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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Live comparison: no in-depth editorial review has been published for this pair yet.
Gemini 4 Argon
Rey multimodal con salida de 1MGoogle de DeepMind insignia de frontera razonamiento motor con 1-millones-token salida limits, autónomo cybersecurity defense, y en tiempo real audio/visión ingesta.
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Llama 4 Behemoth
Gigante abierto de 2Tde Meta gargantuan 2-billones parámetro abierto modelo pushing el de frontera de abierto-weight capacidad y 10M contexto.
$0.00 / 1M
$0.00 / 1M
10M tokens
75%
Diferencias Clave de un Vistazo
- 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%).
| Característica | 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 |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 4 Argon · Strengths & Key Advantages
- Groundbreaking 1,000,000 token maximum salida generación capacidad via largo Decode Continuation
- 1M-token entrada window con 99.7% GraphWalks recall (84.2% en el 256K-1M range)
- nativo procesamiento de completo audio, video streams, y visual datos (91.7% en LVBench)
- Half el token precio de de Anthropic Opus 5.5 y #1 en Chatbot Arena (1525 Elo)
Considerations & Trade-offs
- masivo token outputs puede incur alto absolute costos if unconstrained (~62K salida tokens por index tarea)
- $2/$10 precios es un introductory 50% discount; estándar rates revert un $4/$20 once el promotion ends
Llama 4 Behemoth · Strengths & Key Advantages
- sin rival 2T parámetro abierto capacidad
- Staggering ~10 millones token contexto horizon
- de vanguardia codificación y razonamiento scores
Considerations & Trade-offs
- Demands alto-end datacenter multinodo GPU clústeres (8x H100 minimum)
- Slow inferencia rendimiento en comparación con smaller modelos
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 |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | 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: Respuestas Rápidas (FAQ)
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.
Veredicto
Choose Gemini 4 Argon for whole-repositorio generación de código, completo-book síntesis, video comprensión, y multi-hour audio procesamiento, or choose Llama 4 Behemoth for national laboratories, grande empresarial ai clústeres, y extreme científico modeling.
Preguntas frecuentes
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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Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: Google & Meta Official Documentation