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Head-to-Head Model Comparison

Ministral 3B vs Gemini 1.5 Flash-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
Ministral 3B vs Gemini 1.5 Flash-8B: Benchmarks, Pricing & Speed Comparison

Ministral 3B von Mistral AI und Gemini 1.5 Flash-8B von Google gehören zu den führenden KI-Modellen. Ministral 3B bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.04/$0.04 pro 1M Tokens, während Gemini 1.5 Flash-8B 1.05M Tokens Kontext zu $0.04/$0.15 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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Mistral AI

Ministral 3B

Kompaktes Effizienz-Modell

Ministral 3B von Mistral AI ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 128K Kontextfenster, 180 Token/s Durchsatz, einem Intelligenz-Index von 33/100 und $0.04/$0.04 pro 1M Tok

Intelligence Index33 / 100
Input Token:

$0.04 / 1M

Output Token:

$0.04 / 1M

Context Window:

128K tokens

SWE-bench Verified:

22.1%

Google

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%

Wichtigste Unterschiede auf einen Blick

  • Ministral 3B is 1.6x cheaper for API usage (input + output tokens combined).
  • Gemini 1.5 Flash-8B has a larger context window (1M vs 128K tokens).
  • Ministral 3B is faster at 180 tok/s vs 160 tok/s.
  • Gemini 1.5 Flash-8B leads on SWE-bench coding (24.5% vs 22.1%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureMinistral 3BGemini 1.5 Flash-8B
Intelligence Index
Composite reasoning & knowledge score (0-100)
3337
Provider
Mistral AIGoogle
Input Price
Cost per million input tokens
$0.04 / 1M$0.04 / 1M
Output Price
Cost per million output tokens
$0.04 / 1M$0.15 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.03 / 1M$0.03 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text1.05M tokens~1,573 A4 pages of text
Max Output Tokens
Per single request
8K8K
Throughput Speed
Tokens generated per second
180 tok/s160 tok/s
SWE-bench Verified
Real-world software engineering
22.1%24.5%
GPQA Diamond
Graduate-level reasoning
32.5%38.2%
Cached Input Price
Prompt caching discount per 1M tokens
$0.02 / 1M$0.01 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
Gemini 1.5 Flash-8B
+2.4% SWE-bench lead
Cost
Ministral 3B
1.6x cheaper monthly runtime
Speed
Ministral 3B
+13% 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.

Ministral 3BGemini 1.5 Flash-8B
SWE-bench Verified (Autonomous Coding)

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

22.1%vs24.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

32.5%vs38.2%
MMLU-Pro (Multi-task Language Understanding)

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

49.8%vs58.6%
LMSYS Chatbot Arena Elo

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

1180ptsvs1220pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

2.4%vs3.5%
Terminal-Bench (Agentic Coding & Terminal Use)

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

14.8%vs16.5%
SciCode (Scientific Computing)

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

15.2%vs17.8%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

24.5%vs31.4%
AIME (Advanced Math Competition)

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

28.5%vs34.5%
Long-Context Reasoning

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

60.5%vs69.2%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

180 tok/s vs160 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
Ministral 3B0.21s
Gemini 1.5 Flash-8B0.25s
Est. End-to-End Response (500 tokens)TTFT + generation time
Ministral 3B2.99s
Gemini 1.5 Flash-8B3.38s

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)
Ministral 3B Monthly API Bill
$0.13 / month
$0.04/1M in · $0.04/1M out
Gemini 1.5 Flash-8B Monthly API Bill
$0.21 / month
$0.0375/1M in · $0.15/1M out
Ministral 3B saves approximately $0.08 / month (39%) under this workload.1.6x Savings

Ministral 3B · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 22.1% · GPQA Diamond 32.5%)
  • Hohe Generierungsgeschwindigkeit von 180 Token/Sekunde bei nur 210 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($0.04/$0.04 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

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

Token Use & Task Economics

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

MetricMinistral 3BGemini 1.5 Flash-8B
Cost per Task
Estimated USD to finish one task
$0.03$0.04
Output Tokens / Task
Tokens generated per task
15K18K
Reasoning Tokens / Task
Thinking tokens burned per task
0K0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureMinistral 3BGemini 1.5 Flash-8B
Official API Model ID (SDK)ministral-3b-latestmodels/gemini-1-5-flash-8b
API ProtocolMistral AI REST API (OpenAI-Compatible)Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date2024-102024-10
Knowledge CutoffOctober 2024May 2024
ArchitectureUltra-Compact Dense TransformerHigh-Throughput Multimodal Transformer
Parameters3B8B
Enterprise Rate Limits (RPM / TPM)5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)Flat rate ($0.04/1M in) across full 128K windowStandard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTLCached input at $0.02/1M tokensImplicit + Explicit Context Caching at $0.01/1M with configurable TTL
Computer Use / GUI AgentAPI / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
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 & SandboxSupported via external Tavily/Exa Search & E2B/Docker Sandbox toolsBuilt-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning SupportAPI Prompt Engineering & RAG DistillationVertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token BillingText BPE (~4 chars/token)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 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 ClassificationMistral AI Production Safety & Alignment VerifiedGoogle DeepMind Frontier Safety Framework 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 Modalitiestexttext, vision, audio, video
Output Modalitiestexttext

Ministral 3B vs Gemini 1.5 Flash-8B: Schnelle Antworten (FAQ)

Which is more intelligent, Ministral 3B or Gemini 1.5 Flash-8B?

Gemini 1.5 Flash-8B is more intelligent. It scores 37 compared with 33 on the composite Intelligence Index.

Which model is faster?

Ministral 3B is faster, generating 180 tokens per second compared with 160 tokens per second.

Which model has lower latency?

Ministral 3B has lower latency, with a time to first token of 0.21s compared with 0.25s.

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 Ministral 3B (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 1.5 Flash-8B (Optimal für nativ multimodale Verarbeitung von Text und Bild sowie visuelle Dokumentenanalyse, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Gemini 1.5 Flash-8B führt bei komplexem Reasoning und Coding, Ministral 3B liefert den höheren Token-Durchsatz und Ministral 3B ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Ministral 3B oder Gemini 1.5 Flash-8B?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt Gemini 1.5 Flash-8B vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von Ministral 3B und Gemini 1.5 Flash-8B?+

Ministral 3B kostet $0.04 / $0.04 pro 1M Input-/Output-Tokens, Gemini 1.5 Flash-8B liegt bei $0.04 / $0.15. Beim gewichteten Gesamtpreis ist Ministral 3B günstiger.

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

Ministral 3B bietet 128K Tokens Kontext gegenüber 1.05M Tokens bei Gemini 1.5 Flash-8B. Bei der Generierungsgeschwindigkeit liegt Ministral 3B 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 Mistral AI und Google, Systemkarten und unabhängige Benchmark-Auswertungen (2026).