Mixtral 8x22B vs Mixtral 8x7B
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Mixtral 8x22B dari Mistral AI dan Mixtral 8x7B dari Mistral AI adalah dua model AI unggulan. Mixtral 8x22B menawarkan jendela konteks 65.5K token dengan harga $1.20/$1.20 per 1 juta token, sedangkan Mixtral 8x7B menyediakan konteks 32.8K token seharga $0.20/$0.20 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%
Mixtral 8x7B
Model Dasar Bobot TerbukaMixtral 8x7B dari Mistral AI adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 33K, kecepatan 130 token/detik, indeks kecerdasan 37/100, serta tarif $0.
$0.20 / 1M
$0.20 / 1M
32.8K tokens
N/A
Perbedaan Utama Sekilas
- Mixtral 8x7B is 6.8x cheaper for API usage (input + output tokens combined).
- Mixtral 8x22B has a larger context window (66K vs 33K tokens).
- Mixtral 8x7B is faster at 130 tok/s vs 55 tok/s.
| Feature | Mixtral 8x22B | Mixtral 8x7B |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 44 | 37 |
Provider | Mistral AI | Mistral AI |
Input Price Cost per million input tokens | $1.20 / 1M | $0.20 / 1M |
Output Price Cost per million output tokens | $1.20 / 1M | $0.20 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.78 / 1M | $0.10 / 1M |
Context Window Maximum input + output tokens | 65.5K tokens~98 A4 pages of text | 32.8K tokens~49 A4 pages of text |
Max Output Tokens Per single request | 8K | 4K |
Throughput Speed Tokens generated per second | 55 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 36% | N/A |
GPQA Diamond Graduate-level reasoning | 51.2% | 38% |
Cached Input Price Prompt caching discount per 1M tokens | $0.60 / 1M | $0.05 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkEvaluations ongoing
Skor 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
Mixtral 8x7B · Strengths & Key Advantages
- Jendela konteks 33K dengan kapasitas output maksimum hingga 4K token (GPQA Diamond 38% · MMLU-Pro 66.5%)
- Kecepatan generasi tinggi 130 token/detik dengan latensi token pertama (TTFT) hanya 380 ms
- Bobot terbuka (46.7B (12.9B )) 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 | Mixtral 8x7B |
|---|---|---|
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 | Mixtral 8x7B |
|---|---|---|
| Official API Model ID (SDK) | mixtral-8x22b-latest | mixtral-8x7b-latest |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-04 | 2023-12 |
| Knowledge Cutoff | March 2024 | Late 2023 |
| Architecture | Sparse Mixture-of-Experts (8x22B) | Sparse Mixture of Experts (MoE, 2 active of 8) |
| Parameters | 141B total (39B active MoE) | 46.7B (12.9B active) |
| 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 ($0.2/1M in) across full 33K window |
| Prompt Cache Policy & TTL | Cached input at $0.6/1M tokens | Cached input at $0.05/1M 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) · 29 GB VRAM (4-bit Quantized) · 100 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 Mixtral 8x7B: Jawaban Cepat (FAQ)
Which is more intelligent, Mixtral 8x22B or Mixtral 8x7B?
Mixtral 8x22B is more intelligent. It scores 44 compared with 37 on the composite Intelligence Index.
Which model is faster?
Mixtral 8x7B is faster, generating 130 tokens per second compared with 55 tokens per second.
Which model is cheaper to run?
Mixtral 8x7B is cheaper at $0.10 per 1M tokens blended, compared with $0.78 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Mixtral 8x7B has lower latency, with a time to first token of 0.38s compared with 0.70s.
Which model has a larger context window?
Mixtral 8x22B has a larger context window, supporting 65.5K tokens compared with 32.8K 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 Mixtral 8x7B (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, Mixtral 8x7B memberikan kecepatan generasi token lebih tinggi, dan Mixtral 8x7B lebih hemat biaya untuk penggunaan API skala besar.
Pertanyaan Umum (FAQ)
Mana yang lebih baik untuk coding dan penalaran, Mixtral 8x22B atau Mixtral 8x7B?+
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 Mixtral 8x7B?+
Mixtral 8x22B mengenakan biaya $1.20 / $1.20 per 1M token input/output, sedangkan Mixtral 8x7B sebesar $0.20 / $0.20. Secara keseluruhan, Mixtral 8x7B lebih hemat biaya.
Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+
Mixtral 8x22B mendukung konteks 65.5K token dibandingkan 32.8K token pada Mixtral 8x7B, sementara Mixtral 8x7B 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
- Mistral NeMo vs Mixtral 8x22B: Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
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- Mixtral 8x7B vs Llama 3 (8B): Perbandingan Benchmark, Harga API & Kecepatan (2026)Bandingkan
- Mixtral 8x7B vs Mistral 7B: 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 & Mistral AI serta evaluasi benchmark independen (2026).