Mixtral 8x7B vs Llama 3 (8B)
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mixtral 8x7B by Mistral AI and Llama 3 (8B) by Meta 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, Llama 3 (8B) holds a notable pricing advantage for token ingestion. On output generation, Llama 3 (8B) is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Mixtral 8x7B provides an expanded memory horizon with 33K tokens. In latency-sensitive production environments, Llama 3 (8B) 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 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
Llama 3 (8B)
오픈 웨이트 모델Meta의 Llama 3 (8B) 모델은 8K 토큰 컨텍스트 윈도우와 입력 $0.05/1M 토큰, 출력 $0.1/1M 토큰의 합리적인 API 요금을 제공하며 코딩, 추론 및 엔터프라이즈 자동화에 최적화되어 있습니다.
$0.05 / 1M
$0.10 / 1M
8.2K tokens
12%
한눈에 보는 핵심 차이점
- Llama 3 (8B) is 3.0x cheaper for API usage (input + output tokens combined).
- Mixtral 8x7B has a larger context window (33K vs 8K tokens).
- Llama 3 (8B) is faster at 210 tok/s vs 130 tok/s.
| Feature | Mixtral 8x7B | Llama 3 (8B) |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 37 | 34 |
Provider | Mistral AI | Meta |
Input Price Cost per million input tokens | $0.20 / 1M | $0.05 / 1M |
Output Price Cost per million output tokens | $0.20 / 1M | $0.10 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.10 / 1M | $0.03 / 1M |
Context Window Maximum input + output tokens | 32.8K tokens~49 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 4K | 4K |
Throughput Speed Tokens generated per second | 130 tok/s | 210 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 12% |
GPQA Diamond Graduate-level reasoning | 38% | 34.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.05 / 1M | $0.01 / 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.
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 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
Llama 3 (8B) · Strengths & Key Advantages
- Meta의 첨단 Dense GQA Transformer with 128K Tokenizer 아키텍처와 최대 8K 토큰 컨텍스트 지원
- 경쟁력 있는 API 가격(입력 $0.05/1M, 출력 $0.1/1M)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 8x7B | Llama 3 (8B) |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.20 |
Output Tokens / Task Tokens generated per task | 24K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
기술 사양 (Specifications)
Architecture, release timeline, capabilities, and modalities.
| Feature | Mixtral 8x7B | Llama 3 (8B) |
|---|---|---|
| Official API Model ID (SDK) | mixtral-8x7b-latest | meta-llama/Llama-3-(8B) |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2023-12 | 2024-04 |
| Knowledge Cutoff | Late 2023 | March 2024 |
| Architecture | Sparse Mixture of Experts (MoE, 2 active of 8) | Dense GQA Transformer with 128K Tokenizer |
| Parameters | 46.7B (12.9B active) | 8B |
| 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 ($0.2/1M in) across full 33K window | Flat rate ($0.05/1M in) across full 8K window |
| Prompt Cache Policy & TTL | Cached input at $0.05/1M tokens | Cached input at $0.0125/1M 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) · 29 GB VRAM (4-bit Quantized) · 100 GB VRAM (Native BF16) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 5 GB VRAM (4-bit Quantized) · 17 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 8x7B vs Llama 3 (8B): 핵심 요약 문답 (FAQ)
Which is more intelligent, Mixtral 8x7B or Llama 3 (8B)?
Mixtral 8x7B is more intelligent. It scores 37 compared with 34 on the composite Intelligence Index.
Which model is faster?
Llama 3 (8B) is faster, generating 210 tokens per second compared with 130 tokens per second.
Which model is cheaper to run?
Llama 3 (8B) is cheaper at $0.03 per 1M tokens blended, compared with $0.10 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3 (8B) has lower latency, with a time to first token of 0.22s compared with 0.38s.
Which model has a larger context window?
Mixtral 8x7B has a larger context window, supporting 32.8K tokens compared with 8.2K tokens.
종합 평가
Verdict & Model Selection Recommendation: Choose Mixtral 8x7B if your priority is Cost-effective multilingual enterprise pipelines, customer service bots, and open MoE research. Its performance in demanding tasks justifies its operational token cost. Choose Llama 3 (8B) if your workflow prioritizes Local edge computing, offline laptop inference, robotics, and low-latency API routers. 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 8x7B compare to Llama 3 (8B) in API token pricing and cost per token?+
Mixtral 8x7B costs $0.20 per 1M input tokens and $0.20 per 1M output tokens, whereas Llama 3 (8B) costs $0.05 input and $0.10 output. Overall, Llama 3 (8B) 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 8x7B or Llama 3 (8B)?+
Mixtral 8x7B supports a context window of 33K tokens (~25k words), while Llama 3 (8B) supports 8K tokens (~6k words). Therefore, Mixtral 8x7B can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Mixtral 8x7B or Llama 3 (8B)?+
Mixtral 8x7B scores at the frontier of coding benchmarks, while Llama 3 (8B) scores 12%. For complex autonomous debugging and agentic engineering, Llama 3 (8B) provides robust code generation and tool calling.
What is the difference in reasoning capability and extended thinking between Mixtral 8x7B and Llama 3 (8B)?+
Mixtral 8x7B is optimized for low-latency direct generation. In contrast, Llama 3 (8B) focuses on standard fast inference.
How does inference speed (tokens per second) compare between Mixtral 8x7B and Llama 3 (8B)?+
Mixtral 8x7B generates approximately 130 tokens per second, while Llama 3 (8B) delivers 210 tokens per second. In latency-sensitive production environments, Llama 3 (8B) provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mixtral 8x7B and Llama 3 (8B) to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3 (8B) to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Mixtral 8x7B. 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.
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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.
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