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GPT-5 Chat vs o3-pro

Benchmarks, API Token Pricing, Context Window & Speed Comparison

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AI CompareAI BenchmarkToken PricingModel CostModel SpecsSWE-bench CodingModel VersusWhich AI ModelLLM Speed & LatencyReasoning ModelsExtended ThinkingPrompt Caching ROI
GPT-5 Chat vs o3-pro: Benchmarks, Pricing & Speed Comparison

GPT-5 Chat von OpenAI und o3-pro von OpenAI gehören zu den führenden KI-Modellen. GPT-5 Chat bietet ein Kontextfenster von 128K Tokens bei API-Preisen von $0.00/$0.00 pro 1M Tokens, während o3-pro 200K Tokens Kontext zu $20.00/$80.00 pro 1M Tokens bereitstellt. Hier finden Sie den vollständigen Benchmark-, Latenz- und Kostenvergleich.

Direktvergleich (VS)

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OpenAI

GPT-5 Chat

Frontier-Flaggschiff-Modell

GPT-5 Chat von OpenAI ist ein KI-Modell für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows mit 128K Kontextfenster, 140 Token/s Durchsatz, einem Intelligenz-Index von 56/100 und kostenlosem Open-Weight

Intelligence Index56 / 100
Input Token:

$0.00 / 1M

Output Token:

$0.00 / 1M

Context Window:

128K tokens

SWE-bench Verified:

70%

OpenAI

o3-pro

Deep-Reasoning-Engine

o3-pro von OpenAI ist ein KI-Modell für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen mit 200K Kontextfenster, 30 Token/s Durchsatz, einem Intelligenz-Index von 58/100 und $20/$80 pro 1M Token.

Intelligence Index58 / 100
Input Token:

$20.00 / 1M

Output Token:

$80.00 / 1M

Context Window:

200K tokens

SWE-bench Verified:

71.5%

Wichtigste Unterschiede auf einen Blick

  • GPT-5 Chat is 1.0x cheaper for API usage (input + output tokens combined).
  • o3-pro has a larger context window (200K vs 128K tokens).
  • GPT-5 Chat is faster at 140 tok/s vs 30 tok/s.
  • o3-pro leads on SWE-bench coding (71.5% vs 70.0%).
Need definitions for token pricing, context, or benchmarks?Explore AI Glossary →
FeatureGPT-5 Chato3-pro
Intelligence Index
Composite reasoning & knowledge score (0-100)
5658
Provider
OpenAIOpenAI
Input Price
Cost per million input tokens
$0.00 / 1M$20.00 / 1M
Output Price
Cost per million output tokens
$0.00 / 1M$80.00 / 1M
Blended Price
Weighted 7:2:1 cache:input:output per 1M
$0.00 / 1M$15.50 / 1M
Context Window
Maximum input + output tokens
128K tokens~192 A4 pages of text200K tokens~300 A4 pages of text
Max Output Tokens
Per single request
16K100K
Throughput Speed
Tokens generated per second
140 tok/s30 tok/s
SWE-bench Verified
Real-world software engineering
70%71.5%
GPQA Diamond
Graduate-level reasoning
79%79.5%
Cached Input Price
Prompt caching discount per 1M tokens
$0.00 / 1M$5.00 / 1M

Wer gewinnt in welcher Disziplin?

At-a-glance decision framework
Coding
o3-pro
+1.5% SWE-bench lead
Cost
GPT-5 Chat
1.0x cheaper monthly runtime
Speed
GPT-5 Chat
+367% faster generation
Context
o3-pro
200K tokens window

Geprüfte Benchmark-Ergebnisse im Vergleich

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

GPT-5 Chato3-pro
SWE-bench Verified (Autonomous Coding)

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

70%vs71.5%
GPQA Diamond (Doctorate-Level Science & Math)

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

79%vs79.5%
MMLU-Pro (Multi-task Language Understanding)

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

85%vs88.5%
LMSYS Chatbot Arena Elo

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

1358ptsvs1352pts
Humanity's Last Exam (Frontier Expert Knowledge)

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

11.6%vs13.6%
Terminal-Bench (Agentic Coding & Terminal Use)

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

42.5%vs47.5%
SciCode (Scientific Computing)

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

40%vs45.5%
LiveCodeBench (Competitive Coding)

Contamination-free competitive programming problems collected continuously over time.

68%vs73%
AIME (Advanced Math Competition)

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

88%vs93%
Long-Context Reasoning

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

76%vs80.5%
Generation Speed (Throughput)

Tokens emitted per second during active generation streaming.

