Gemini 3.8 Flash vs Llama 5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 3.8 Flash by Google and Llama 5 by Meta represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Llama 5 holds a notable pricing advantage for token ingestion. On output generation, Llama 5 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Llama 5 provides an expanded memory horizon with 5,000K tokens. In latency-sensitive production environments, Gemini 3.8 Flash leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
بدّل وقارن بين أي نموذجين
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Gemini 3.8 Flash
نموذج استدلال عميقنموذج Gemini 3.8 Flash المقدم من Google يوفر نافذة سياق تصل إلى 1.0M رمز، بتكلفة $0.75 لكل مليون رمز إدخال و$3.75 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.75 / 1M
$3.75 / 1M
1.05M tokens
74.8%
Llama 5
نموذج مفتوح الأوزاننموذج Llama 5 المقدم من Meta يوفر نافذة سياق تصل إلى 5M رمز، بتكلفة $0 لكل مليون رمز إدخال و$0 لكل مليون رمز إخراج، ومصمم للبرمجة والاستدلال وتطبيقات الأعمال.
$0.00 / 1M
$0.00 / 1M
5M tokens
74%
أهم الفروقات في لمحة
- 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 3.8 Flash is faster at 175 tok/s vs 65 tok/s.
- Gemini 3.8 Flash leads on SWE-bench coding (74.8% vs 74.0%).
| Feature | Gemini 3.8 Flash | Llama 5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 87 | 94 |
Provider | Meta | |
Input Price Cost per million input tokens | $0.75 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $3.75 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.66 / 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 | 66K | 128K |
Throughput Speed Tokens generated per second | 175 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 74.8% | 74% |
GPQA Diamond Graduate-level reasoning | 84% | 84% |
Cached Input Price Prompt caching discount per 1M tokens | $0.19 / 1M | $0.00 / 1M |
من يتفوق في كل مجال؟
At-a-glance decision frameworkنتائج الاختبارات المعيارية الموثقة
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.
زمن الاستجابة والسرعة
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
محاكي تكلفة الرموز التفاعلي
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Gemini 3.8 Flash · Strengths & Key Advantages
- بنية Late-2026 optimized sparse multimodal transformer متطورة من Google مع سعة سياق تبلغ 1.0M رمز
- كفاءة عالية في التكلفة ($0.75/1M إدخال و$3.75/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Llama 5 · Strengths & Key Advantages
- بنية Mixture-of-Experts with recursive self-improvement (~600B total, 40-80B active) متطورة من Meta مع سعة سياق تبلغ 5M رمز
- كفاءة عالية في التكلفة ($0/1M إدخال و$0/1M إخراج) وسرعة استجابة ممتازة
- أداء قوي في اختبارات البرمجة، الاستدلال المنطقي، ودعم اللغات المتعددة
Considerations & Trade-offs
- يتطلب إدارة دقيقة لاستهلاك الرموز عند استخدام نافذة السياق الكاملة
- قد يحتاج إلى ضبط دقيق للتعليمات (Prompt Engineering) في المهام التخصصية المعقدة
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3.8 Flash | Llama 5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.20 | $0.00 |
Output Tokens / Task Tokens generated per task | 44K | 40K |
Reasoning Tokens / Task Thinking tokens burned per task | 21K | 20K |
المواصفات التقنية
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3.8 Flash | Llama 5 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-8-flash | meta-llama/Llama-5 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI-Compatible REST / vLLM / SGLang / HuggingFace TGI |
| Release Date | 2026-09 | 2026-04 |
| Knowledge Cutoff | June 2026 | January 2026 |
| Architecture | Late-2026 optimized sparse multimodal transformer | 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 $0.1875/1M 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 3.8 Flash vs Llama 5: إجابات سريعة (FAQ)
Which is more intelligent, Gemini 3.8 Flash or Llama 5?
Llama 5 is more intelligent. It scores 94 compared with 87 on the composite Intelligence Index.
Which model is faster?
Gemini 3.8 Flash is faster, generating 175 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 $0.66 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Gemini 3.8 Flash has lower latency, with a time to first token of 0.39s 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.
الحكم النهائي
Verdict & Model Selection Recommendation: Choose Gemini 3.8 Flash if your priority is Enterprise document synthesis, general coding, and high-quality voice/vision workflows. Its performance in demanding tasks justifies its operational token cost. Choose Llama 5 if your workflow prioritizes Autonomous open-source multi-agent systems, deep software refactoring, and frontier research. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
الأسئلة الشائعة (FAQ)
How does Gemini 3.8 Flash compare to Llama 5 in API token pricing and cost per token?+
Gemini 3.8 Flash costs $0.75 per 1M input tokens and $3.75 per 1M output tokens, whereas Llama 5 costs $0.00 input and $0.00 output. Overall, Llama 5 is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: Gemini 3.8 Flash or Llama 5?+
Gemini 3.8 Flash supports a context window of 1,049K tokens (~786k words), while Llama 5 supports 5,000K tokens (~3,750k words). Therefore, Llama 5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini 3.8 Flash or Llama 5?+
Gemini 3.8 Flash scores 74.8% on SWE-bench Verified while Llama 5 scores 74%. For autonomous software engineering and multi-file debugging, Gemini 3.8 Flash holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Gemini 3.8 Flash and Llama 5?+
Gemini 3.8 Flash features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Llama 5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini 3.8 Flash and Llama 5?+
Gemini 3.8 Flash generates approximately 175 tokens per second, while Llama 5 delivers 65 tokens per second. In latency-sensitive production environments, Gemini 3.8 Flash provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 3.8 Flash and Llama 5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Llama 5 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3.8 Flash. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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- GPT-OSS 120B vs Llama 5: Benchmarks, Pricing & Speed Comparisonمقارنة
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- Llama 5 vs DeepSeek V4 Pro: Benchmarks, Pricing & Speed Comparisonمقارنة
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Benchmark scores, token prices, and rate limits evolve rapidly as AI labs release model updates. Always verify current specifications and pricing on the official provider documentation.
المصادر الرسمية: GetInfoUs Independent AI Benchmarks & Official API Specs