Open-weights Mixture-of-Experts powerhouse with 141B total parameters and 39B active, delivering exceptional math, coding, and multilingual performance.
Technical Specifications
- Provider
- Mistral AI
- Category
- open-source
- Release Date
- 2024-04
- Batch API Discount
- 50% off async batch
- Consumer Subscription
- Free / Self-Hosted
- Reasoning Effort Control
- Fixed
- Structured Outputs
- Yes
- Output Modalities
- text
- Official Documentation
- Visit Official Docs ↗
Data Privacy, Licensing & Deployment Governance
Official Provider Release Intelligence, API Quotas & Agentic Specs
- Official API Model ID (SDK String)
- mixtral-8x22b-latest
- Supported SDK & API Protocol
- OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
- Lifecycle & Deprecation Status
- Legacy Stable (Upgrade path available in Mistral AI lineup)
- Production Rate Limits (RPM & TPM)
- Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud
- Long-Context Pricing Tier (>128K / >200K)
- Flat rate ($1.2/1M in) across full 66K window
- Prompt Cache Write & TTL Policy
- Cached input at $0.6/1M tokens
- Computer Use & Browser GUI Agent
- API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
- MCP & Parallel Tool Calling
- Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
- Web Search Grounding & Code Sandbox
- Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools
- Fine-Tuning & Distillation Support
- Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)
- Multimodal Tokenization Billing
- Text BPE (~4 chars/token)
- Weights Precision & Quantization
- BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0)
- Minimum GPU VRAM Requirement
- 87 GB VRAM (4-bit Quantized) · 303 GB VRAM (Native BF16)
- Supported Inference Engines
- vLLM, SGLang, Ollama, llama.cpp, TensorRT-LLM, HuggingFace TGI
- System Card & Safety Framework
- Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)
- Enterprise Compliance (SOC2 / HIPAA / ZDR)
- 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Mixtral 8x22B Cost, Context, Hardware & API Integration
For single-user chat, a $20/mo subscription caps your bill below this API volume.
Break-even point: ~139 requests/day equals $20/month.
Same Monthly Workload Cost Across Popular Reference Models
Live Prompt Paste Tokenizer & Cost Counter
Paste real system instructions, JSON schemas, or code to measure BPE token counts across tokenizers.
Hybrid Prompt Routing Cascade Simulator
Route routine queries to a fast lightweight model ($0.15/1M) and escalate only complex reasoning turns to Mixtral 8x22B:
Benchmark Scores & 6-Axis Capability Radar
- SWE-bench Verified
- 36%
- GPQA Diamond
- 51.2%
- MMLU-Pro
- 74%
- HumanEval
- 82.5%
- Terminal-Bench
- 31%
- AIME
- 62%
- Long-Context Reasoning
- 68%
- Chatbot Arena Elo
- 1260pts
Strengths
- High parameter capacity (141B) with efficient 39B active token inference
- Fully open weights under Apache 2.0 license for private enterprise hosting
- Native function calling and high code completion fidelity
Limitations & Trade-offs
- Requires substantial multi-GPU VRAM for local inference clusters
- 64K context window smaller than 128K+ alternatives
Editorial Assessment
The defining large-scale open MoE foundation model of early 2024.
Best for: Self-hosted enterprise private clusters, open-source AI infrastructure, and multilingual translation
Compare Mixtral 8x22B
Frequently Asked Questions about Mixtral 8x22B
How much does the Mixtral 8x22B API cost per 1M tokens?+
Mixtral 8x22B by Mistral AI is priced at $1.2 per 1M input tokens and $1.2 per 1M output tokens. With prompt caching enabled, cached input tokens cost $0.6 per 1M tokens, and asynchronous Batch API requests receive a 50% discount.
Should I use a $20/month subscription or the Mixtral 8x22B pay-as-you-go API?+
A flat $20/month consumer subscription is predictable for daily interactive chat and manual coding. However, if you are building software, running automated workflows, or sending low-to-moderate volume with prompt caching enabled, the pay-as-you-go API is typically far more cost-effective and offers higher rate limits without manual UI caps.
How many words and pages fit in Mixtral 8x22B's context window?+
Mixtral 8x22B features a 66K tokens context window, which equals approximately 49,152 English words or ~131 standard single-spaced pages in a single prompt. Its maximum output generation per turn is 8,192 tokens.
Can I run Mixtral 8x22B locally on my own GPU (Open Weights)?+
Yes, Mixtral 8x22B (141B total (39B active MoE)) releases open weights that can be self-hosted locally or on private cloud GPUs using vLLM, Ollama, SGLang, or llama.cpp.
Does Mistral AI train on data sent to the Mixtral 8x22B API, and is commercial use allowed?+
100% Isolated when self-hosted on private GPUs or VPC. License tier: Permissive Open-Weights (Apache 2.0 / MIT Commercial). Full commercial ownership of generated outputs. Supported deployment channels include: Self-Hosted GPU (vLLM / Ollama), Private VPC, or Managed Serverless API.
How does Mixtral 8x22B perform on coding and reasoning benchmarks?+
Mixtral 8x22B achieves a composite Intelligence Index of 44/100, scoring 36% on SWE-bench Verified, 51.2% on GPQA Diamond, 74% on MMLU-Pro, and 1260 pts on Chatbot Arena Elo.
What is Mixtral 8x22B best used for?+
Mixtral 8x22B is best suited for: Self-hosted enterprise private clusters, open-source AI infrastructure, and multilingual translation. Assessment: The defining large-scale open MoE foundation model of early 2024.
Mistral AI Family Lineage, Routing Tiers & Upgrade Path
Step down to a lighter sibling for high-volume routing or step up to Mistral AI's flagship reasoning tier:
Mistral Medium 3.5
Mistral Small 4
Devstral 2
Devstral Medium
Devstral Small
Mistral Small 3.2
AI Glossary & Architecture Concepts
View full glossary →Key machine learning concepts and benchmarks relevant to Mixtral 8x22B:
The basic unit of text that a language model processes, typically representing about 3 to 4 English characters or roughly 0.75 words. For example, the sentence "Artificial intelligence is fast" breaks down into about 5 tokens.
A standardized evaluation suite used to objectively measure and rank an AI model's capabilities across specific tasks — such as SWE-bench Verified for software engineering, AIME for competitive math, or MMLU for general knowledge.
The maximum number of tokens a model can consider at once — both the input it reads and the output it writes. A 1,000,000-token context window can hold roughly 750,000 English words in a single request.
The ability of a language model to recognize when an external capability is needed and output structured JSON arguments to invoke external APIs, databases, or terminal commands. The runtime executes the tool and passes results back to the model.
An open standard created by Anthropic that allows AI applications and agents to connect seamlessly to local filesystems, development environments, and third-party APIs via standardized server interfaces.
A cost and latency optimization where inference providers store pre-computed attention states for repeated prompt prefixes. Reusing cached tokens typically slashes API input costs by 50% to 90% and cuts TTFT substantially.
Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
Sources: Mistral AI Open Source Technical Release (April 2024)↗