Head-to-Head Model Comparison

Gemini 3.8 Flash vs Llama 5

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

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Gemini 3.8 FlashProfile

Llama 5Profile

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Popular Matchups:

Google

Gemini 3.8 Flash

Ultraschnelle Echtzeit-Inferenz

Gemini 3.8 Flash von Google ist ein KI-Modell für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing mit 1.0M Kontextfenster, 175 Token/s Durchsatz, einem Intelligenz-Index von 87/100 und $0.75/$3.75 pro 1M Token.

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

Open-Weights-Basismodell

Llama 5 von Meta ist ein KI-Modell für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität mit 5M Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 94/100 und kostenlosem Open-Weights-Zugang.

Intelligence Index94 / 100

Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

5M tokens

SWE-bench Verified:

74%

Wichtigste Unterschiede auf einen Blick

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

Wer gewinnt in welcher Disziplin?

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

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Latenz & Antwortzeit

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

Interaktiver Token-Kosten-Simulator

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 Kontextfenster und bis zu 66K maximale Output-Token (SWE-bench Verified 74.8% · GPQA Diamond 84%)
Hohe Generierungsgeschwindigkeit von 175 Token/Sekunde bei nur 390 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($0.75/$3.75 pro 1M Token) inklusive Prompt-Caching, Tool-Calling und strikter JSON-Ausgabe

Considerations & Trade-offs

Proprietäres Cloud-Modell ohne Möglichkeit zum lokalen Download der Modellgewichte
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

Llama 5 · Strengths & Key Advantages

5M Kontextfenster und bis zu 128K maximale Output-Token (SWE-bench Verified 74% · GPQA Diamond 84%)
Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 850 ms Erstlatenz (TTFT)
Offene Modellgewichte (600B+ total (~40-80B )) für lokales Self-Hosting und Fine-Tuning über vLLM, SGLang oder Ollama

Considerations & Trade-offs

Erfordert dedizierte GPU-Hardware mit ausreichend VRAM für den lokalen Betrieb in voller Präzision
Bei extrem komplexen mehrstufigen Forschungsfragen bieten schwere Reasoning-Spezialmodelle noch tiefere Analysen

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

Technische Spezifikationen

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: Schnelle Antworten (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.

Fazit

Sowohl Gemini 3.8 Flash (Optimal für ultraschnelle Echtzeit-Antworten, niedrige Latenz und Hochdurchsatz-Routing, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Llama 5 (Optimal für Self-Hosting im eigenen Rechenzentrum, Fine-Tuning und volle Datensouveränität, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Llama 5 führt bei komplexem Reasoning und Coding, Gemini 3.8 Flash liefert den höheren Token-Durchsatz und Llama 5 ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 3.8 Flash oder Llama 5?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Llama 5 vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Gemini 3.8 Flash und Llama 5?+

Gemini 3.8 Flash kostet $0.75 / $3.75 pro 1M Input-/Output-Tokens, Llama 5 liegt bei $0.00 / $0.00. Beim gewichteten Gesamtpreis ist Llama 5 günstiger.

Welches Modell hat das größere Kontextfenster und die höhere Geschwindigkeit?+

Gemini 3.8 Flash bietet 1.05M Tokens Kontext gegenüber 5M Tokens bei Llama 5. Bei der Generierungsgeschwindigkeit liegt Gemini 3.8 Flash vorn.

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

Beliebige Modelle wechseln & vergleichen

Gemini 3.8 Flash

Llama 5

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Gemini 3.8 Flash · Strengths & Key Advantages

Considerations & Trade-offs

Llama 5 · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Gemini 3.8 Flash vs Llama 5: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

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