Gemini 2.5 Flash-Lite vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (4)
Compare Gemini 2.5 Flash-Lite by Google against Llama 5 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 2.5 Flash-Lite
Ágil 2.5ligero, ultra-economical Gemini modelo para alto-frequency multimodal requests.
$0.10 / 1M
$0.40 / 1M
1.05M tokens
N/A
Llama 5
Automejora recursivade Meta insignia abierto modelo con nativo agente training, recursive self-improvement, y 5M token contexto.
$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 2.5 Flash-Lite is faster at 220 tok/s vs 65 tok/s.
| Característica | Gemini 2.5 Flash-Lite | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 56 | 94 |
Provider | Meta | |
Input Price Cost per million input tokens | $0.10 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.08 / 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 | 33K | 128K |
Throughput Speed Tokens generated per second | 220 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | N/A | 74% |
GPQA Diamond Graduate-level reasoning | N/A | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.03 / 1M | $0.00 / 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.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
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 2.5 Flash-Lite · Strengths & Key Advantages
- económico precios con completo 1M contexto
- rápido transmisión rendimiento
- Basic uso de herramientas y razonamiento soporte
Considerations & Trade-offs
- limitado precisión en complejo multipaso instrucciones
- visión detail resolución menor que Pro
Llama 5 · Strengths & Key Advantages
- 5 millones token contexto con nativo multi-audio/video comprensión
- Bucle de entrenamiento de automejora recursiva
- Completely de pesos abiertos con empresarial comercial license
Considerations & Trade-offs
- complejo MoE servicio requirements
- Los cambios del tokenizador de 4B requieren actualizar entornos de ajuste heredados
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 2.5 Flash-Lite | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.35 | $0.00 |
Output Tokens / Task Tokens generated per task | 12K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 4K | 20K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | Gemini 2.5 Flash-Lite | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-2-5-flash-lite | meta-llama/Llama-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2025-06 | 2026-04 |
| Knowledge Cutoff | March 2025 | January 2026 |
| Architecture | Compact multimodal transformer with lightweight thinking | 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 $0.025/1M 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) | 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 | text, vision, video, audio |
| Output Modalities | text | text |
Gemini 2.5 Flash-Lite vs Llama 5: Respuestas Rápidas (FAQ)
Which is more intelligent, Gemini 2.5 Flash-Lite or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 56 on the composite Intelligence Index.
Which model is faster?
Gemini 2.5 Flash-Lite is faster, generating 220 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 $0.08 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 2.5 Flash-Lite 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 2.5 Flash-Lite for bulk documento extracción, cliente service enrutamiento, y alto-volumen parsing, or choose Llama 5 for autónomo de código abierto multi-agente systems, profundo software refactorización, y de frontera investigación.
Preguntas frecuentes
Which is cheaper: Gemini 2.5 Flash-Lite or Llama 5?+
Gemini 2.5 Flash-Lite costs $0.1/1M input and $0.4/1M output tokens, while Llama 5 costs $0/1M input and $0/1M output tokens.
Which model has a larger context window: Gemini 2.5 Flash-Lite or Llama 5?+
Gemini 2.5 Flash-Lite 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