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
Amiral Gemisi Öncü ModelxAI tarafından geliştirilen Grok-4; öncü kurumsal akıl yürütme, yazılım geliştirme ve üretim ölçekli AI iş akışları için tasarlanmış olup 1M bağlam penceresi, 125 token/sn hız, 92/100 zeka indeksi ve 1M token başına $2.5/$10 fiyatlandırma s
$2.50 / 1M
$10.00 / 1M
1M tokens
76.5%
Llama 5
Açık Ağırlıklı Temel ModelMeta tarafından geliştirilen Llama 5; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 5M bağlam penceresi, 65 token/sn hız, 94/100 zeka indeksi ve ücretsiz açık ağırlık erişimi s
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Bir Bakışta Temel Farklar
- 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 |
Hangi Alanda Kim Kazanıyor?
At-a-glance decision frameworkDoğrulanmış Benchmark Puanları
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.
Gecikme Süresi ve Yanıt Hızı
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Gerçek Zamanlı Token Maliyet Simülatörü
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Grok-4 · Strengths & Key Advantages
- 1M bağlam penceresi ve yanıt başına maksimum 64K çıktı tokenı kapasitesi (SWE-bench Verified 76.5% · GPQA Diamond 77.8%)
- Sadece 680 ms ilk token gecikmesi (TTFT) ile saniyede 125 token yüksek üretim hızı
- İstem önbellekleme (Prompt Caching), araç çağırma ve yapılandırılmış JSON çıktısını destekleyen rekabetçi API ekonomisi (1M token başına $2.5/$10 fiyatlandırma)
Considerations & Trade-offs
- Kapalı ağırlıklı bulut modelidir; çevrimdışı yerel sunuculara indirilerek çalıştırılamaz
- Yüksek hacimli üretim trafiğinde maliyetleri optimize etmek için Prompt Caching ve Batch API kullanımı önerilir
Llama 5 · Strengths & Key Advantages
- 5M bağlam penceresi ve yanıt başına maksimum 128K çıktı tokenı kapasitesi (SWE-bench Verified 74% · GPQA Diamond 84%)
- Sadece 850 ms ilk token gecikmesi (TTFT) ile saniyede 65 token yüksek üretim hızı
- vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (600B+ total (~40-80B ))
Considerations & Trade-offs
- Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
- Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir
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 |
Teknik Özellikler
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: Hızlı Yanıtlar (SSS)
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.
Sonuç
Choose Grok-4 for öncü kurumsal akıl yürütme, yazılım geliştirme ve üretim ölçekli ai iş akışları, kurumsal rag mimarileri ve ölçeklenebilir üretim api entegrasyonları için idealdir., or choose Llama 5 for özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal rag mimarileri ve ölçeklenebilir üretim api entegrasyonları için idealdir..
Sıkça Sorulan Sorular (SSS)
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.
Resmi kaynaklar: xAI & Meta Official Documentation