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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Live comparison: no in-depth editorial review has been published for this pair yet.
Gemini 1.5 Flash
Contexte 1M pour quelques centimesélevé-vitesse, coût-optimized bourreau de travail fournissant un 1M token fenêtre de contexte à ultra-faible tarification, conçu pour élevé-frequency faible-latence production applications.
$0.07 / 1M
$0.30 / 1M
1.05M tokens
20%
Llama 5
Auto-amélioration récursivede Meta phare ouvert modèle intégrant natif agent training, recursive self-improvement, et 5M token contexte.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
Différences Clés en un Coup d’Œil
- 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%).
| Caractéristique | 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 |
Qui gagne où ?
At-a-glance decision frameworkScores de Benchmarks Vérifiés
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.
Latence et Temps de Réponse
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulateur Interactif de Coût de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 1.5 Flash · Strengths & Key Advantages
- Phenomenal prix efficacité ($0.075 par 1M entrée tokens)
- Blazing rapide débit (180 tokens/sec) avec complet 1M contexte support
- Comprehensive multimodal support including vidéo et audio ingestion
Considerations & Trade-offs
- codage et mathématique raisonnement scores inférieur que Gemini 1.5 Pro ou Gemini 2.0
- Occasional hallucinations sur deeply nuanced logique queries
Llama 5 · Strengths & Key Advantages
- 5 millions token contexte avec natif multi-audio/vidéo compréhension
- Boucle d'entraînement à auto-amélioration récursive
- Completely à poids ouverts avec d'entreprise commercial license
Considerations & Trade-offs
- complexe MoE hébergement requirements
- Les modifications du tokeniseur 4B nécessitent la mise à jour des environnements d'ajustement existants
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 |
Spécifications Techniques
Architecture, release timeline, capabilities, and modalities.
| Caractéristique | 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: Réponses Rapides (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.
Verdict
Choose Gemini 1.5 Flash for élevé-volume production apis, client support bots, document batch traitement, et en direct transcription, or choose Llama 5 for autonome open source multi-agent systems, profond logiciel refactorisation, et de pointe recherche.
Questions fréquentes
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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Les scores de benchmarks, tarifs par token et limites évoluent rapidement. Vérifiez toujours les spécifications actuelles sur la documentation officielle du fournisseur.
Sources: Google & Meta Official Documentation