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Gemini 1.5 Flash-8B vs Ministral 8B

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien
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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
Gemini 1.5 Flash-8B vs Ministral 8B: Benchmarks, Pricing & Speed Comparison

Gemini 1.5 Flash-8B von Google und Ministral 8B von Mistral AI gehören zu den führenden KI-Modellen. Gemini 1.5 Flash-8B bietet ein Kontextfenster von 1.05M Tokens bei API-Preisen von $0.04/$0.15 pro 1M Tokens, während Ministral 8B 128K Tokens Kontext zu $0.10/$0.10 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Gemini 1.5 Flash-8B

Nativ Multimodale KI

Gemini 1.5 Flash-8B von Google ist ein KI-Modell für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse mit 1.0M Kontextfenster, 160 Token/s Durchsatz, einem Intelligenz-Index von 37/100 und $0.0375/$0.15 pro

Intelligence Index37 / 100
Input Token:

$0.04 / 1M

Output Token:

$0.15 / 1M

Context Window:

1.05M tokens

SWE-bench Verified:

24.5%

Mistral AI

Ministral 8B

Kompaktes Effizienz-Modell

Ministral 8B von Mistral AI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 128K Kontextfenster, 110 Token/s Durchsatz, einem Intelligenz-Index von 38/100 und $0.1/$0.1 pro 1M Token

Intelligence Index38 / 100
Input Token:

$0.10 / 1M

Output Token:

$0.10 / 1M

Context Window:

128K tokens

SWE-bench Verified:

32.5%

Wichtigste Unterschiede auf einen Blick

  • Gemini 1.5 Flash-8B is 1.5x cheaper for API usage (input + output tokens combined).
  • Gemini 1.5 Flash-8B has a larger context window (1M vs 128K tokens).
  • Gemini 1.5 Flash-8B is faster at 160 tok/s vs 110 tok/s.
  • Ministral 8B leads on SWE-bench coding (32.5% vs 24.5%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGemini 1.5 Flash-8BMinistral 8B
Intelligence Index
Composite reasoning & knowledge score (0-100)
3738
Provider
GoogleMistral AI
Input Price
Cost per million input tokens
$0.04 / 1M$0.10 / 1M
Output Price
Cost per million output tokens
$0.15 / 1M$0.10 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.03 / 1M$0.07 / 1M
Context Window
Maximum input + output tokens
1.05M tokens~1,573 A4 pages of text128K tokens~192 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
160 tok/s110 tok/s
SWE-bench Verified
Real-world software engineering
24.5%32.5%
GPQA Diamond
Graduate-level reasoning
38.2%41.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.01 / 1M$0.05 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Ministral 8B
+8.0% SWE-bench lead
Cost
Gemini 1.5 Flash-8B
1.5x cheaper monthly runtime
Speed
Gemini 1.5 Flash-8B
+45% faster generation
Context
Gemini 1.5 Flash-8B
1.05M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

Gemini 1.5 Flash-8BMinistral 8B
SWE-bench Verified (Autonomous Coding)

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

24.5%vs32.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

38.2%vs41.5%
MMLU-Pro (Multi-task Language Understanding)

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

58.6%vs60.1%
LMSYS Chatbot Arena Elo

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

1220ptsvs1235pts
Humanity's Last Exam (Frontier Expert Knowledge)

Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.

3.5%vs4.2%
Terminal-Bench (Agentic Coding & Terminal Use)

Autonomous multi-step tasks executed inside a real command-line terminal environment.

16.5%vs21%
SciCode (Scientific Computing)

Research-grade coding problems drawn from real scientific and numerical workloads.

17.8%vs21.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

31.4%vs35.8%
AIME (Advanced Math Competition)

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

34.5%vs41.2%
Long-Context Reasoning

Accuracy when reasoning over very large inputs that fill the model context window.

