Last refreshed 2026-04-19. Next refresh: weekly.
Why use Mixtral 8x7B on Replicate API?
Replicate API offers Mixtral 8x7B with pay-as-you-go pricing at $0.20/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare Mixtral 8x7B across 18 providers to find the best fit for your use caseSetup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"mixtral-8x7b",
input={"prompt": "Hello"}mixtral-8x7bRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# mixtral-8x7b format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"mixtral-8x7b",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- 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 Mixtral 8x7B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Databricks Foundation Model Serving | $0.50 | $1.00 |
| NVIDIA NIM | — | — |
| GCP Vertex AI | $0.40 | $1.20 |
| AWS Bedrock | $0.45 | $0.70 |
| OctoAI API (Deprecated) | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.20 |
| Output tokens | $1.00 |
Capabilities
No model capability flags are currently sourced.
About Mixtral 8x7B
Mixtral 8x7B, developed by Mistral AI, features a cutting-edge Mixture of Experts (MoE) architecture, utilizing eight experts with seven billion parameters each, yielding a total of 46.7 billion parameters. This architecture activates only two experts per token, allowing for efficient processing and a 6x faster inference rate compared to Llama 2 70B. The model excels in performance, surpassing Llama 2 70B and competing with GPT-3.5 on numerous benchmarks. It supports multiple languages and can handle context up to 32,000 tokens, enhancing understanding of lengthy text.