Mixtral 8x22B vs Mixtral 8x7B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mixtral 8x22B by Mistral AI and Mixtral 8x7B by Mistral AI represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Mixtral 8x7B holds a notable pricing advantage for token ingestion. On output generation, Mixtral 8x7B is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Mixtral 8x22B provides an expanded memory horizon with 66K tokens. In latency-sensitive production environments, Mixtral 8x7B leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
원하는 AI 모델 선택 및 실시간 비교
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Mixtral 8x22B
오픈 웨이트 모델Mistral AI의 Mixtral 8x22B 모델은 65K 토큰 컨텍스트 윈도우와 입력 $1.2/1M 토큰, 출력 $1.2/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$1.20 / 1M
$1.20 / 1M
65.5K tokens
36%
Mixtral 8x7B
오픈 웨이트 모델Mistral AI의 Mixtral 8x7B 모델은 32K 토큰 컨텍스트 윈도우와 입력 $0.2/1M 토큰, 출력 $0.2/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.20 / 1M
$0.20 / 1M
32.8K tokens
N/A
한눈에 보는 핵심 차이점
- Mixtral 8x7B is 6.8x cheaper for API usage (input + output tokens combined).
- Mixtral 8x22B has a larger context window (66K vs 33K tokens).
- Mixtral 8x7B is faster at 130 tok/s vs 55 tok/s.
| Feature | Mixtral 8x22B | Mixtral 8x7B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 37 |
Provider | Mistral AI | Mistral AI |
Input Price Cost per million input tokens | $1.20 / 1M | $0.20 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $0.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.78 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 65.5K tokens~98 A4 pages of text | 32.8K tokens~49 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 55 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 36% | N/A |
GPQA Diamond Graduate-level reasoning | 51.2% | 38% |
Cached Input Price Prompt caching discount per 1M tokens | $0.60 / 1M | $0.05 / 1M |
부문별 우위 모델
At-a-glance decision frameworkEvaluations ongoing
검증된 벤치마크 점수 비교
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
지연 시간(Latency) 및 응답 속도
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
실시간 API 토큰 비용 시뮬레이터
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mixtral 8x22B · Strengths & Key Advantages
- Mistral AI의 첨단 Sparse Mixture-of-Experts (8x22B) 아키텍처와 최대 65K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $1.2/1M, 출력 $1.2/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Mixtral 8x7B · Strengths & Key Advantages
- Mistral AI의 첨단 Sparse Mixture of Experts (MoE, 2 active of 8) 아키텍처와 최대 32K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.2/1M, 출력 $0.2/1M)과 빠른 응답 속도
- 코드 생성, 복잡한 논리 추론 및 다국어 벤치마크에서 검증된 성능 발휘
Considerations & Trade-offs
- 대규모 장문 컨텍스트 처리 시 토큰 비용 및 호출 한도 관리가 필요함
- 특수 도메인 작업에서는 정교한 프롬프트 엔지니어링과 RAG 연동이 권장됨
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mixtral 8x22B | Mixtral 8x7B |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.45 | $0.20 |
Output Tokens / Task Tokens generated per task | 35K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Mixtral 8x22B | Mixtral 8x7B |
|---|---|---|
| Official API Model ID (SDK) | mixtral-8x22b-latest | mixtral-8x7b-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-04 | 2023-12 |
| Knowledge Cutoff | March 2024 | Late 2023 |
| Architecture | Sparse Mixture-of-Experts (8x22B) | Sparse Mixture of Experts (MoE, 2 active of 8) |
| Parameters | 141B total (39B active MoE) | 46.7B (12.9B active) |
| Enterprise Rate Limits (RPM / TPM) | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($1.2/1M in) across full 66K window | Flat rate ($0.2/1M in) across full 33K window |
| Prompt Cache Policy & TTL | Cached input at $0.6/1M tokens | Cached input at $0.05/1M tokens |
| Computer Use / GUI Agent | API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop) | 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 | Native MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema |
| Web Search Grounding & Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) | Text BPE (~4 chars/token) |
| Quantization & Min GPU VRAM | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 87 GB VRAM (4-bit Quantized) · 303 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 29 GB VRAM (4-bit Quantized) · 100 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text | text |
| Output Modalities | text | text |
Mixtral 8x22B vs Mixtral 8x7B: 핵심 요약 문답 (FAQ)
Which is more intelligent, Mixtral 8x22B or Mixtral 8x7B?
Mixtral 8x22B is more intelligent. It scores 44 compared with 37 on the composite Intelligence Index.
Which model is faster?
Mixtral 8x7B is faster, generating 130 tokens per second compared with 55 tokens per second.
Which model is cheaper to run?
Mixtral 8x7B is cheaper at $0.10 per 1M tokens blended, compared with $0.78 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Mixtral 8x7B has lower latency, with a time to first token of 0.38s compared with 0.70s.
Which model has a larger context window?
Mixtral 8x22B has a larger context window, supporting 65.5K tokens compared with 32.8K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose Mixtral 8x22B if your priority is Self-hosted enterprise private clusters, open-source AI infrastructure, and multilingual translation. Its performance in demanding tasks justifies its operational token cost. Choose Mixtral 8x7B if your workflow prioritizes Cost-effective multilingual enterprise pipelines, customer service bots, and open MoE research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
자주 묻는 질문 (FAQ)
How does Mixtral 8x22B compare to Mixtral 8x7B in API token pricing and cost per token?+
Mixtral 8x22B costs $1.20 per 1M input tokens and $1.20 per 1M output tokens, whereas Mixtral 8x7B costs $0.20 input and $0.20 output. Overall, Mixtral 8x7B is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Mixtral 8x22B or Mixtral 8x7B?+
Mixtral 8x22B supports a context window of 66K tokens (~49k words), while Mixtral 8x7B supports 33K tokens (~25k words). Therefore, Mixtral 8x22B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mixtral 8x22B or Mixtral 8x7B?+
Mixtral 8x22B scores 36% on SWE-bench Verified, while Mixtral 8x7B scores competitively for software generation. For complex autonomous debugging and agentic engineering, Mixtral 8x22B provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Mixtral 8x22B and Mixtral 8x7B?+
Mixtral 8x22B is optimized for low-latency direct generation. In contrast, Mixtral 8x7B focuses on standard fast inference.
How does inference speed (tokens per second) compare between Mixtral 8x22B and Mixtral 8x7B?+
Mixtral 8x22B generates approximately 55 tokens per second, while Mixtral 8x7B delivers 130 tokens per second. In latency-sensitive production environments, Mixtral 8x7B provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mixtral 8x22B and Mixtral 8x7B to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Mixtral 8x7B to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mixtral 8x22B. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
- Mistral NeMo vs Mixtral 8x22B: Benchmarks, Pricing & Speed Comparison비교
- Mixtral 8x22B vs Llama 3 (70B): Benchmarks, Pricing & Speed Comparison비교
- Mixtral 8x7B vs Llama 3 (8B): Benchmarks, Pricing & Speed Comparison비교
- Mixtral 8x7B vs Mistral 7B: Benchmarks, Pricing & Speed Comparison비교
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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.
공식 출처: GetInfoUs Independent AI Benchmarks & Official API Specs