Mixtral 8x7B Instruct v0.1
cortecs/mixtral-8x7b-instruct-v0-1出品方： cortecs · 發布 2023-12-11 · 知識截止: 2023-09
⚠ 本模型為社群微調 / 衍生版本，並非廠商官方發布。
Prices in USD per 1M tokens. Unknown means the provider does not publish per-token pricing.
能力清單
Model fit scores
0–100 · higher is betterThese scores reward declared capabilities, context size, price and provider availability — they are not benchmark results. Use them as a directional signal alongside your own evaluation.
Coding11
- Tool calling0/40
- Structured output0/20
- Reasoning10/10
- Context window (100K → 1M)0/20
- Provider availability1/10
Agents31
- Tool calling0/35
- Structured output0/25
- Reasoning15/15
- Output token limit15/15
- Provider availability1/10
JSON / structured output28
- Structured output / JSON mode0/50
- Tool calling0/20
- Temperature control10/10
- Price-friendly for high-volume18/20
Cost efficiency61
- Headline price (log-scaled)61/95
- Has prompt-cache pricing0/5
Long context0
- Context ≥ 100K0/100
Production-readiness50
- Number of independent providers5/40
- Has published per-token price20/20
- Context window ≥ 8K15/15
- No data inconsistencies across providers10/10
- Official model (not derivative)0/15
Cost Efficiency Index
Open full calculator →Estimated cost using the recommended provider's headline rate. Each scenario fixes average input/output tokens — the assumptions are shown in the third column.
| Scenario | Cost | Assumption |
|---|---|---|
RAG answer per 1,000 RAG answers | $2.53 < $0.01 per request | 5K input tokens (query + 4 retrieved chunks of ~1K each) and a 500-token answer. Typical SaaS knowledge-base bot. |
Support ticket triage per 10,000 tickets | $5.06 < $0.01 per request | 1K input tokens (ticket body + system prompt) and a 100-token JSON classification reply. High-volume customer support. |
Data extraction per 1,000 documents | $1.22 < $0.01 per request | 2K input tokens (a single document page) and a 500-token JSON extraction. ETL / invoice / form pipelines. |
Code review per 1,000 PRs | $4.18 < $0.01 per request | 8K input tokens (diff + surrounding files) and a 1K-token review comment. PR-bot workloads. |
Agent step per 1,000 steps | $5.66 < $0.01 per request | 12K input tokens (long-running tool history) and a 600-token tool-call decision. Cost per agent step. |
定價詳情
推薦定價來自 cortecs · mixtral-8x7B-instruct-v0.1
於 1 家供應商可用
| 服務商 | 服務商模型 ID | 輸入 / 1M | 輸出 / 1M | 上下文 | 發布日期 |
|---|---|---|---|---|---|
| Cortecs cortecs | mixtral-8x7B-instruct-v0.1 | $0.438 | $0.680 | 32K | 2023-12-11 |
Frequently asked questions
How much does Mixtral 8x7B Instruct v0.1 cost?
Mixtral 8x7B Instruct v0.1 costs $0.438 per 1M input tokens and $0.680 per 1M output tokens, sourced from cortecs. Cache reads, audio tokens and >200K-context tiers (where applicable) are listed in the Pricing detail block above.
What is the context window of Mixtral 8x7B Instruct v0.1?
Mixtral 8x7B Instruct v0.1 has a context window of 32K tokens, with a max output of 32K tokens per reply. This is the total combined size of prompt + completion.
Does Mixtral 8x7B Instruct v0.1 support tool calling?
No. Mixtral 8x7B Instruct v0.1 does not support tool calling (function calling). If your workflow requires it, look at the /capabilities/tool-calling list for alternatives.
Does Mixtral 8x7B Instruct v0.1 support structured output / JSON mode?
Support for structured output / JSON-schema-constrained decoding is not reported for Mixtral 8x7B Instruct v0.1 in our data source. Verify with cortecs's official documentation before relying on it in production.
Can Mixtral 8x7B Instruct v0.1 accept image input?
No. Mixtral 8x7B Instruct v0.1 only accepts text as input. If you need image input, see our /capabilities/vision list for current vision-capable models.
Is Mixtral 8x7B Instruct v0.1 open-weight?
Yes. Mixtral 8x7B Instruct v0.1's weights are publicly available, so you can self-host or fine-tune. Note that open weights ≠ open source — the training data and code are typically not released.
What are the best alternatives to Mixtral 8x7B Instruct v0.1?
If Mixtral 8x7B Instruct v0.1 doesn't fit, consider Nova Pro 1.0, Hermes 4 70B, INTELLECT 3. Each one targets the same use case — see the Related links below for direct head-to-head pages.
Where does this data come from?
All numbers come from the public models.dev API and are normalised into a single canonical model record. We re-pull daily and write any changes (price, context, capability) to the /changelog page.
Explore more
More cortecs models
- Nova Pro 1.0$1.02 in / $4.06 out
- Hermes 4 70B$0.12 in / $0.36 out
- INTELLECT 3$0.22 in / $1.20 out
- GPT Oss 120bUnknown pricing
- GPT 4.1$2.35 in / $9.42 out
Capability lists this model is in
最近更新：
Data is sourced from models.dev and normalized for comparison. Prices and capabilities may change. Always verify critical production decisions with the provider's official documentation.