Head-to-Head Model Comparison

Llama 3.1 405B vs GPT-4o

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial

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Llama 3.1 405BProfile

GPT-4oProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

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%

OpenAI

GPT-4o

Model Suara & Audio Asli

GPT-4o dari OpenAI adalah model AI yang dirancang untuk sintesis suara alami, pemrosesan audio real-time, dan transkripsi ucapan, menghadirkan jendela konteks 128K, kecepatan 105 token/detik, indeks kecerdasan 41/100, serta tarif $2.5/$10 p

Intelligence Index41 / 100

Input Token:

$2.50 / 1M

Output Token:

$10.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

38.8%

Perbedaan Utama Sekilas

Llama 3.1 405B is 13.0x cheaper for API usage (input + output tokens combined).
GPT-4o is faster at 105 tok/s vs 50 tok/s.
Llama 3.1 405B leads on SWE-bench coding (41.5% vs 38.8%).

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

Feature	Llama 3.1 405B	GPT-4o
Intelligence Index Composite reasoning & knowledge score (0-100)	48	41
Provider	Meta	OpenAI
Input Price Cost per million input tokens	$0.00 / 1M	$2.50 / 1M
Output Price Cost per million output tokens	$0.00 / 1M	$10.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.53 / 1M	$2.38 / 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	8K	16K
Throughput Speed Tokens generated per second	50 tok/s	105 tok/s
SWE-bench Verified Real-world software engineering	41.5%	38.8%
GPQA Diamond Graduate-level reasoning	53%	53.6%
Cached Input Price Prompt caching discount per 1M tokens	$0.75 / 1M	$1.25 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Llama 3.1 405B

+2.7% SWE-bench lead

Cost

Llama 3.1 405B

13.0x cheaper monthly runtime

Speed

GPT-4o

+110% faster generation

Context

Tied

128K tokens window

Skor Benchmark Terverifikasi

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

Llama 3.1 405BGPT-4o

SWE-bench Verified (Autonomous Coding)

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

41.5%vs38.8%

GPQA Diamond (Doctorate-Level Science & Math)

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

53%vs53.6%

MMLU-Pro (Multi-task Language Understanding)

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

77.2%vs72.6%

LMSYS Chatbot Arena Elo

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

1288ptsvs1286pts

Humanity's Last Exam (Frontier Expert Knowledge)

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

N/Avs4.1%

Terminal-Bench (Agentic Coding & Terminal Use)

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

N/Avs18.5%

SciCode (Scientific Computing)

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

N/Avs27.3%

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs43.9%

AIME (Advanced Math Competition)

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

N/Avs29.7%

Long-Context Reasoning

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

N/Avs58.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

50 tok/s vs105 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 3.1 405B0.85s

GPT-4o0.41s

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

Llama 3.1 405B10.85s

GPT-4o5.17s

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 3.1 405B Monthly API Bill

$1.13 / month

$0/1M in · $0/1M out

GPT-4o Monthly API Bill

$14.63 / month

$2.5/1M in · $10/1M out

Llama 3.1 405B saves approximately $13.50 / month (92%) under this workload.13.0x Savings

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

GPT-4o · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 38.8% · GPQA Diamond 53.6%)
Kecepatan generasi tinggi 105 token/detik dengan latensi token pertama (TTFT) hanya 410 ms
Skema harga API kompetitif (tarif $2.5/$10 per 1M token) dengan dukungan cache prompt dan output JSON terstruktur

Considerations & Trade-offs

Model cloud proprietari tertutup; bobot model tidak dapat diunduh untuk penggunaan lokal offline
Untuk trafik produksi skala besar, disarankan mengaktifkan fitur Prompt Caching dan Batch API guna menghemat biaya

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

Metric	Llama 3.1 405B	GPT-4o
Cost per Task Estimated USD to finish one task	$0.20	$0.68
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	Llama 3.1 405B	GPT-4o
Official API Model ID (SDK)	meta-llama/Llama-3.1-405B	gpt-4o
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI Responses API & Chat Completions v1
Release Date	2024-07	2024-05
Knowledge Cutoff	December 2023	October 2023
Architecture	World’s Largest Open Foundation Transformer	Omni-Multimodal Dense
Parameters	405B	Confidential
Enterprise Rate Limits (RPM / TPM)	Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud	10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)	Free across full context window (only KV-cache VRAM scales)	Flat rate ($2.5/1M in) across full 128K window
Prompt Cache Policy & TTL	Automatic prefix KV-caching in vLLM / SGLang (0 cost)	Automatic prefix caching at $1.25/1M (no extra cache-write fee)
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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	Native Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning Support	Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory)	Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token Billing	Text BPE (~4 chars/token)	Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec)
Quantization & Min GPU VRAM	BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 251 GB VRAM (4-bit Quantized) · 871 GB VRAM (Native BF16)	Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier Classification	Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma)	OpenAI Preparedness Framework Audited & System Card Published
Enterprise Compliance (ZDR/HIPAA/SOC2)	100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision, audio
Output Modalities	text	text, audio

Llama 3.1 405B vs GPT-4o: Jawaban Cepat (FAQ)

Which is more intelligent, Llama 3.1 405B or GPT-4o?

Llama 3.1 405B is more intelligent. It scores 48 compared with 41 on the composite Intelligence Index.

Which model is faster?

GPT-4o is faster, generating 105 tokens per second compared with 50 tokens per second.

Which model is cheaper to run?

Llama 3.1 405B is cheaper at $0.53 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-4o has lower latency, with a time to first token of 0.41s compared with 0.85s.

Kesimpulan

Baik 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.) maupun GPT-4o (Sangat cocok untuk sintesis suara alami, pemrosesan audio real-time, dan transkripsi ucapan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Llama 3.1 405B unggul dalam penalaran dan pemrograman, GPT-4o memberikan kecepatan generasi token lebih tinggi, dan Llama 3.1 405B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Llama 3.1 405B atau GPT-4o?+

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 3.1 405B vs GPT-4o?+

Llama 3.1 405B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan GPT-4o sebesar $2.50 / $10.00. Secara keseluruhan, Llama 3.1 405B lebih hemat biaya.

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

Llama 3.1 405B mendukung konteks 128K token dibandingkan 128K token pada GPT-4o, sementara GPT-4o 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 3.1 405B vs GPT-4o

Ganti & Bandingkan Model Apa Saja

Llama 3.1 405B

GPT-4o

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Llama 3.1 405B · Strengths & Key Advantages

Considerations & Trade-offs

GPT-4o · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Llama 3.1 405B vs GPT-4o: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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