Head-to-Head Model Comparison

Gemini 3.8 Flash vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

GetInfoUs araştırma ekibi tarafından incelendiSon inceleme: October 2026Yayın ilkeleri

Paylaş

İstediğiniz Modelleri Seçin ve Karşılaştırın

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Gemini 3.8 FlashProfile

Llama 5Profile

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Popular Matchups:

Google

Gemini 3.8 Flash

Ultra Hızlı Düşük Gecikme

Google tarafından geliştirilen Gemini 3.8 Flash; ultra düşük gecikmeli gerçek zamanlı sohbet ve yüksek hacimli yönlendirme için tasarlanmış olup 1.0M bağlam penceresi, 175 token/sn hız, 87/100 zeka indeksi ve 1M token başına $0.75/$3.75 fiy

Intelligence Index87 / 100

Input Token:

$0.75 / 1M

Output Token:

$3.75 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

74.8%

Llama 5

Açık Ağırlıklı Temel Model

Meta tarafından geliştirilen Llama 5; özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği için tasarlanmış olup 5M bağlam penceresi, 65 token/sn hız, 94/100 zeka indeksi ve ücretsiz açık ağırlık erişimi s

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Bir Bakışta Temel Farklar

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%).

Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →

Feature	Gemini 3.8 Flash	Llama 5
Intelligence Index Composite reasoning & knowledge score (0-100)	87	94
Provider	Google	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

Hangi Alanda Kim Kazanıyor?

At-a-glance decision framework

Coding

Gemini 3.8 Flash

+0.8% SWE-bench lead

Cost

Llama 5

1.0x cheaper monthly runtime

Speed

Gemini 3.8 Flash

+169% faster generation

Context

Llama 5

5M tokens window

Doğrulanmış Benchmark Puanları

Official evaluation metrics verified by independent third-party research labs.

Gemini 3.8 FlashLlama 5

SWE-bench Verified (Autonomous Coding)

Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.

74.8%vs74%

GPQA Diamond (Doctorate-Level Science & Math)

Challenging questions created by domain experts to test complex PhD-level reasoning.

84%vs84%

MMLU-Pro (Multi-task Language Understanding)

Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.

88.8%vs87%

LMSYS Chatbot Arena Elo

Crowdsourced human preference evaluation based on blind, head-to-head model battles.

1480ptsvs1485pts

LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

N/Avs68%

AIME (Advanced Math Competition)

American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.

89.5%vs88%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

175 tok/s vs65 tok/s

Gecikme Süresi ve Yanıt Hızı

Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.

Time to First Token (TTFT)Lower is better

Gemini 3.8 Flash0.39s

Llama 50.85s

Est. End-to-End Response (500 tokens)TTFT + generation time

Gemini 3.8 Flash3.25s

Llama 58.54s

Gerçek Zamanlı Token Maliyet Simülatörü

Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.

Daily Input Tokens100K / day

10K500K1M

Daily Output Tokens30K / day

5K150K300K

Prompt Cache Hit Ratio50% cached

0% (Cold)50%90% (Warm)

Gemini 3.8 Flash Monthly API Bill

$4.78 / month

$0.75/1M in · $3.75/1M out

Llama 5 Monthly API Bill

$0.00 / month

$0/1M in · $0/1M out

Llama 5 saves approximately $4.78 / month (100%) under this workload.1.0x Savings

Gemini 3.8 Flash · Strengths & Key Advantages

1.0M bağlam penceresi ve yanıt başına maksimum 66K çıktı tokenı kapasitesi (SWE-bench Verified 74.8% · GPQA Diamond 84%)
Sadece 390 ms ilk token gecikmesi (TTFT) ile saniyede 175 token yüksek üretim hızı
İstem önbellekleme (Prompt Caching), araç çağırma ve yapılandırılmış JSON çıktısını destekleyen rekabetçi API ekonomisi (1M token başına $0.75/$3.75 fiyatlandırma)

Considerations & Trade-offs

Kapalı ağırlıklı bulut modelidir; çevrimdışı yerel sunuculara indirilerek çalıştırılamaz
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

Llama 5 · Strengths & Key Advantages

5M bağlam penceresi ve yanıt başına maksimum 128K çıktı tokenı kapasitesi (SWE-bench Verified 74% · GPQA Diamond 84%)
Sadece 850 ms ilk token gecikmesi (TTFT) ile saniyede 65 token yüksek üretim hızı
vLLM, SGLang veya Ollama üzerinden yerel GPU sunucularında barındırılabilen açık ağırlıklı mimari (600B+ total (~40-80B ))

Considerations & Trade-offs

Tam hassasiyette yerel GPU çıkarımı için yeterli VRAM kapasitesine sahip donanım altyapısı gerektirir
Çok karmaşık akademik ve bilimsel çıkarımlarda üst düzey ağır akıl yürütme modelleri daha derin sonuçlar verebilir

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

Teknik Özellikler

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: Hızlı Yanıtlar (SSS)

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.

Sonuç

Hem Gemini 3.8 Flash (ultra düşük gecikmeli gerçek zamanlı sohbet ve yüksek hacimli yönlendirme, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) hem de Llama 5 (özel sunucuda kendi kendine barındırma (self-hosting), ince ayar ve tam veri egemenliği, kurumsal RAG mimarileri ve ölçeklenebilir üretim API entegrasyonları için idealdir.) güçlü üretim seçenekleridir: Llama 5 karmaşık akıl yürütme ve kodlama testlerinde öne çıkarken, Gemini 3.8 Flash daha yüksek üretim hızı sağlar; Llama 5 ise yüksek hacimli API kullanımlarında daha ekonomiktir.

Sıkça Sorulan Sorular (SSS)

Kodlama ve akıl yürütme için Gemini 3.8 Flash mi yoksa Llama 5 mi daha iyi?+

Bileşik zeka endeksi ve yazılım testlerinde Llama 5 önde yer alıyor. Detaylı karşılaştırma için yukarıdaki SWE-bench ve GPQA tablolarına göz atabilirsiniz.

Gemini 3.8 Flash ve Llama 5 API fiyatları nasıl karşılaştırılır?+

Gemini 3.8 Flash 1M giriş/çıkış tokeni başına $0.75 / $3.75 ücretlendirilirken, Llama 5 $0.00 / $0.00 seviyesindedir. Karma maliyette Llama 5 daha avantajlıdır.

Hangi modelin bağlam penceresi daha geniş ve hızı daha yüksek?+

Gemini 3.8 Flash 1.05M token bağlam penceresi sunarken Llama 5 5M token kapasiteye sahiptir. Saniyedeki token üretim hızında ise Gemini 3.8 Flash öndedir.

AI Terminology & Guide

Explore Glossary →

Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.

Tokens & Pricing Context Window AI Benchmarks Reasoning Models Tool Calling Model Context Protocol (MCP)

Gemini 3.8 Flash vs Llama 5

İstediğiniz Modelleri Seçin ve Karşılaştırın

Gemini 3.8 Flash

Llama 5

Hangi Alanda Kim Kazanıyor?

Doğrulanmış Benchmark Puanları

Gecikme Süresi ve Yanıt Hızı

Gerçek Zamanlı Token Maliyet Simülatörü

Gemini 3.8 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Teknik Özellikler

Gemini 3.8 Flash vs Llama 5: Hızlı Yanıtlar (SSS)

Sonuç

Sıkça Sorulan Sorular (SSS)

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