Mixtral 8x22B vs Llama 3 (70B)
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mixtral 8x22B by Mistral AI and Llama 3 (70B) 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 (70B) holds a notable pricing advantage for token ingestion. On output generation, Llama 3 (70B) 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, Llama 3 (70B) 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.
İstediğiniz Modelleri Seçin ve Karşılaştırın
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
Mixtral 8x22B
Açık Ağırlıklı ModelMistral AI tarafından geliştirilen Mixtral 8x22B, 65K token bağlam penceresi ve 1M girdi başına $1.2, 1M çıktı başına $1.2 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$1.20 / 1M
$1.20 / 1M
65.5K tokens
36%
Llama 3 (70B)
Açık Ağırlıklı ModelMeta tarafından geliştirilen Llama 3 (70B), 8K token bağlam penceresi ve 1M girdi başına $0.35, 1M çıktı başına $0.8 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.35 / 1M
$0.80 / 1M
8.2K tokens
24%
Bir Bakışta Temel Farklar
- Llama 3 (70B) is 2.7x cheaper for API usage (input + output tokens combined).
- Mixtral 8x22B has a larger context window (66K vs 8K tokens).
- Llama 3 (70B) is faster at 110 tok/s vs 55 tok/s.
- Mixtral 8x22B leads on SWE-bench coding (36.0% vs 24.0%).
| Feature | Mixtral 8x22B | Llama 3 (70B) |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 42 |
Provider | Mistral AI | Meta |
Input Price Cost per million input tokens | $1.20 / 1M | $0.35 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $0.80 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.78 / 1M | $0.21 / 1M |
Context Window Maximum input + output tokens | 65.5K tokens~98 A4 pages of text | 8.2K tokens~12 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 55 tok/s | 110 tok/s |
SWE-bench Verified Real-world software engineering | 36% | 24% |
GPQA Diamond Graduate-level reasoning | 51.2% | 46% |
Cached Input Price Prompt caching discount per 1M tokens | $0.60 / 1M | $0.09 / 1M |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mixtral 8x22B · Strengths & Key Advantages
- Mistral AI imzalı gelişmiş Sparse Mixture-of-Experts (8x22B) mimarisi ve 65K token bağlam kapasitesi
- Dengeli API maliyeti ($1.2/1M girdi, $1.2/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Llama 3 (70B) · Strengths & Key Advantages
- Meta imzalı gelişmiş GQA High-Capacity Transformer mimarisi ve 8K token bağlam kapasitesi
- Dengeli API maliyeti ($0.35/1M girdi, $0.8/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Mixtral 8x22B | Llama 3 (70B) |
|---|---|---|
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 |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Mixtral 8x22B | Llama 3 (70B) |
|---|---|---|
| Official API Model ID (SDK) | mixtral-8x22b-latest | meta-llama/Llama-3-(70B) |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-04 | 2024-04 |
| Knowledge Cutoff | March 2024 | March 2024 |
| Architecture | Sparse Mixture-of-Experts (8x22B) | GQA High-Capacity Transformer |
| Parameters | 141B total (39B active MoE) | 70B |
| 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 ($0.35/1M in) across full 8K window |
| Prompt Cache Policy & TTL | Cached input at $0.6/1M tokens | Cached input at $0.0875/1M 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) · 43 GB VRAM (4-bit Quantized) · 151 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 Llama 3 (70B): Hızlı Yanıtlar (SSS)
Which is more intelligent, Mixtral 8x22B or Llama 3 (70B)?
Mixtral 8x22B is more intelligent. It scores 44 compared with 42 on the composite Intelligence Index.
Which model is faster?
Llama 3 (70B) is faster, generating 110 tokens per second compared with 55 tokens per second.
Which model is cheaper to run?
Llama 3 (70B) is cheaper at $0.21 per 1M tokens blended, compared with $0.78 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Llama 3 (70B) has lower latency, with a time to first token of 0.42s compared with 0.70s.
Which model has a larger context window?
Mixtral 8x22B has a larger context window, supporting 65.5K tokens compared with 8.2K tokens.
Sonuç
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 Llama 3 (70B) if your workflow prioritizes Enterprise private LLM hosting, on-premises compliance workflows, and domain fine-tuning. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Sıkça Sorulan Sorular (SSS)
How does Mixtral 8x22B compare to Llama 3 (70B) 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 Llama 3 (70B) costs $0.35 input and $0.80 output. Overall, Llama 3 (70B) 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 Llama 3 (70B)?+
Mixtral 8x22B supports a context window of 66K tokens (~49k words), while Llama 3 (70B) supports 8K tokens (~6k 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 Llama 3 (70B)?+
Mixtral 8x22B scores 36% on SWE-bench Verified while Llama 3 (70B) scores 24%. For autonomous software engineering and multi-file debugging, Mixtral 8x22B holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Mixtral 8x22B and Llama 3 (70B)?+
Mixtral 8x22B is optimized for low-latency direct generation. In contrast, Llama 3 (70B) focuses on standard fast inference.
How does inference speed (tokens per second) compare between Mixtral 8x22B and Llama 3 (70B)?+
Mixtral 8x22B generates approximately 55 tokens per second, while Llama 3 (70B) delivers 110 tokens per second. In latency-sensitive production environments, Llama 3 (70B) provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Mixtral 8x22B and Llama 3 (70B) to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 3 (70B) 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.
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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.
Resmi kaynaklar: GetInfoUs Independent AI Benchmarks & Official API Specs