/mistral-7b-v0.1",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "mistralai/mistral-7b-v0.1", not the LLMReference slug "mistral-7b-v0.1".
- Replicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
- The examples expect REPLICATE_API_TOKEN; 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 | $0.05 |
| Output tokens | $0.25 |
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.