Gemini 3 Pro vs GPT-5.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Gemini 3 Pro by Google and GPT-5.1 by OpenAI 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, GPT-5.1 holds a notable pricing advantage for token ingestion. On output generation, GPT-5.1 is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Gemini 3 Pro provides an expanded memory horizon with 2,000K tokens. In latency-sensitive production environments, GPT-5.1 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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Gemini 3 Pro
Deep-Reasoning-ModellGemini 3 Pro von Google ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 2M Token zu API-Preisen von $2/1M Input- und $12/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$2.00 / 1M
$12.00 / 1M
2M tokens
76.2%
GPT-5.1
Deep-Reasoning-ModellGPT-5.1 von OpenAI ist ein leistungsstarkes KI-Modell mit einem Kontextfenster von 400K Token zu API-Preisen von $1.25/1M Input- und $10/1M Output-Token für Softwareentwicklung, Analyse und Enterprise-Workflows.
$1.25 / 1M
$10.00 / 1M
400K tokens
74.9%
Wichtigste Unterschiede auf einen Blick
- GPT-5.1 is 1.3x cheaper for API usage (input + output tokens combined).
- Gemini 3 Pro has a larger context window (2M vs 400K tokens).
- GPT-5.1 is faster at 130 tok/s vs 118 tok/s.
- Gemini 3 Pro leads on SWE-bench coding (76.2% vs 74.9%).
| Feature | Gemini 3 Pro | GPT-5.1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 62 | 60 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $2.00 / 1M | $1.25 / 1M |
Output Price Cost per million output tokens | $12.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.74 / 1M | $1.34 / 1M |
Context Window Maximum input + output tokens | 2M tokens~3,000 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 66K | 128K |
Throughput Speed Tokens generated per second | 118 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 76.2% | 74.9% |
GPQA Diamond Graduate-level reasoning | 73.5% | 72.1% |
Cached Input Price Prompt caching discount per 1M tokens | $0.20 / 1M | $0.13 / 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.
Gemini 3 Pro · Strengths & Key Advantages
- Fortschrittliche Sparse Multimodal MoE-Architektur von Google mit 2M Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($2/1M Input, $12/1M 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
GPT-5.1 · Strengths & Key Advantages
- Fortschrittliche Unified Reasoning (Instant & Thinking)-Architektur von OpenAI mit 400K Token Kontextfenster
- Starkes Preis-Leistungs-Verhältnis ($1.25/1M 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
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Gemini 3 Pro | GPT-5.1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $2.40 | $1.45 |
Output Tokens / Task Tokens generated per task | 60K | 52K |
Reasoning Tokens / Task Thinking tokens burned per task | 31K | 28K |
Technische Spezifikationen
Architecture, release timeline, capabilities, and modalities.
| Feature | Gemini 3 Pro | GPT-5.1 |
|---|---|---|
| Official API Model ID (SDK) | models/gemini-3-pro | gpt-5-1 |
| API Protocol | Google Gemini API (generateContent) + OpenAI-Compatible SDK | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-11 | 2025-11 |
| Knowledge Cutoff | January 2025 | October 2024 |
| Architecture | Sparse Multimodal MoE | Unified Reasoning (Instant & Thinking) |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 4,000,000 – 10,000,000 TPM | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($1.25/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Implicit + Explicit Context Caching at $0.2/1M with configurable TTL | Automatic prefix caching at $0.125/1M (no extra cache-write fee) |
| 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 | Built-in Google Search Grounding + Managed Python Code Execution Sandbox | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Vertex AI Supervised Adapter Tuning & Context Distillation | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) |
| Multimodal Token Billing | Text 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) · Vision (~258–765 tokens per image tile) |
| 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 | Google DeepMind Frontier Safety Framework Verified | OpenAI Preparedness Framework Audited & System Card Published |
| 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, video | text, vision |
| Output Modalities | text | text |
Gemini 3 Pro vs GPT-5.1: Schnelle Antworten (FAQ)
Which is more intelligent, Gemini 3 Pro or GPT-5.1?
Gemini 3 Pro is more intelligent. It scores 62 compared with 60 on the composite Intelligence Index.
Which model is faster?
GPT-5.1 is faster, generating 130 tokens per second compared with 118 tokens per second.
Which model is cheaper to run?
GPT-5.1 is cheaper at $1.34 per 1M tokens blended, compared with $1.74 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.1 has lower latency, with a time to first token of 1.20s compared with 1.80s.
Which model has a larger context window?
Gemini 3 Pro has a larger context window, supporting 2M tokens compared with 400K tokens.
Fazit
Verdict & Model Selection Recommendation: Choose Gemini 3 Pro if your priority is Long-document analysis, full-codebase reasoning, and multimodal tasks needing 500K+ tokens. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5.1 if your workflow prioritizes General-purpose production APIs and teams needing balanced quality, speed, and cost. 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 Gemini 3 Pro compare to GPT-5.1 in API token pricing and cost per token?+
Gemini 3 Pro costs $2.00 per 1M input tokens and $12.00 per 1M output tokens, whereas GPT-5.1 costs $1.25 input and $10.00 output. Overall, GPT-5.1 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: Gemini 3 Pro or GPT-5.1?+
Gemini 3 Pro supports a context window of 2,000K tokens (~1,500k words), while GPT-5.1 supports 400K tokens (~300k words). Therefore, Gemini 3 Pro can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Gemini 3 Pro or GPT-5.1?+
Gemini 3 Pro scores 76.2% on SWE-bench Verified while GPT-5.1 scores 74.9%. For autonomous software engineering and multi-file debugging, Gemini 3 Pro holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Gemini 3 Pro and GPT-5.1?+
Gemini 3 Pro features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5.1 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Gemini 3 Pro and GPT-5.1?+
Gemini 3 Pro generates approximately 118 tokens per second, while GPT-5.1 delivers 130 tokens per second. In latency-sensitive production environments, GPT-5.1 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Gemini 3 Pro and GPT-5.1 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.1 to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Gemini 3 Pro. 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-5.2 Chat vs GPT-5.1: 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