Mixtral 8x22B vs Llama 3 (70B)
Benchmarks, API Token Pricing, Context Window & Speed Comparison
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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.
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Mixtral 8x22B
Open-Weights-ModellMixtral 8x22B von Mistral AI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 65K Token zu API-Preisen von $1.2/1M Input- und $1.2/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.20 / 1M
$1.20 / 1M
65.5K tokens
36%
Llama 3 (70B)
Open-Weights-ModellLlama 3 (70B) von Meta ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 8K Token zu API-Preisen von $0.35/1M Input- und $0.8/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$0.35 / 1M
$0.80 / 1M
8.2K tokens
24%
Wichtigste Unterschiede auf einen Blick
- 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 |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
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.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mixtral 8x22B · Strengths & Key Advantages
- Fortschrittliche Sparse Mixture-of-Experts (8x22B)-Architektur von Mistral AI mit 65K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1.2/1M Input, $1.2/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Llama 3 (70B) · Strengths & Key Advantages
- Fortschrittliche GQA High-Capacity Transformer-Architektur von Meta mit 8K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($0.35/1M Input, $0.8/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
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 |
Technische Spezifikationen
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): Schnelle Antworten (FAQ)
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.
Fazit
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.
Häufig gestellte Fragen (FAQ)
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.
Offizielle Quellen: GetInfoUs Independent AI Benchmarks & Official API Specs