Head-to-Head Model Comparison

LLaMA-1 (65B) vs Llama 2 (70B)

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Ditinjau oleh tim riset GetInfoUsPeninjauan terakhir: October 2026Kebijakan editorial
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
LLaMA-1 (65B) vs Llama 2 (70B): Benchmarks, Pricing & Speed Comparison

LLaMA-1 (65B) dari Meta dan Llama 2 (70B) dari Meta adalah dua model AI unggulan. LLaMA-1 (65B) menawarkan jendela konteks 2K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan Llama 2 (70B) menyediakan konteks 4.1K token seharga $0.40/$0.80 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.

Perbandingan Langsung (VS)

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Meta

LLaMA-1 (65B)

Model Dasar Bobot Terbuka

LLaMA-1 (65B) dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 2K, kecepatan 45 token/detik, indeks kecerdasan 25/100, serta akses gratis ope

Intelligence Index25 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

2K tokens

SWE-bench Verified:

N/A

Meta

Llama 2 (70B)

Model Dasar Bobot Terbuka

Llama 2 (70B) dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 4K, kecepatan 55 token/detik, indeks kecerdasan 31/100, serta tarif $0.4/$0.8

Intelligence Index31 / 100
Input Token:

$0.40 / 1M

Output Token:

$0.80 / 1M

Context Window:

4.1K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

  • LLaMA-1 (65B) is 1.0x cheaper for API usage (input + output tokens combined).
  • Llama 2 (70B) has a larger context window (4K vs 2K tokens).
  • Llama 2 (70B) is faster at 55 tok/s vs 45 tok/s.
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureLLaMA-1 (65B)Llama 2 (70B)
Intelligence Index
Composite reasoning & knowledge score (0-100)
2531
Provider
MetaMeta
Input Price
Cost per million input tokens
$0.00 / 1M$0.40 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$0.80 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$0.23 / 1M
Context Window
Maximum input + output tokens
2K tokens~3 A4 pages of text4.1K tokens~6 A4 pages of text
Max Output Tokens
Per single request
2K4K
Throughput Speed
Tokens generated per second
45 tok/s55 tok/s
SWE-bench Verified
Real-world software engineering
N/AN/A
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$0.10 / 1M

Siapa Unggul di Mana?

At-a-glance decision framework
Coding

Evaluations ongoing

Cost
LLaMA-1 (65B)
1.0x cheaper monthly runtime
Speed
Llama 2 (70B)
+22% faster generation
Context
Llama 2 (70B)
4.1K tokens window

Skor Benchmark Terverifikasi

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

LLaMA-1 (65B)Llama 2 (70B)
MMLU-Pro (Multi-task Language Understanding)

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

48%vs56.8%
LMSYS Chatbot Arena Elo

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

1020ptsvs1105pts
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

45 tok/s vs55 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-1 (65B)0.75s
Llama 2 (70B)0.60s
Est. End-to-End Response (500 tokens)TTFT + generation time
LLaMA-1 (65B)11.86s
Llama 2 (70B)9.69s

Simulator Biaya Token Interaktif

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
LLaMA-1 (65B) Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
Llama 2 (70B) Monthly API Bill
$1.47 / month
$0.4/1M in · $0.8/1M out
LLaMA-1 (65B) saves approximately $1.47 / month (100%) under this workload.1.0x Savings

LLaMA-1 (65B) · Strengths & Key Advantages

  • Jendela konteks 2K dengan kapasitas output maksimum hingga 2K token (MMLU-Pro 48% · Arena Elo 1020)
  • Kecepatan generasi tinggi 45 token/detik dengan latensi token pertama (TTFT) hanya 750 ms
  • Bobot terbuka (65B (also 7B, 13B, 33B)) 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 2 (70B) · Strengths & Key Advantages

  • Jendela konteks 4K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 56.8% · Arena Elo 1105)
  • Kecepatan generasi tinggi 55 token/detik dengan latensi token pertama (TTFT) hanya 600 ms
  • Bobot terbuka (70B (also 7B, 13B)) 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.

MetricLLaMA-1 (65B)Llama 2 (70B)
Cost per Task
Estimated USD to finish one task
$0.20$0.20
Output Tokens / Task
Tokens generated per task
24K24K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

FeatureLLaMA-1 (65B)Llama 2 (70B)
Official API Model ID (SDK)meta-llama/LLaMA-1-(65B)meta-llama/Llama-2-(70B)
API ProtocolOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGIOpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date2023-022023-07
Knowledge Cutoff2022September 2022
ArchitectureDecoder-Only Transformer with RoPE & SwiGLUGrouped-Query Attention (GQA) Transformer
Parameters65B (also 7B, 13B, 33B)70B (also 7B, 13B)
Enterprise Rate Limits (RPM / TPM)Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed CloudUnlimited (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)Flat rate ($0.4/1M in) across full 4K window
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Cached input at $0.1/1M tokens
Computer Use / GUI AgentText-only completion (No GUI action loop)Text-only completion (No GUI action loop)
MCP & Parallel Tool CallingPrompt-based JSON extraction onlyPrompt-based JSON extraction only
Web Search Grounding & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportFull 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 BillingText BPE (~4 chars/token)Text BPE (~4 chars/token)
Quantization & Min GPU VRAMBF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 40 GB VRAM (4-bit Quantized) · 140 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 ClassificationOpen-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 Control100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control
Input Modalitiestexttext
Output Modalitiestexttext

LLaMA-1 (65B) vs Llama 2 (70B): Jawaban Cepat (FAQ)

Which is more intelligent, LLaMA-1 (65B) or Llama 2 (70B)?

Llama 2 (70B) is more intelligent. It scores 31 compared with 25 on the composite Intelligence Index.

Which model is faster?

Llama 2 (70B) is faster, generating 55 tokens per second compared with 45 tokens per second.

Which model is cheaper to run?

LLaMA-1 (65B) is cheaper at $0.00 per 1M tokens blended, compared with $0.23 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Llama 2 (70B) has lower latency, with a time to first token of 0.60s compared with 0.75s.

Which model has a larger context window?

Llama 2 (70B) has a larger context window, supporting 4.1K tokens compared with 2K tokens.

Kesimpulan

Baik LLaMA-1 (65B) (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun Llama 2 (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: Llama 2 (70B) unggul dalam penalaran dan pemrograman, Llama 2 (70B) memberikan kecepatan generasi token lebih tinggi, dan LLaMA-1 (65B) lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, LLaMA-1 (65B) atau Llama 2 (70B)?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 2 (70B) memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API LLaMA-1 (65B) vs Llama 2 (70B)?+

LLaMA-1 (65B) mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan Llama 2 (70B) sebesar $0.40 / $0.80. Secara keseluruhan, LLaMA-1 (65B) lebih hemat biaya.

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

LLaMA-1 (65B) mendukung konteks 2K token dibandingkan 4.1K token pada Llama 2 (70B), sementara Llama 2 (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.

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 Meta & Meta serta evaluasi benchmark independen (2026).