Last refreshed 2026-06-15. Next refresh: weekly.
Why use Mixtral 8x7B Instruct v0.1 on Together AI?
Together AI offers Mixtral 8x7B Instruct v0.1 with pay-as-you-go pricing at $0.40/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare Mixtral 8x7B Instruct v0.1 across 5 providers to find the best fit for your use caseInput / 1M
$0.40
Output / 1M
$0.40
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install togetherAuth
export TOGETHER_API_KEY=...Call
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",Model ID
mistralai/Mixtral-8x7B-Instruct-v0.1Request example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="mistralai/Mixtral-8x7B-Instruct-v0.1",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "mistralai/Mixtral-8x7B-Instruct-v0.1", not the LLMReference slug "mixtral-8x7b-instruct-v0.1".
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_API_KEY; rename it only if your application config maps the new variable.
Compare Mixtral 8x7B Instruct v0.1 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Together AI | $0.40 | $0.40 |
| OctoML (Deprecated) | $0.40 | $0.60 |
| AWS Bedrock | $0.45 | $0.45 |
| IBM watsonx | $0.18 | $0.18 |
| DeepInfra | $0.15 | $0.45 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.40 |
| Output tokens | $0.40 |
Capabilities
No model capability flags are currently sourced.
About Mixtral 8x7B Instruct v0.1
Mixtral 8x7B Instruct v0.1 is MistralAI's Mixtral model. It offers a 32K-token context window with weights openly available for self-hosting.
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Model Specs
Released2023-12-10
Parameters56B
Context33k
ArchitectureDecoder Only
Knowledge cutoff2023-12