Grok-4 vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Grok-4 by xAI against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Grok-4
Model Unggulan Garis DepanGrok-4 dari xAI adalah model AI yang dirancang untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi AI, menghadirkan jendela konteks 1M, kecepatan 125 token/detik, indeks kecerdasan 92/100, serta tarif $2.5/$10 p
$2.50 / 1M
$10.00 / 1M
1M tokens
76.5%
Llama 5
Model Dasar Bobot TerbukaLlama 5 dari Meta adalah model AI yang dirancang untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, menghadirkan jendela konteks 5M, kecepatan 65 token/detik, indeks kecerdasan 94/100, serta akses gratis open-weig
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Perbedaan Utama Sekilas
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 1M tokens).
- Grok-4 is faster at 125 tok/s vs 65 tok/s.
- Grok-4 leads on SWE-bench coding (76.5% vs 74.0%).
| Feature | Grok-4 | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 92 | 94 |
Provider | xAI | Meta |
Input Price Cost per million input tokens | $2.50 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.68 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 64K | 128K |
Throughput Speed Tokens generated per second | 125 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 76.5% | 74% |
GPQA Diamond Graduate-level reasoning | 77.8% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.25 / 1M | $0.00 / 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.
Grok-4 · Strengths & Key Advantages
- Jendela konteks 1M dengan kapasitas output maksimum hingga 64K token (SWE-bench Verified 76.5% · GPQA Diamond 77.8%)
- Kecepatan generasi tinggi 125 token/detik dengan latensi token pertama (TTFT) hanya 680 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
Llama 5 · Strengths & Key Advantages
- Jendela konteks 5M dengan kapasitas output maksimum hingga 128K token (SWE-bench Verified 74% · GPQA Diamond 84%)
- Kecepatan generasi tinggi 65 token/detik dengan latensi token pertama (TTFT) hanya 850 ms
- Bobot terbuka (600B+ total (~40-80B )) 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 | Grok-4 | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.65 | $0.00 |
Output Tokens / Task Tokens generated per task | 48K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 24K | 20K |
Spesifikasi Teknis
Architecture, release timeline, capabilities, and modalities.
| Feature | Grok-4 | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | grok-4-latest | meta-llama/Llama-5 |
| API Protocol | xAI REST API (OpenAI-Compatible) | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-07 | 2026-04 |
| Knowledge Cutoff | Real-time (Up to October 2026) | January 2026 |
| Architecture | Colossus-Scale Dense Mixture-of-Experts | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| Enterprise Rate Limits (RPM / TPM) | 5,000 – 15,000 RPM (Production Tier) · 4,000,000 – 10,000,000 TPM | Unlimited (Hardware-bound on self-hosted GPU) / 10,000 RPM Cloud · Unlimited local throughput / 10M+ TPM Managed Cloud |
| Long-Context Tier (>128K/200K) | Flat rate ($2.5/1M in) across full 1000K window | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Cached input at $0.25/1M tokens | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| Computer Use / GUI Agent | Supported (Screen coordinate grounding, browser & desktop GUI action loops) | 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 | Real-time Web & X (Twitter) Search Grounding + Code Execution | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | API Prompt Engineering & RAG Distillation | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) |
| Quantization & Min GPU VRAM | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 372 GB VRAM (4-bit Quantized) · 1290 GB VRAM (Native BF16) |
| Safety & Frontier Classification | xAI Production Safety & Alignment Verified | Open-Weights System Card + Customizable Guardrail Layer (Llama Guard / ShieldGemma) |
| Enterprise Compliance (ZDR/HIPAA/SOC2) | SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier | 100% Air-Gapped On-Premises / Private VPC · Full HIPAA, GDPR & SOC2 Control |
| Input Modalities | text, vision | text, vision, video, audio |
| Output Modalities | text | text |
Grok-4 vs Llama 5: Jawaban Cepat (FAQ)
Which is more intelligent, Grok-4 or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 92 on the composite Intelligence Index.
Which model is faster?
Grok-4 is faster, generating 125 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Llama 5 is cheaper at $0.00 per 1M tokens blended, compared with $1.68 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Grok-4 has lower latency, with a time to first token of 0.68s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1M tokens.
Kesimpulan
Choose Grok-4 for sangat cocok untuk penalaran perusahaan garis depan, pemrograman tingkat lanjut, dan produksi ai, sistem rag perusahaan, dan integrasi api skala produksi., or choose Llama 5 for sangat cocok untuk self-hosting di server pribadi, fine-tuning, dan kedaulatan data penuh, sistem rag perusahaan, dan integrasi api skala produksi..
Pertanyaan Umum (FAQ)
Which is cheaper: Grok-4 or Llama 5?+
Grok-4 costs $2.5/1M input and $10/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Grok-4 or Llama 5?+
Grok-4 supports 1M tokens whereas Llama 5 supports 5M tokens.
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: xAI & Meta Official Documentation