140 tok/s vs30 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
GPT-5 Chat1.05s
o3-pro5.20s
Est. End-to-End Response (500 tokens)TTFT + generation time
GPT-5 Chat4.62s
o3-pro21.87s

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)
GPT-5 Chat Monthly API Bill
$0.00 / month
$0/1M in · $0/1M out
o3-pro Monthly API Bill
$109.50 / month
$20/1M in · $80/1M out
GPT-5 Chat saves approximately $109.50 / month (100%) under this workload.1.0x Savings

GPT-5 Chat · Strengths & Key Advantages

  • 128K Kontextfenster und bis zu 16K maximale Output-Token (SWE-bench Verified 70% · GPQA Diamond 79%)
  • Hohe Generierungsgeschwindigkeit von 140 Token/Sekunde bei nur 1050 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur (kostenlosem Open-Weights-Zugang) 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

o3-pro · Strengths & Key Advantages

  • 200K Kontextfenster und bis zu 100K maximale Output-Token (SWE-bench Verified 71.5% · GPQA Diamond 79.5%)
  • Hohe Generierungsgeschwindigkeit von 30 Token/Sekunde bei nur 5200 ms Erstlatenz (TTFT)
  • Effiziente API-Struktur ($20/$80 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 hohem Anfragevolumen empfiehlt sich der Einsatz von Prompt-Caching und Batch-API zur Kostenoptimierung

Token Use & Task Economics

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

MetricGPT-5 Chato3-pro
Cost per Task
Estimated USD to finish one task
$1.50$11.68
Output Tokens / Task
Tokens generated per task
54K58K
Reasoning Tokens / Task
Thinking tokens burned per task
28K38K

Technische Spezifikationen

Architecture, release timeline, capabilities, and modalities.

FeatureGPT-5 Chato3-pro
Official API Model ID (SDK)gpt-5-chato3-pro
API ProtocolOpenAI Responses API & Chat Completions v1OpenAI Responses API & Chat Completions v1
Release Date2025-082025-06
Knowledge CutoffSeptember 2024June 2024
ArchitectureChat-tuned unified reasoning modelHigh-effort reasoning transformer
ParametersConfidentialConfidential
Enterprise Rate Limits (RPM / TPM)10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM
Long-Context Tier (>128K/200K)Free across full context window (only KV-cache VRAM scales)Flat rate ($20/1M in) across full 200K window
Prompt Cache Policy & TTLAutomatic prefix KV-caching in vLLM / SGLang (0 cost)Automatic prefix caching at $5/1M (no extra cache-write fee)
Computer Use / GUI AgentSupported (Screen coordinate grounding, browser & desktop GUI action loops)Supported (Screen coordinate grounding, browser & desktop GUI action loops)
MCP & Parallel Tool CallingNative MCP Client/Server ready · Parallel function callingNative MCP Client/Server ready · Parallel function calling · 100% Strict JSON Schema
Web Search Grounding & SandboxNative Web Search tool + Hosted Code Interpreter & File Search RAGNative Web Search tool + Hosted Code Interpreter & File Search RAG
Official Fine-Tuning SupportSupervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT)
Multimodal Token BillingText BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile)
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 ClassificationOpenAI Preparedness Framework Audited & System Card PublishedOpenAI Preparedness Framework Audited & System Card Published
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, visiontext, vision
Output Modalitiestexttext

GPT-5 Chat vs o3-pro: Schnelle Antworten (FAQ)

Which is more intelligent, GPT-5 Chat or o3-pro?

o3-pro is more intelligent. It scores 58 compared with 56 on the composite Intelligence Index.

Which model is faster?

GPT-5 Chat is faster, generating 140 tokens per second compared with 30 tokens per second.

Which model is cheaper to run?

GPT-5 Chat is cheaper at $0.00 per 1M tokens blended, compared with $15.50 (7:2:1 cache:input:output ratio).

Which model has lower latency?

GPT-5 Chat has lower latency, with a time to first token of 1.05s compared with 5.20s.

Which model has a larger context window?

o3-pro has a larger context window, supporting 200K tokens compared with 128K tokens.

Fazit

Sowohl GPT-5 Chat (Optimal für anspruchsvolles Enterprise-Reasoning, Softwareentwicklung und produktive KI-Workflows, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) als auch o3-pro (Optimal für tiefgehendes logisches Denken, komplexe Mathematik und wissenschaftliche Analysen, Enterprise-RAG-Systeme und skalierbare API-Pipelines.) überzeugen im Produktionseinsatz: o3-pro führt bei komplexem Reasoning und Coding, GPT-5 Chat liefert den höheren Token-Durchsatz und GPT-5 Chat ist die kosteneffizienteste Wahl für große API-Workloads.

Häufig gestellte Fragen (FAQ)

Welches Modell ist besser für Coding und Reasoning – GPT-5 Chat oder o3-pro?+

Im Gesamt-Intelligenzindex und bei Software-Benchmarks liegt o3-pro vorn. Prüfen Sie oben die SWE-bench- und GPQA-Werte für Ihren Anwendungsfall.

Wie unterscheiden sich die API-Preise von GPT-5 Chat und o3-pro?+

GPT-5 Chat kostet $0.00 / $0.00 pro 1M Input-/Output-Tokens, o3-pro liegt bei $20.00 / $80.00. Beim gewichteten Gesamtpreis ist GPT-5 Chat günstiger.

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

GPT-5 Chat bietet 128K Tokens Kontext gegenüber 200K Tokens bei o3-pro. Bei der Generierungsgeschwindigkeit liegt GPT-5 Chat 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 OpenAI und OpenAI, Systemkarten und unabhängige Benchmark-Auswertungen (2026).