Gemini 4 Argon vs Llama 4 Behemoth
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 4 Argon by Google against Llama 4 Behemoth by Meta across real-world coding, reasoning, token economics, and latency.
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Gemini 4 Argon
Derin Akıl Yürütme ModeliGoogle tarafından geliştirilen Gemini 4 Argon, 1M token bağlam penceresi ve 1M girdi başına $2, 1M çıktı başına $10 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Llama 4 Behemoth
Açık Ağırlıklı ModelMeta tarafından geliştirilen Llama 4 Behemoth, 10M token bağlam penceresi ve 1M girdi başına $0, 1M çıktı başına $0 API fiyatlandırmasıyla yazılım geliştirme, analiz ve kurumsal otomasyon için tasarlanmıştır.
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Bir Bakışta Temel Farklar
- Llama 4 Behemoth is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 4 Behemoth has a larger context window (10M vs 1M tokens).
- Gemini 4 Argon is faster at 115 tok/s vs 45 tok/s.
- Gemini 4 Argon leads on SWE-bench coding (81.9% vs 75.0%).
| Feature | Gemini 4 Argon | Llama 4 Behemoth |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 97 | 93 |
Provider | Meta | |
Input Price Cost per million input tokens | $2.00 / 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 | $1.47 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1M tokens~1,500 A4 pages of text | 10M tokens~15,000 A4 pages of text |
Max Output Tokens Per single request | 1000K | 64K |
Throughput Speed Tokens generated per second | 115 tok/s | 45 tok/s |
SWE-bench Verified Real-world software engineering | 81.9% | 75% |
GPQA Diamond Graduate-level reasoning | 81.5% | 82% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 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.
Gemini 4 Argon · Strengths & Key Advantages
- Google imzalı gelişmiş Pathways Multimodal Sparse Mixture-of-Experts mimarisi ve 1M token bağlam kapasitesi
- Dengeli API maliyeti ($2/1M girdi, $10/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Llama 4 Behemoth · Strengths & Key Advantages
- Meta imzalı gelişmiş Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) mimarisi ve 10M token bağlam kapasitesi
- Dengeli API maliyeti ($0/1M girdi, $0/1M çıktı) ve yüksek yanıt hızı
- Kod üretimi, mantıksal çıkarım ve çok dilli görevlerde güçlü kıyaslama (benchmark) sonuçları
Considerations & Trade-offs
- Çok uzun bağlamlı sorgularda token maliyetlerinin dikkatli yönetilmesi gerekir
- Alan bazlı özel iş akışlarında en yüksek verim için doğru istem (prompt) tasarımı önerilir
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 4 Argon | Llama 4 Behemoth |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.45 | $0.00 |
Output Tokens / Task Tokens generated per task | 75K | 35K |
Reasoning Tokens / Task Thinking tokens burned per task | 36K | 15K |
Teknik Özellikler
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 4 Argon | Llama 4 Behemoth |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-4-argon | meta-llama/Llama-4-Behemoth |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-05 |
| Knowledge Cutoff | June 2026 | February 2026 |
| Architecture | Pathways Multimodal Sparse Mixture-of-Experts | Colossal sparse MoE architecture (288B active / 2T total, 16-of-2 MoE) |
| Parameters | Confidential | 2T total / 288B active (16-of-2 MoE) |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 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) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL | 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context 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) · Audio (~25–32 tokens/sec) · Video (~258–300 tokens/sec) | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) |
| 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) · 1240 GB VRAM (4-bit Quantized) · 4300 GB VRAM (Native BF16) |
| Safety & Frontier Classification | Google DeepMind Frontier Safety Framework 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, audio, video | text, vision |
| Output Modalities | text | text |
Gemini 4 Argon vs Llama 4 Behemoth: Hızlı Yanıtlar (SSS)
Which is more intelligent, Gemini 4 Argon or Llama 4 Behemoth?
Gemini 4 Argon is more intelligent. It scores 97 compared with 93 on the composite Intelligence Index.
Which model is faster?
Gemini 4 Argon is faster, generating 115 tokens per second compared with 45 tokens per second.
Which model is cheaper to run?
Llama 4 Behemoth is cheaper at $0.00 per 1M tokens blended, compared with $1.47 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 4 Argon has lower latency, with a time to first token of 0.85s compared with 1.20s.
Which model has a larger context window?
Llama 4 Behemoth has a larger context window, supporting 10M tokens compared with 1M tokens.
Sonuç
Choose Gemini 4 Argon for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve google tabanlı üretim sistemleri, or choose Llama 4 Behemoth for kurumsal otomasyon, yazılım mühendisliği, otonom yz ajanları ve meta tabanlı üretim sistemleri.
Sıkça Sorulan Sorular (SSS)
Which is cheaper: Gemini 4 Argon or Llama 4 Behemoth?+
Gemini 4 Argon costs $2/1M input and $10/1M output tokens, while Llama 4 Behemoth costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Gemini 4 Argon or Llama 4 Behemoth?+
Gemini 4 Argon supports 1M tokens whereas Llama 4 Behemoth supports 10M 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: Google & Meta Official Documentation