Head-to-Head Model Comparison

Sonar vs Gemini 1.5 Pro

Benchmarks, API Token Pricing, Context Window & Speed Comparison

Geprüft vom GetInfoUs-ForschungsteamZuletzt geprüft: October 2026Redaktionelle Richtlinien

Beliebige Modelle wechseln & vergleichen

Select any AI models from our verified benchmark database to generate live side-by-side matrices.

SonarProfile

Gemini 1.5 ProProfile

Multi-Model Arena Stack:

5-Model Matrix Workbench

Popular Matchups:

Perplexity

Sonar

Kompaktes Effizienz-Modell

Sonar von Perplexity ist ein KI-Modell für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads mit 128K Kontextfenster, 130 Token/s Durchsatz, einem Intelligenz-Index von 73/100 und $1/$1 pro 1M Token.

Intelligence Index73 / 100

Input Token:

$1.00 / 1M

Output Token:

$1.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

N/A

Google

Gemini 1.5 Pro

Klassische Modellgeneration

Gemini 1.5 Pro von Google ist ein KI-Modell für zuverlässige Dialogsysteme, Textzusammenfassungen und klassische Enterprise-Aufgaben mit 2M Kontextfenster, 65 Token/s Durchsatz, einem Intelligenz-Index von 46/100 und $1.25/$5 pro 1M Token.

Intelligence Index46 / 100

Input Token:

$1.25 / 1M

Output Token:

$5.00 / 1M

Context Window:

2M tokens

SWE-bench Verified:

32.5%

Wichtigste Unterschiede auf einen Blick

Sonar is 2.7x cheaper for API usage (input + output tokens combined).
Gemini 1.5 Pro has a larger context window (2M vs 128K tokens).
Sonar is faster at 130 tok/s vs 65 tok/s.

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

Feature	Sonar	Gemini 1.5 Pro
Intelligence Index Composite reasoning & knowledge score (0-100)	73	46
Provider	Perplexity	Google
Input Price Cost per million input tokens	$1.00 / 1M	$1.25 / 1M
Output Price Cost per million output tokens	$1.00 / 1M	$5.00 / 1M
Blended Price Weighted 7:2:1 cache:input:output per 1M	$0.37 / 1M	$0.97 / 1M
Context Window Maximum input + output tokens	128K tokens~192 A4 pages of text	2M tokens~3,000 A4 pages of text
Max Output Tokens Per single request	4K	8K
Throughput Speed Tokens generated per second	130 tok/s	65 tok/s
SWE-bench Verified Real-world software engineering	N/A	32.5%
GPQA Diamond Graduate-level reasoning	N/A	58.5%
Cached Input Price Prompt caching discount per 1M tokens	$0.10 / 1M	$0.31 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework

Coding

Evaluations ongoing

Cost

Sonar

2.7x cheaper monthly runtime

Speed

Sonar

+100% faster generation

Context

Gemini 1.5 Pro

2M tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

SonarGemini 1.5 Pro

SWE-bench Verified (Autonomous Coding)

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

N/Avs32.5%

GPQA Diamond (Doctorate-Level Science & Math)

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

N/Avs58.5%

MMLU-Pro (Multi-task Language Understanding)

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

71%vs75.2%

LMSYS Chatbot Arena Elo

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

1245ptsvs1290pts

Long-Context Reasoning

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

N/Avs89.2%

Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

130 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

Sonar0.35s

Gemini 1.5 Pro0.80s

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

Sonar4.20s

Gemini 1.5 Pro8.49s

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)

Sonar Monthly API Bill

$2.55 / month

$1/1M in · $1/1M out

Gemini 1.5 Pro Monthly API Bill

$6.84 / month

$1.25/1M in · $5/1M out

Sonar saves approximately $4.29 / month (63%) under this workload.2.7x Savings

Sonar · Strengths & Key Advantages

128K Kontextfenster und bis zu 4K maximale Output-Token (MMLU-Pro 71% · Arena Elo 1245)
Hohe Generierungsgeschwindigkeit von 130 Token/Sekunde bei nur 350 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($1/$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

Gemini 1.5 Pro · Strengths & Key Advantages

2M Kontextfenster und bis zu 8K maximale Output-Token (SWE-bench Verified 32.5% · GPQA Diamond 58.5%)
Hohe Generierungsgeschwindigkeit von 65 Token/Sekunde bei nur 800 ms Erstlatenz (TTFT)
Effiziente API-Struktur ($1.25/$5 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.

