Head-to-Head Model Comparison

Llama 3.3 70B vs Llama 3.1 405B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

Ganti & Bandingkan Model Apa Saja

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

Llama 3.3 70BProfile

Llama 3.1 405BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 3.3 70B

Model Dasar Bobot Terbuka

Llama 3.3 70B dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 280 token/detik, indeks kecerdasan 38/100, serta akses gratis

Intelligence Index38 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

39.5%

Llama 3.1 405B

Model Dasar Bobot Terbuka

Llama 3.1 405B dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 128K, kecepatan 50 token/detik, indeks kecerdasan 48/100, serta akses gratis

Intelligence Index48 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

41.5%

Perbedaan Utama Sekilas

Llama 3.3 70B is 7.5x cheaper for API usage (input + output tokens combined).
Llama 3.3 70B is faster at 280 tok/s vs 50 tok/s.
Llama 3.1 405B leads on SWE-bench coding (41.5% vs 39.5%).

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

Feature	Llama 3.3 70B	Llama 3.1 405B
Intelligence Index Composite reasoning & knowledge score (0-100)	38	48
Provider	Meta	Meta
Input Price Cost per million input tokens	$0.00 / 1M	$0.00 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$0.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.07 / 1M	$0.53 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	128K tokens~192 A4 pages of text
Max Output Tokens Per single request	4K	8K
Throughput Speed Tokens generated per second	280 tok/s	50 tok/s
SWE-bench Verified Real-world software engineering	39.5%	41.5%
GPQA Diamond Graduate-level reasoning	48.9%	53%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.75 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Llama 3.1 405B

+2.0% SWE-bench lead

Cost

Llama 3.3 70B

7.5x cheaper monthly runtime

Speed

Llama 3.3 70B

+460% faster generation

Context

Tied

128K tokens window

Skor Benchmark Terverifikasi

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

Llama 3.3 70BLlama 3.1 405B

SWE-bench Verified (Autonomous Coding)

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

39.5%vs41.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

48.9%vs53%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs77.2%

LMSYS Chatbot Arena Elo

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

1285ptsvs1288pts

Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

7.8%vsN/A

Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

26.1%vsN/A

SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

23.7%vsN/A

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

42.5%vsN/A

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

56.2%vsN/A

Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

63.9%vsN/A

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

280 tok/s vs50 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

Llama 3.3 70B0.25s

Llama 3.1 405B0.85s

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

Llama 3.3 70B2.04s

Llama 3.1 405B10.85s

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)

Llama 3.3 70B Monthly API Bill

$0.15 / month

$0/1M in · $0/1M out

Llama 3.1 405B Monthly API Bill

$1.13 / month

$0/1M in · $0/1M out

Llama 3.3 70B saves approximately $0.97 / month (87%) under this workload.7.5x Savings

Llama 3.3 70B · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 4K token (SWE-bench Verified 39.5% · GPQA Diamond 48.9%)
Kecepatan generasi tinggi 280 token/detik dengan latensi token pertama (TTFT) hanya 250 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

Llama 3.1 405B · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 41.5% · GPQA Diamond 53%)
Kecepatan generasi tinggi 50 token/detik dengan latensi token pertama (TTFT) hanya 850 ms
Bobot terbuka (405B) 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	Llama 3.3 70B	Llama 3.1 405B
Cost per Task Estimated USD to finish one task	$0.21	$0.20
Output Tokens / Task Tokens generated per task	45K	24K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.3 70B	Llama 3.1 405B
Official API Model ID (SDK)	meta-llama/Llama-3.3-70B	meta-llama/Llama-3.1-405B
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-12	2024-07
Knowledge Cutoff	December 2023	December 2023
Architecture	Dense Autoregressive Transformer	World’s Largest Open Foundation Transformer
Parameters	70B	405B
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)	Free across full context window (only KV-cache VRAM scales)	Free across full context window (only KV-cache VRAM scales)
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix KV-caching in vLLM / SGLang (0 cost)
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) · 43 GB VRAM (4-bit Quantized) · 151 GB VRAM (Native BF16)	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 251 GB VRAM (4-bit Quantized) · 871 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

Llama 3.3 70B vs Llama 3.1 405B: Jawaban Cepat (FAQ)

Which is more intelligent, Llama 3.3 70B or Llama 3.1 405B?

Llama 3.1 405B is more intelligent. It scores 48 compared with 38 on the composite Intelligence Index.

Which model is faster?

Llama 3.3 70B is faster, generating 280 tokens per second compared with 50 tokens per second.

Which model is cheaper to run?

Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $0.53 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 3.3 70B has lower latency, with a time to first token of 0.25s compared with 0.85s.

Kesimpulan

Baik Llama 3.3 70B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 3.1 405B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Llama 3.1 405B unggul dalam penalaran dan pemrograman, Llama 3.3 70B memberikan kecepatan generasi token lebih tinggi, dan Llama 3.3 70B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Llama 3.3 70B atau Llama 3.1 405B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 3.1 405B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Llama 3.3 70B vs Llama 3.1 405B?+

Llama 3.3 70B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Llama 3.1 405B sebesar $0.00 / $0.00. Secara keseluruhan, Llama 3.3 70B lebih hemat biaya.

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

Llama 3.3 70B mendukung konteks 128K token dibandingkan 128K token pada Llama 3.1 405B, sementara Llama 3.3 70B 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)

Llama 3.3 70B vs Llama 3.1 405B

Ganti & Bandingkan Model Apa Saja

Llama 3.3 70B

Llama 3.1 405B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Llama 3.3 70B · Strengths & Key Advantages

Considerations & Trade-offs

Llama 3.1 405B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Llama 3.3 70B vs Llama 3.1 405B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

Related reading