GPT-4o vs Gemini 1.5 Pro
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-4o by OpenAI and Gemini 1.5 Pro by Google represent two of the frontier AI models analyzed in our comprehensive 2026 AI benchmark and LLM comparison. In this head-to-head evaluation, we examine API token pricing, context window specifications, inference speed (tokens per second), and empirical reasoning performance. From a pure economic perspective, Gemini 1.5 Pro holds a notable pricing advantage for token ingestion. On output generation, Gemini 1.5 Pro is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 1.5 Pro provides an expanded memory horizon with 2,000K tokens. In latency-sensitive production environments, GPT-4o leads in tokens-per-second throughput, making it ideal for real-time conversational agents and high-concurrency workflows. Below is the complete empirical head-to-head breakdown across cost, speed, context, and capability matrices.
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GPT-4o
Flaggschiff-Frontier-ModellGPT-4o von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 128K Token zu API-Preisen von $2.5/1M Input- und $10/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$2.50 / 1M
$10.00 / 1M
128K tokens
38.8%
Gemini 1.5 Pro
Flaggschiff-Frontier-ModellGemini 1.5 Pro von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 2M Token zu API-Preisen von $1.25/1M Input- und $5/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.25 / 1M
$5.00 / 1M
2M tokens
32.5%
Wichtigste Unterschiede auf einen Blick
- Gemini 1.5 Pro is 2.1x cheaper for API usage (input + output tokens combined).
- Gemini 1.5 Pro has a larger context window (2M vs 128K tokens).
- GPT-4o is faster at 105 tok/s vs 65 tok/s.
- GPT-4o leads on SWE-bench coding (38.8% vs 32.5%).
| Feature | GPT-4o | Gemini 1.5 Pro |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 41 | 46 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $2.50 / 1M | $1.25 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $5.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $2.38 / 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 | 16K | 8K |
Throughput Speed Tokens generated per second | 105 tok/s | 65 tok/s |
SWE-bench Verified Real-world software engineering | 38.8% | 32.5% |
GPQA Diamond Graduate-level reasoning | 53.6% | 58.5% |
Cached Input Price Prompt caching discount per 1M tokens | $1.25 / 1M | $0.31 / 1M |
Wer gewinnt in welcher Disziplin?
At-a-glance decision frameworkGeprüfte Benchmark-Ergebnisse im Vergleich
Official evaluation metrics verified by independent third-party research labs.
Industry benchmark evaluating real-world GitHub issue resolution and multi-file debugging.
Challenging questions created by domain experts to test complex PhD-level reasoning.
Hardened reasoning and academic knowledge benchmark with reduced lucky-guess probability.
Crowdsourced human preference evaluation based on blind, head-to-head model battles.
Extremely difficult, closed-ended questions spanning the frontier of human expert knowledge.
Autonomous multi-step tasks executed inside a real command-line terminal environment.
Research-grade coding problems drawn from real scientific and numerical workloads.
Contamination-free competitive programming problems collected continuously over time.
American Invitational Mathematics Examination — multi-step Olympiad-level reasoning.
Accuracy when reasoning over very large inputs that fill the model context window.
Tokens emitted per second during active generation streaming.
