Llama 3.2 3B vs Gemini 4 Argon
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Llama 3.2 3B by Meta against Gemini 4 Argon by Google across real-world coding, reasoning, token economics, and latency.
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Llama 3.2 3B
Modelo Base de Pesos AbiertosLlama 3.2 3B de Meta es un modelo de IA diseñado para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, con ventana de contexto de 128K, velocidad de 240 tokens/s, índice de inteligencia de 52/100 y tari
$0.00 / 1M
$0.00 / 1M
128K tokens
N/A
Gemini 4 Argon
IA Multimodal NativaGemini 4 Argon de Google es un modelo de IA diseñado para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, con ventana de contexto de 1M, velocidad de 115 tokens/s, índice de inteligencia de 97/100 y tarifa d
$2.00 / 1M
$10.00 / 1M
1M tokens
81.9%
Diferencias Clave de un Vistazo
- Llama 3.2 3B is 1.0x cheaper for API usage (input + output tokens combined).
- Gemini 4 Argon has a larger context window (1M vs 128K tokens).
- Llama 3.2 3B is faster at 240 tok/s vs 115 tok/s.
| Característica | Llama 3.2 3B | Gemini 4 Argon |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 52 | 97 |
Provider | Meta | |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.47 / 1M |
Context Window Maximum input + output tokens | 128K tokens~192 A4 pages of text | 1M tokens~1,500 A4 pages of text |
Max Output Tokens Per single request | 4K | 1000K |
Throughput Speed Tokens generated per second | 240 tok/s | 115 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 81.9% |
GPQA Diamond Graduate-level reasoning | N/A | 81.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.10 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkEvaluations ongoing
Puntuaciones 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.
Llama 3.2 3B · Strengths & Key Advantages
- Ventana de contexto de 128K con hasta 4K tokens de salida máxima (MMLU-Pro 56.5% · Arena Elo 1190)
- Rendimiento ágil de 240 tokens/segundo con latencia inicial (TTFT) de solo 160 ms
- Pesos abiertos (3B) listos para autoalojamiento local o en nube privada mediante vLLM y Ollama
Considerations & Trade-offs
- Requiere infraestructura GPU dedicada con suficiente memoria VRAM para inferencia local sin cuantización
- En demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Gemini 4 Argon · Strengths & Key Advantages
- Ventana de contexto de 1M con hasta 1M tokens de salida máxima (SWE-bench Verified 81.9% · GPQA Diamond 81.5%)
- Rendimiento ágil de 115 tokens/segundo con latencia inicial (TTFT) de solo 850 ms
- Economía de API competitiva ($2/$10 por 1M de tokens) con soporte para caché de prompts y salidas JSON estructuradas
Considerations & Trade-offs
- Modelo propietario en la nube; no permite descargar los pesos para ejecución local sin conexión
- En volúmenes masivos de tokens, se recomienda activar el caché de prompts y Batch API para optimizar costos
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Llama 3.2 3B | Gemini 4 Argon |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.00 | $2.45 |
Output Tokens / Task Tokens generated per task | 3K | 75K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 36K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Llama 3.2 3B | Gemini 4 Argon |
|---|---|---|
| Official API Model ID (SDK) | meta-llama/Llama-3.2-3B | models/gemini-4-argon |
| API Protocol | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-09 | 2026-09 |
| Knowledge Cutoff | December 2023 | June 2026 |
| Architecture | Pruned and distilled compact transformer | Pathways Multimodal Sparse Mixture-of-Experts |
| Parameters | 3B | 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) · 4,000,000 – 10,000,000 TPM |
| Long-Context Tier (>128K/200K) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Implicit + Explicit Context Caching at $0.1/1M with configurable TTL |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Full Weights SFT, LoRA, QLoRA, DPO & GRPO RL (Unsloth / Axolotl / Llama-Factory) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) | 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 | BF16 / FP16, FP8 Dynamic, INT4 AWQ / GPTQ, GGUF (Q4_K_M / Q8_0) · 2 GB VRAM (4-bit Quantized) · 6 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) | Google DeepMind Frontier Safety Framework Verified |
| 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, audio, video |
| Output Modalities | text | text |
Llama 3.2 3B vs Gemini 4 Argon: Respuestas Rápidas (FAQ)
Which is more intelligent, Llama 3.2 3B or Gemini 4 Argon?
Gemini 4 Argon is more intelligent. It scores 97 compared with 52 on the composite Intelligence Index.
Which model is faster?
Llama 3.2 3B is faster, generating 240 tokens per second compared with 115 tokens per second.
Which model is cheaper to run?
Llama 3.2 3B 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?
Llama 3.2 3B has lower latency, with a time to first token of 0.16s compared with 0.85s.
Which model has a larger context window?
Gemini 4 Argon has a larger context window, supporting 1M tokens compared with 128K tokens.
Veredicto
Choose Llama 3.2 3B for ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas rag empresariales y automatización mediante api., or choose Gemini 4 Argon for ideal para comprensión multimodal nativa de texto e imagen y análisis visual de documentos, sistemas rag empresariales y automatización mediante api..
Preguntas frecuentes
Which is cheaper: Llama 3.2 3B or Gemini 4 Argon?+
Llama 3.2 3B costs $0/1M input and $0/1M output tokens, while Gemini 4 Argon costs $2/1M input and $10/1M output tokens.
Which model has a larger context window: Llama 3.2 3B or Gemini 4 Argon?+
Llama 3.2 3B supports 128K tokens whereas Gemini 4 Argon supports 1M 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: Meta & Google Official Documentation