Head-to-Head Model Comparison

Llama 3.1 70B vs OLMo 2 13B

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 3.1 70BProfile

OLMo 2 13BProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Llama 3.1 70B

Model Dasar Bobot Terbuka

Llama 3.1 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 75 token/detik, indeks kecerdasan 75/100, serta akses gratis o

Intelligence Index75 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

42%

AI2

OLMo 2 13B

Model Dasar Bobot Terbuka

OLMo 2 13B dari AI2 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 71/100, serta akses gratis open-

Intelligence Index71 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

32.8K tokens

SWE-bench Verified:

N/A

Perbedaan Utama Sekilas

Both models cost the same under this workload — choose on capability, not price.
Llama 3.1 70B has a larger context window (128K vs 33K tokens).
OLMo 2 13B is faster at 130 tok/s vs 75 tok/s.

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

Feature	Llama 3.1 70B	OLMo 2 13B
Intelligence Index Composite reasoning & knowledge score (0-100)	75	71
Provider	Meta	AI2
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	$0.00 / 1M	$0.00 / 1M
Context Window Maximum input + output tokens	128K tokens~192 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	75 tok/s	130 tok/s
SWE-bench Verified Real-world software engineering	42%	N/A
GPQA Diamond Graduate-level reasoning	52%	N/A

Siapa Unggul di Mana?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Tied

Identical monthly runtime cost

Speed

OLMo 2 13B

+73% faster generation

Context

Llama 3.1 70B

128K tokens window

Skor Benchmark Terverifikasi

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

Llama 3.1 70BOLMo 2 13B

SWE-bench Verified (Autonomous Coding)

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

42%vsN/A

GPQA Diamond (Doctorate-Level Science & Math)

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

52%vsN/A

MMLU-Pro (Multi-task Language Understanding)

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

79.5%vs66%

LMSYS Chatbot Arena Elo

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

1330ptsvs1200pts

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

75 tok/s vs130 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 70B0.40s

OLMo 2 13B0.31s

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

Llama 3.1 70B7.07s

OLMo 2 13B4.16s

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 70B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

OLMo 2 13B Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

These models cost the same under this workload — no savings either way.Break-even

Llama 3.1 70B · Strengths & Key Advantages

Jendela konteks 128K dengan kapasitas output maksimum hingga 8K token (SWE-bench Verified 42% · GPQA Diamond 52%)
Kecepatan generasi tinggi 75 token/detik dengan latensi token pertama (TTFT) hanya 400 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

OLMo 2 13B · Strengths & Key Advantages

Jendela konteks 33K dengan kapasitas output maksimum hingga 4K token (MMLU-Pro 66% · Arena Elo 1200)
Kecepatan generasi tinggi 130 token/detik dengan latensi token pertama (TTFT) hanya 310 ms
Bobot terbuka (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.

Metric	Llama 3.1 70B	OLMo 2 13B
Cost per Task Estimated USD to finish one task	$0.00	$0.00
Output Tokens / Task Tokens generated per task	6K	10K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Spesifikasi Teknis

Architecture, release timeline, capabilities, and modalities.

Feature	Llama 3.1 70B	OLMo 2 13B
Official API Model ID (SDK)	meta-llama/Llama-3.1-70B	olmo-2-13b
API Protocol	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI	OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI
Release Date	2024-07	2024-11
Knowledge Cutoff	December 2023	August 2024
Architecture	High-capacity dense multilingual transformer	Fully Open Research Transformer (Data, Code & Weights)
Parameters	70B	13B
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) · 8 GB VRAM (4-bit Quantized) · 28 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 3.1 70B vs OLMo 2 13B: Jawaban Cepat (FAQ)

Which is more intelligent, Llama 3.1 70B or OLMo 2 13B?

Llama 3.1 70B is more intelligent. It scores 75 compared with 71 on the composite Intelligence Index.

Which model is faster?

OLMo 2 13B is faster, generating 130 tokens per second compared with 75 tokens per second.

Which model has lower latency?

OLMo 2 13B has lower latency, with a time to first token of 0.31s compared with 0.40s.

Which model has a larger context window?

Llama 3.1 70B has a larger context window, supporting 128K tokens compared with 32.8K tokens.

Kesimpulan

Baik Llama 3.1 70B (Sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem RAG perusahaan, dan integrasi API skala produksi.) maupun OLMo 2 13B (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 70B unggul dalam penalaran dan pemrograman, OLMo 2 13B memberikan kecepatan generasi token lebih tinggi, dan Llama 3.1 70B lebih hemat biaya untuk penggunaan API skala besar.

Pertanyaan Umum (FAQ)

Mana yang lebih baik untuk coding dan penalaran, Llama 3.1 70B atau OLMo 2 13B?+

Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 3.1 70B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.

Berapa perbandingan harga API Llama 3.1 70B vs OLMo 2 13B?+

Llama 3.1 70B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan OLMo 2 13B sebesar $0.00 / $0.00. Secara keseluruhan, Llama 3.1 70B lebih hemat biaya.

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

Llama 3.1 70B mendukung konteks 128K token dibandingkan 32.8K token pada OLMo 2 13B, sementara OLMo 2 13B 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 70B vs OLMo 2 13B

Ganti & Bandingkan Model Apa Saja

Llama 3.1 70B

OLMo 2 13B

Siapa Unggul di Mana?

Skor Benchmark Terverifikasi

Latensi & Waktu Respons

Simulator Biaya Token Interaktif

Llama 3.1 70B · Strengths & Key Advantages

Considerations & Trade-offs

OLMo 2 13B · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Spesifikasi Teknis

Llama 3.1 70B vs OLMo 2 13B: Jawaban Cepat (FAQ)

Kesimpulan

Pertanyaan Umum (FAQ)

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