Latenz & Antwortzeit
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Interaktiver Token-Kosten-Simulator
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-4o · Strengths & Key Advantages
- Fortschrittliche Omni-Multimodal Dense-Architektur von OpenAI mit 128K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2.5/1M Input, $10/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Gemini 1.5 Pro · Strengths & Key Advantages
- Fortschrittliche Sparse Mixture-of-Experts (MoE) Multimodal-Architektur von Google mit 2M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1.25/1M Input, $5/1M Output) bei hoher Durchsatzrate
- Hohe Präzision bei Code-Generierung, logischem Schließen und mehrsprachigen Benchmarks
Considerations & Trade-offs
- Bei sehr großen Kontextmengen ist eine sorgfältige Token-Budgetierung ratsam
- Spezialisierte Fachaufgaben profitieren von gezieltem Prompt-Engineering
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-4o | Gemini 1.5 Pro |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.68 | $0.20 |
Output Tokens / Task Tokens generated per task | 24K | 24K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-4o | Gemini 1.5 Pro |
|---|---|---|
| Official API Model ID (SDK) | gpt-4o | models/gemini-1-5-pro |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2024-05 | 2024-02 |
| Knowledge Cutoff | October 2023 | May 2024 |
| Architecture | Omni-Multimodal Dense | Sparse Mixture-of-Experts (MoE) Multimodal |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 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 ($2.5/1M in) across full 128K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $1.25/1M (no extra cache-write fee) | Implicit + Explicit Context Caching at $0.31/1M with configurable TTL |
| 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Built-in Google Search Grounding + Managed Python Code Execution Sandbox |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Vertex AI Supervised Adapter Tuning & Context Distillation |
| Multimodal Token Billing | Text BPE (~4 chars/token) · Vision (~258–765 tokens per image tile) · Audio (~25–32 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) | Cloud-Hosted Proprietary (FP8 / BF16 Tensor Core Cluster) · Zero Local VRAM Required (100% Serverless Cloud API) |
| Safety & Frontier Classification | OpenAI Preparedness Framework Audited & System Card Published | 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, vision, audio | text, vision, audio, video |
| Output Modalities | text, audio | text |
GPT-4o vs Gemini 1.5 Pro: Schnelle Antworten (FAQ)
Which is more intelligent, GPT-4o or Gemini 1.5 Pro?
Gemini 1.5 Pro is more intelligent. It scores 46 compared with 41 on the composite Intelligence Index.
Which model is faster?
GPT-4o is faster, generating 105 tokens per second compared with 65 tokens per second.
Which model is cheaper to run?
Gemini 1.5 Pro is cheaper at $0.97 per 1M tokens blended, compared with $2.38 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4o has lower latency, with a time to first token of 0.41s 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
Verdict & Model Selection Recommendation: Choose GPT-4o if your priority is Omni-modal voice applications, conversational agents, and structured enterprise APIs. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 1.5 Pro if your workflow prioritizes Massive codebase migration, 1-hour video understanding, financial report libraries, and audio analysis. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Häufig gestellte Fragen (FAQ)
How does GPT-4o compare to Gemini 1.5 Pro in API token pricing and cost per token?+
GPT-4o costs $2.50 per 1M input tokens and $10.00 per 1M output tokens, whereas Gemini 1.5 Pro costs $1.25 input and $5.00 output. Overall, Gemini 1.5 Pro is the more cost-effective model for high-volume API pipelines. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-4o or Gemini 1.5 Pro?+
GPT-4o supports a context window of 128K tokens (~96k words), while Gemini 1.5 Pro supports 2,000K tokens (~1,500k words). Therefore, Gemini 1.5 Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-4o or Gemini 1.5 Pro?+
GPT-4o scores 38.8% on SWE-bench Verified while Gemini 1.5 Pro scores 32.5%. For autonomous software engineering and multi-file debugging, GPT-4o holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-4o and Gemini 1.5 Pro?+
GPT-4o is optimized for low-latency direct generation. In contrast, Gemini 1.5 Pro focuses on standard fast inference.
How does inference speed (tokens per second) compare between GPT-4o and Gemini 1.5 Pro?+
GPT-4o generates approximately 105 tokens per second, while Gemini 1.5 Pro delivers 65 tokens per second. In latency-sensitive production environments, GPT-4o provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-4o and Gemini 1.5 Pro to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to Gemini 1.5 Pro to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to GPT-4o. This tiered routing pattern cuts overall API bills by up to 70%.
Understand key metrics like context window, token pricing, reasoning mode, and SWE-bench scores in our comprehensive AI glossary.
Related reading
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- GPT-4o vs GPT-4 Turbo: Benchmarks, Pricing & Speed ComparisonVergleich
- Grok 2 vs GPT-4o: Benchmarks, Pricing & Speed ComparisonVergleich
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: GetInfoUs Independent AI Benchmarks & Official API Specs