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.
任意のAIモデルを切り替えて比較
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Mixtral 8x7B
オープンウェイトモデルMistral AI が提供する Mixtral 8x7B は、最大 32K トークンのコンテキストウィンドウを備え、入力 $0.2/100万トークン・出力 $0.2/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.20 / 1M
$0.20 / 1M
32.8K tokens
N/A
Llama 3 (8B)
オープンウェイトモデルMeta が提供する Llama 3 (8B) は、最大 8K トークンのコンテキストウィンドウを備え、入力 $0.05/100万トークン・出力 $0.1/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.05 / 1M
$0.10 / 1M
8.2K tokens
12%
ひと目でわかる主な違い
- 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 |
分野別勝者スコアカード
At-a-glance decision frameworkEvaluations ongoing
検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mixtral 8x7B · Strengths & Key Advantages
- Mistral AI の高度な Sparse Mixture of Experts (MoE, 2 active of 8) アーキテクチャと 32K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.2/1M、出力 $0.2/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Llama 3 (8B) · Strengths & Key Advantages
- Meta の高度な Dense GQA Transformer with 128K Tokenizer アーキテクチャと 8K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0.05/1M、出力 $0.1/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
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 |
技術仕様・スペック
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): よくある質問 (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.
総合評価
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.
よくある質問 (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.
Related reading
- Llama 3 (8B) vs Llama 3 (70B): Benchmarks, Pricing & Speed Comparison比較
- Mixtral 8x22B vs Mixtral 8x7B: Benchmarks, Pricing & Speed Comparison比較
- Mixtral 8x7B vs Mistral 7B: Benchmarks, Pricing & Speed Comparison比較
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed Comparison比較
- Aion-3.0 vs Gemini 3.6 Flash: Benchmarks, Pricing & Speed Comparison比較
- Aion-Mini vs Laguna XS 2.1: Benchmarks, Pricing & Speed Comparison比較
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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs