Gemini 1.5 Flash vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 1.5 Flash by Google against Llama 5 by Meta across real-world coding, reasoning, token economics, and latency.
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Gemini 1.5 Flash
Inferencia UltrarrápidaGemini 1.5 Flash de Google es un modelo de IA diseñado para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, con ventana de contexto de 1.0M, velocidad de 180 tokens/s, índice de inteligencia de 38/100 y tarifa de $
$0.07 / 1M
$0.30 / 1M
1.05M tokens
20%
Llama 5
Modelo Base de Pesos AbiertosLlama 5 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 5M, velocidad de 65 tokens/s, índice de inteligencia de 94/100 y tarifa de ac
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Diferencias Clave de un Vistazo
- Llama 5 is 1.0x cheaper for API usage (input + output tokens combined).
- Llama 5 has a larger context window (5M vs 1M tokens).
- Gemini 1.5 Flash is faster at 180 tok/s vs 65 tok/s.
- Llama 5 leads on SWE-bench coding (74.0% vs 20.0%).
| Característica | Gemini 1.5 Flash | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 38 | 94 |
Provider | Meta | |
Input Price Cost per million input tokens | $0.07 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.30 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.06 / 1M | $0.00 / 1M |
Context Window Maximum input + output tokens | 1.05M tokens~1,573 A4 pages of text | 5M tokens~7,500 A4 pages of text |
Max Output Tokens Per single request | 8K | 128K |
Throughput Speed Tokens generated per second | 180 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 20% | 74% |
GPQA Diamond Graduate-level reasoning | 42.1% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.02 / 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.
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 1.5 Flash · Strengths & Key Advantages
- Ventana de contexto de 1.0M con hasta 8K tokens de salida máxima (SWE-bench Verified 20% · GPQA Diamond 42.1%)
- Rendimiento ágil de 180 tokens/segundo con latencia inicial (TTFT) de solo 320 ms
- Economía de API competitiva ($0.075/$0.3 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 demostraciones científicas extremas, los modelos de razonamiento pesado de gama alta ofrecen mayor profundidad
Llama 5 · Strengths & Key Advantages
- Ventana de contexto de 5M con hasta 128K tokens de salida máxima (SWE-bench Verified 74% · GPQA Diamond 84%)
- Rendimiento ágil de 65 tokens/segundo con latencia inicial (TTFT) de solo 850 ms
- Pesos abiertos (600B+ total (~40-80B )) 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 1.5 Flash | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.20 | $0.00 |
Output Tokens / Task Tokens generated per task | 24K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 20K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Gemini 1.5 Flash | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-1-5-flash | meta-llama/Llama-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2024-05 | 2026-04 |
| Knowledge Cutoff | May 2024 | January 2026 |
| Architecture | Distilled Sparse MoE Multimodal | Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) |
| Parameters | Confidential | 600B+ total (~40-80B active) |
| 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 $0.01875/1M 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) · 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 | 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, video, audio |
| Output Modalities | text | text |
Gemini 1.5 Flash vs Llama 5: Respuestas Rápidas (FAQ)
Which is more intelligent, Gemini 1.5 Flash or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 38 on the composite Intelligence Index.
Which model is faster?
Gemini 1.5 Flash is faster, generating 180 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 $0.06 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 1.5 Flash has lower latency, with a time to first token of 0.32s compared with 0.85s.
Which model has a larger context window?
Llama 5 has a larger context window, supporting 5M tokens compared with 1.05M tokens.
Veredicto
Choose Gemini 1.5 Flash for ideal para interacción en tiempo real, baja latencia y enrutamiento de alto volumen, sistemas rag empresariales y automatización mediante api., or choose Llama 5 for ideal para autoalojamiento en servidores propios, ajuste fino (fine-tuning) y soberanía de datos, sistemas rag empresariales y automatización mediante api..
Preguntas frecuentes
Which is cheaper: Gemini 1.5 Flash or Llama 5?+
Gemini 1.5 Flash costs $0.075/1M input and $0.3/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Gemini 1.5 Flash or Llama 5?+
Gemini 1.5 Flash supports 1.0M 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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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