Head-to-Head Model Comparison

Mixtral 8x7B vs Llama 3 (8B)

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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Select any AI models from our verified benchmark database to generate live side-by-side matrices.

Mixtral 8x7BProfile

Llama 3 (8B)Profile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Mistral AI

Mixtral 8x7B

Model Dasar Bobot Terbuka

Mixtral 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.

Intelligence Index37 / 100

Input Token:

$0.20 / 1M

Output Token:

$0.20 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

Llama 3 (8B)

Inferensi Super Cepat

Llama 3 (8B) dari Meta adalah model AI yang dirancang untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, menghadirkan jendela konteks 8K, kecepatan 210 token/detik, indeks kecerdasan 34/100, serta tarif $0.

Intelligence Index34 / 100

Input Token:

$0.05 / 1M

Output Token:

$0.10 / 1M

Context Window:

8.2K tokens

SWE-bench Verified:

12%

Perbedaan Utama Sekilas

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.

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

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

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Llama 3 (8B)

3.0x cheaper monthly runtime

Speed

Llama 3 (8B)

+62% faster generation

Context

Mixtral 8x7B

32.8K tokens window

Skor Benchmark Terverifikasi

Official evaluation metrics verified by independent third-party research labs.

Mixtral 8x7BLlama 3 (8B)

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

N/Avs12%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

38%vs34.2%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

66.5%vs62.5%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1195ptsvs1150pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 tok/s vs210 tok/s

Latensi & Waktu Respons

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Mixtral 8x7B0.38s

Llama 3 (8B)0.22s

Est. End-to-End Response (500 tokens)TTFT + generation time

Mixtral 8x7B4.23s

Llama 3 (8B)2.60s

Simulator Biaya Token Interaktif

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Mixtral 8x7B Monthly API Bill

$0.56 / month

$0.2/1M in · $0.2/1M out

Llama 3 (8B) Monthly API Bill

$0.18 / month

$0.05/1M in · $0.1/1M out

Llama 3 (8B) saves approximately $0.37 / month (67%) under this workload.3.0x Savings

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

Llama 3 (8B) · Strengths & Key Advantages

Jendela konteks 8K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 12% · GPQA Diamond 34.2%)
Kecepatan generasi tinggi 210 token/detik dengan latensi token pertama (TTFT) hanya 220 ms
Bobot terbuka (8B) 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 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

Spesifikasi Teknis

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): Jawaban Cepat (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.

Kesimpulan

Baik Mixtral 8x7B (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 (8B) (Sangat cocok untuk interaksi real-time dengan latensi sangat rendah dan routing bervolume tinggi, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Mixtral 8x7B unggul dalam penalaran dan pemrograman, Llama 3 (8B) memberikan kecepatan generasi token lebih tinggi, dan Llama 3 (8B) lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Mixtral 8x7B atau Llama 3 (8B)?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Mixtral 8x7B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Mixtral 8x7B vs Llama 3 (8B)?+

Mixtral 8x7B mengenakan biaya $0.20 / $0.20 per 1M token input/output, sedangkan Llama 3 (8B) sebesar $0.05 / $0.10. Secara keseluruhan, Llama 3 (8B) lebih hemat biaya.

Model mana yang memiliki jendela konteks lebih besar dan kecepatan lebih tinggi?+

Mixtral 8x7B mendukung konteks 32.8K token dibandingkan 8.2K token pada Llama 3 (8B), sementara Llama 3 (8B) unggul dalam kecepatan output token per detik.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Mixtral 8x7B vs Llama 3 (8B)

Ganti & Bandingkan Model Apa Saja

Mixtral 8x7B

Llama 3 (8B)

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Mixtral 8x7B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3 (8B) · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Mixtral 8x7B vs Llama 3 (8B): Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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