Metric	Sonar	Gemini 1.5 Pro
Cost per Task Estimated USD to finish one task	$0.05	$0.20
Output Tokens / Task Tokens generated per task	10K	24K
Reasoning Tokens / Task Thinking tokens burned per task	0K	0K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

Feature	Sonar	Gemini 1.5 Pro
Official API Model ID (SDK)	sonar	models/gemini-1-5-pro
API Protocol	Perplexity REST API (OpenAI-Compatible)	Google Gemini API (generateContent) + OpenAI-Compatible SDK
Release Date	2024-01	2024-02
Knowledge Cutoff	Real-time (Web-grounded)	May 2024
Architecture	Web-Search Augmented Autoregressive Transformer	Sparse Mixture-of-Experts (MoE) Multimodal
Parameters	Confidential / Llama 3 Fine-Tune	Confidential
Enterprise Rate Limits (RPM / TPM)	5,000 – 15,000 RPM (Production Tier) · 800,000 – 2,000,000 TPM	10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM
Long-Context Tier (>128K/200K)	Flat rate ($1/1M in) across full 128K window	Standard rate up to 200K tokens; 2x input/output tier above 200K tokens
Prompt Cache Policy & TTL	Cached input at $0.1/1M tokens	Implicit + Explicit Context Caching at $0.31/1M with configurable TTL
Computer Use / GUI Agent	API / CLI Tool-Use & DOM Agent ready (no raw pixel desktop click loop)	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	Supported via external Tavily/Exa Search & E2B/Docker Sandbox tools	Built-in Google Search Grounding + Managed Python Code Execution Sandbox
Official Fine-Tuning Support	API Prompt Engineering & RAG Distillation	Vertex AI Supervised Adapter Tuning & Context Distillation
Multimodal Token Billing	Text 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 VRAM	Cloud-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 Classification	Perplexity Production Safety & Alignment Verified	Google DeepMind Frontier Safety Framework Verified
Enterprise Compliance (ZDR/HIPAA/SOC2)	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier	SOC2 Type II · GDPR · Zero Data Retention (ZDR) & HIPAA BAA Available on Enterprise Tier
Input Modalities	text	text, vision, audio, video
Output Modalities	text	text

Sonar vs Gemini 1.5 Pro: Schnelle Antworten (FAQ)

Which is more intelligent, Sonar or Gemini 1.5 Pro?

Sonar is more intelligent. It scores 73 compared with 46 on the composite Intelligence Index.

Which model is faster?

Sonar is faster, generating 130 tokens per second compared with 65 tokens per second.

Which model is cheaper to run?

Sonar is cheaper at $0.37 per 1M tokens blended, compared with $0.97 (7:2:1 cache:input:output ratio).

Which model has lower latency?

Sonar has lower latency, with a time to first token of 0.35s compared with 0.80s.

Which model has a larger context window?

Gemini 1.5 Pro has a larger context window, supporting 2M tokens compared with 128K tokens.

Fazit

Sowohl Sonar (Optimal für maximale Kosteneffizienz pro Token und ressourcenschonende Produktions-Workloads, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch Gemini 1.5 Pro (Optimal für zuverlässige Dialogsysteme, Textzusammenfassungen und klassische Enterprise-Aufgaben, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: Sonar führt bei komplexem Reasoning und Coding, Sonar liefert den höheren Token-Durchsatz und Sonar ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – Sonar oder Gemini 1.5 Pro?+

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

Wie unterscheiden sich die API-Preise von Sonar und Gemini 1.5 Pro?+

Sonar kostet $1.00 / $1.00 pro 1M Input-/Output-Tokens, Gemini 1.5 Pro liegt bei $1.25 / $5.00. Beim gewichteten Gesamtpreis ist Sonar günstiger.

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

Sonar bietet 128K Tokens Kontext gegenüber 2M Tokens bei Gemini 1.5 Pro. Bei der Generierungsgeschwindigkeit liegt Sonar 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)

Sonar vs Gemini 1.5 Pro

Beliebige Modelle wechseln & vergleichen

Sonar

Gemini 1.5 Pro

Wer gewinnt in welcher Disziplin?

Geprüfte Benchmark-Ergebnisse im Vergleich

Latenz & Antwortzeit

Interaktiver Token-Kosten-Simulator

Sonar · Strengths & Key Advantages

Considerations & Trade-offs

Gemini 1.5 Pro · Strengths & Key Advantages

Considerations & Trade-offs

Token Use & Task Economics

Technische Spezifikationen

Sonar vs Gemini 1.5 Pro: Schnelle Antworten (FAQ)

Fazit

Häufig gestellte Fragen (FAQ)

Related reading