Mixtral 8x22B vs Llama 3 (70B)
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mixtral 8x22B dari Mistral AI dan Llama 3 (70B) dari Meta adalah dua model AI unggulan. Mixtral 8x22B menawarkan jendela konteks 65.5K token dengan harga $1.20/$1.20 per 1 juta token, sedangkan Llama 3 (70B) menyediakan konteks 8.2K token seharga $0.35/$0.80 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Mixtral 8x22B
Model Dasar Bobot TerbukaMixtral 8x22B dari Mistral AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 66K, kecepatan 55 token/detik, indeks kecerdasan 44/100, serta tarif $1.
$1.20 / 1M
$1.20 / 1M
65.5K tokens
36%
Llama 3 (70B)
Model Dasar Bobot TerbukaLlama 3 (70B) dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 8K, kecepatan 110 token/detik, indeks kecerdasan 42/100, serta tarif $0.35/$0.
$0.35 / 1M
$0.80 / 1M
8.2K tokens
24%
Perbedaan Utama Sekilas
- 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 |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
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.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Mixtral 8x22B · Strengths & Key Advantages
- Jendela konteks 66K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 36% · GPQA Diamond 51.2%)
- Kecepatan generasi tinggi 55 token/detik dengan latensi token pertama (TTFT) hanya 700 ms
- Bobot terbuka (141B total (39B MoE)) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
Llama 3 (70B) · Strengths & Key Advantages
- Jendela konteks 8K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 24% · GPQA Diamond 46%)
- Kecepatan generasi tinggi 110 token/detik dengan latensi token pertama (TTFT) hanya 420 ms
- Bobot terbuka (70B) yang siap di-hosting mandiri di GPU lokal atau cloud privat melalui vLLM dan Ollama
Considerations & Trade-offs
- Membutuhkan infrastruktur GPU dengan kapasitas VRAM yang memadai untuk eksekusi lokal presisi penuh
- Pada tugas pembuktian ilmiah tingkat ekstrem, model penalaran berat kelas atas memberikan kedalaman lebih tinggi
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 |
Spesifikasi Teknis
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): Jawaban Cepat (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.
Kesimpulan
Baik Mixtral 8x22B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 3 (70B) (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Mixtral 8x22B unggul dalam penalaran dan pemrograman, Llama 3 (70B) memberikan kecepatan generasi token lebih tinggi, dan Llama 3 (70B) lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Mixtral 8x22B atau Llama 3 (70B)?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Mixtral 8x22B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Mixtral 8x22B vs Llama 3 (70B)?+
Mixtral 8x22B mengenakan biaya $1.20 / $1.20 per 1M token input/output, sedangkan Llama 3 (70B) sebesar $0.35 / $0.80. Secara keseluruhan, Llama 3 (70B) lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Mixtral 8x22B mendukung konteks 65.5K token dibandingkan 8.2K token pada Llama 3 (70B), sementara Llama 3 (70B) unggul dalam kecepatan output token per detik.
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): Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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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.
Sumber resmi: Dokumentasi API resmi Mistral AI & Meta serta evaluasi benchmark independen (2026).