Llama 3.3 70B vs GPT-4o mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Llama 3.3 70B dari Meta dan GPT-4o mini dari OpenAI adalah dua model AI unggulan. Llama 3.3 70B menawarkan jendela konteks 128K token dengan harga $0.00/$0.00 per 1 juta token, sedangkan GPT-4o mini menyediakan konteks 128K token seharga $0.15/$0.60 per 1 juta token. Berikut perbandingan lengkap benchmark, latensi, dan biayanya.
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Llama 3.3 70B
Model Dasar Bobot TerbukaLlama 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
$0.00 / 1M
$0.00 / 1M
128K tokens
39.5%
GPT-4o mini
Model Ringan & Hemat BiayaGPT-4o mini dari OpenAI adalah model AI yang dirancang untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, menghadirkan jendela konteks 128K, kecepatan 140 token/detik, indeks kecerdasan 30/100, serta tarif $0.
$0.15 / 1M
$0.60 / 1M
128K tokens
24.5%
Perbedaan Utama Sekilas
- Llama 3.3 70B is 5.8x cheaper for API usage (input + output tokens combined).
- Llama 3.3 70B is faster at 280 tok/s vs 140 tok/s.
- Llama 3.3 70B leads on SWE-bench coding (39.5% vs 24.5%).
| Feature | Llama 3.3 70B | GPT-4o mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 30 |
Provider | Meta | OpenAI |
Input Price Cost per million input tokens | $0.00 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.07 / 1M | $0.14 / 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 | 16K |
Throughput Speed Tokens generated per second | 280 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 39.5% | 24.5% |
GPQA Diamond Graduate-level reasoning | 48.9% | 40.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.07 / 1M |
Siapa Unggul di Mana?
At-a-glance decision frameworkSkor Benchmark Terverifikasi
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latensi & Waktu Respons
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulator Biaya Token Interaktif
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
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
GPT-4o mini · Strengths & Key Advantages
- Jendela konteks 128K dengan kapasitas output maksimum hingga 16K token (SWE-bench Verified 24.5% · GPQA Diamond 40.2%)
- Kecepatan generasi tinggi 140 token/detik dengan latensi token pertama (TTFT) hanya 310 ms
- Skema harga API kompetitif (tarif $0.15/$0.6 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
- 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 | GPT-4o mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.21 | $0.16 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Llama 3.3 70B | GPT-4o mini |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.3-70B | gpt-4o-mini |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-12 | 2024-07 |
| Knowledge Cutoff | December 2023 | October 2023 |
| Architecture | Dense Autoregressive Transformer | Small Multimodal Dense |
| Parameters | 70B | 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 ($0.15/1M in) across full 128K window |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Automatic prefix caching at $0.075/1M (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) |
| 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) | 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 |
| Output Modalities | text | text |
Llama 3.3 70B vs GPT-4o mini: Jawaban Cepat (FAQ)
Which is more intelligent, Llama 3.3 70B or GPT-4o mini?
Llama 3.3 70B is more intelligent. It scores 38 compared with 30 on the composite Intelligence Index.
Which model is faster?
Llama 3.3 70B is faster, generating 280 tokens per second compared with 140 tokens per second.
Which model is cheaper to run?
Llama 3.3 70B is cheaper at $0.07 per 1M tokens blended, compared with $0.14 (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 0.31s.
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 GPT-4o mini (Sangat cocok untuk efisiensi biaya API maksimal per token dan throughput produksi yang ringan, sistem RAG perusahaan, dan integrasi API skala produksi.) sangat tangguh: Llama 3.3 70B 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 GPT-4o mini?+
Dalam indeks kecerdasan gabungan dan benchmark coding, Llama 3.3 70B memimpin. Lihat skor SWE-bench dan GPQA di atas untuk detailnya.
Berapa perbandingan harga API Llama 3.3 70B vs GPT-4o mini?+
Llama 3.3 70B mengenakan biaya $0.00 / $0.00 per 1M token input/output, sedangkan GPT-4o mini sebesar $0.15 / $0.60. 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 GPT-4o mini, sementara Llama 3.3 70B unggul dalam kecepatan output token per detik.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
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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 & OpenAI serta evaluasi benchmark independen (2026).