69.2%vs65.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

160 tok/s vs110 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 1.5 Flash-8B0.25s
Ministral 8B0.33s
Est. End-to-End Response (500 tokens)TTFT + generation time
Gemini 1.5 Flash-8B3.38s
Ministral 8B4.88s

Interaktiver Token-Kosten-Simulator

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

100K / day
10K500K1M
30K / day
5K150K300K
50% cached
0% (Cold)50%90% (Warm)
Gemini 1.5 Flash-8B Monthly API Bill
$0.21 / month
$0.0375/1M in · $0.15/1M out
Ministral 8B Monthly API Bill
$0.32 / month
$0.1/1M in · $0.1/1M out
Gemini 1.5 Flash-8B saves approximately $0.11 / month (35%) under this workload.1.5x Savings

Gemini 1.5 Flash-8B · Strengths & Key Advantages

  • 1.0M Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 24.5% · GPQA Diamond 38.2%)
  • Hohe Generierungsgeschwindigkeit von 160 Token/Sekunde bei nur 250 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.0375/$0.15 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

Ministral 8B · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 32.5% · GPQA Diamond 41.5%)
  • Hohe Generierungsgeschwindigkeit von 110 Token/Sekunde bei nur 330 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.1/$0.1 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

Token Use & Task Economics

Estimated cost and token consumption to complete one representative agentic task.

MetricGemini 1.5 Flash-8BMinistral 8B
Cost per Task
Estimated USD to finish one task
$0.04$0.08
Output Tokens / Task
Tokens generated per task
18K21K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGemini 1.5 Flash-8BMinistral 8B
Official API Model ID (SDK)models/gemini-1-5-flash-8bministral-8b-latest
API ProtocolGoogle Gemini API (generateContent) + OpenAI-Compatible SDKMistral AI REST API (OpenAI-Compatible)
Release Date2024-102024-10
Knowledge CutoffMay 2024October 2024
ArchitectureHigh-Throughput Multimodal TransformerCompact Dense Transformer with Interleaved Sliding Window
Parameters8B8B
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Standard rate up to 200K tokens; 2x input/output tier above 200K tokensFlat rate ($0.1/1M in) across full 128K window
Prompt Cache Policy & TTLImplicit + Explicit Context Caching at $0.01/1M with configurable TTLCached input at $0.05/1M tokens
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON SchemaNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxBuilt-in Google Search Grounding + Managed Python Code Execution SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox tools
Official Fine-Tuning SupportVertex AI Supervised Adapter Tuning & Context DistillationAPI Prompt Engineering & RAG Distillation
Multimodal Token BillingText 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)
Quantization & Min GPU VRAMCloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API)
Safety & Frontier ClassificationGoogle DeepMind Frontier Safety Framework VerifiedMistral AI Production Safety & Alignment Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise TierSOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalitiestext, vision, audio, videotext
Output Modalitiestexttext

Gemini 1.5 Flash-8B vs Ministral 8B: Schnelle Antworten (FAQ)

Which is more intelligent, Gemini 1.5 Flash-8B or Ministral 8B?

Ministral 8B is more intelligent. It scores 38 compared with 37 on the composite Intelligence Index.

Which model is faster?

Gemini 1.5 Flash-8B is faster, generating 160 tokens per second compared with 110 tokens per second.

Which model is cheaper to run?

Gemini 1.5 Flash-8B is cheaper at $0.03 per 1M tokens blended, compared with $0.07 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Gemini 1.5 Flash-8B has lower latency, with a time to first token of 0.25s compared with 0.33s.

Which model has a larger context window?

Gemini 1.5 Flash-8B has a larger context window, supporting 1.05M tokens compared with 128K tokens.

Fazit

Sowohl Gemini 1.5 Flash-8B (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Ministral 8B (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Ministral 8B führt bei komplexem Reasoning und Coding, Gemini 1.5 Flash-8B liefert den höheren Token-Durchsatz und Gemini 1.5 Flash-8B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Gemini 1.5 Flash-8B oder Ministral 8B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Ministral 8B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Gemini 1.5 Flash-8B und Ministral 8B?+

Gemini 1.5 Flash-8B kostet $0.04 / $0.15 pro 1M Input-/Output-Tokens, Ministral 8B liegt bei $0.10 / $0.10. Beim gewichteten Gesamtpreis ist Gemini 1.5 Flash-8B günstiger.

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

Gemini 1.5 Flash-8B bietet 1.05M Tokens Kontext gegenüber 128K Tokens bei Ministral 8B. Bei der Generierungsgeschwindigkeit liegt Gemini 1.5 Flash-8B 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 & PricingContext WindowAI BenchmarksReasoning ModelsTool CallingModel Context Protocol (MCP)

Related reading

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.

Offizielle Quellen: Offizielle API-Dokumentation von Google und Mistral AI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).