GPT-5.1 Chat vs Gemini 3 Pro
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5.1 Chat by OpenAI and Gemini 3 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, GPT-5.1 Chat holds a notable pricing advantage for token ingestion. On output generation, GPT-5.1 Chat 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 Chat 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-5.1 Chat
高度な推論モデルOpenAI が提供する GPT-5.1 Chat は、最大 128K トークンのコンテキストウィンドウを備え、入力 $0/100万トークン・出力 $0/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$0.00 / 1M
$0.00 / 1M
128K tokens
74%
Gemini 3 Pro
高度な推論モデルGoogle が提供する Gemini 3 Pro は、最大 2M トークンのコンテキストウィンドウを備え、入力 $2/100万トークン・出力 $12/100万トークンで利用可能な、コーディング・推論・業務自動化向け AI モデルです。
$2.00 / 1M
$12.00 / 1M
2M tokens
76.2%
ひと目でわかる主な違い
- GPT-5.1 Chat is 1.0x cheaper for API usage (input + output tokens combined).
- Gemini 3 Pro has a larger context window (2M vs 128K tokens).
- GPT-5.1 Chat is faster at 132 tok/s vs 118 tok/s.
- Gemini 3 Pro leads on SWE-bench coding (76.2% vs 74.0%).
| Feature | GPT-5.1 Chat | Gemini 3 Pro |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 60 | 62 |
Provider | OpenAI | |
Input Price Cost per million input tokens | $0.00 / 1M | $2.00 / 1M |
Output Price Cost per million output tokens | $0.00 / 1M | $12.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.00 / 1M | $1.74 / 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 | 66K |
Throughput Speed Tokens generated per second | 132 tok/s | 118 tok/s |
SWE-bench Verified Real-world software engineering | 74% | 76.2% |
GPQA Diamond Graduate-level reasoning | 71.8% | 73.5% |
Cached Input Price Prompt caching discount per 1M tokens | $0.00 / 1M | $0.20 / 1M |
分野別勝者スコアカード
At-a-glance decision framework検証済みベンチマークスコア比較
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.
レイテンシ＆応答時間
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
リアルタイムAPIトークン料金シミュレーター
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5.1 Chat · Strengths & Key Advantages
- OpenAI の高度な Chat-tuned unified reasoning model アーキテクチャと 128K トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $0/1M、出力 $0/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Gemini 3 Pro · Strengths & Key Advantages
- Google の高度な Sparse Multimodal MoE アーキテクチャと 2M トークンのコンテキスト処理能力
- 優れたコストパフォーマンス（入力 $2/1M、出力 $12/1M）と安定した応答速度
- プログラミング、論理的推論、多言語タスクにおいて高いベンチマーク精度を実現
Considerations & Trade-offs
- 大規模な長文コンテキスト処理時はトークン消費量の最適化が必要
- 専門領域のタスクでは適切なプロンプト設計や RAG の併用が推奨される
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5.1 Chat | Gemini 3 Pro |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.48 | $2.40 |
Output Tokens / Task Tokens generated per task | 52K | 60K |
Reasoning Tokens / Task Thinking tokens burned per task | 28K | 31K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5.1 Chat | Gemini 3 Pro |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-1-chat | models/gemini-3-pro |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Google Gemini API (generateContent) + OpenAI-Compatible SDK |
| Release Date | 2025-11 | 2025-11 |
| Knowledge Cutoff | October 2024 | January 2025 |
| Architecture | Chat-tuned unified reasoning model | Sparse Multimodal MoE |
| 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) | Free across full context window (only KV-cache VRAM scales) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) | Implicit + Explicit Context Caching at $0.2/1M 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 | 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) | 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 | text, vision, audio, video |
| Output Modalities | text | text |
GPT-5.1 Chat vs Gemini 3 Pro: よくある質問 (FAQ)
Which is more intelligent, GPT-5.1 Chat or Gemini 3 Pro?
Gemini 3 Pro is more intelligent. It scores 62 compared with 60 on the composite Intelligence Index.
Which model is faster?
GPT-5.1 Chat is faster, generating 132 tokens per second compared with 118 tokens per second.
Which model is cheaper to run?
GPT-5.1 Chat is cheaper at $0.00 per 1M tokens blended, compared with $1.74 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-5.1 Chat has lower latency, with a time to first token of 1.18s compared with 1.80s.
Which model has a larger context window?
Gemini 3 Pro has a larger context window, supporting 2M tokens compared with 128K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose GPT-5.1 Chat if your priority is Interactive ChatGPT experiences. Its performance in demanding tasks justifies its operational token cost. Choose Gemini 3 Pro if your workflow prioritizes Long-document analysis, full-codebase reasoning, and multimodal tasks needing 500K+ tokens. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
よくある質問 (FAQ)
How does GPT-5.1 Chat compare to Gemini 3 Pro in API token pricing and cost per token?+
GPT-5.1 Chat costs $0.00 per 1M input tokens and $0.00 per 1M output tokens, whereas Gemini 3 Pro costs $2.00 input and $12.00 output. Overall, GPT-5.1 Chat 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-5.1 Chat or Gemini 3 Pro?+
GPT-5.1 Chat supports a context window of 128K tokens (~96k words), while Gemini 3 Pro supports 2,000K tokens (~1,500k 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: GPT-5.1 Chat or Gemini 3 Pro?+
GPT-5.1 Chat scores 74% on SWE-bench Verified while Gemini 3 Pro scores 76.2%. 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 GPT-5.1 Chat and Gemini 3 Pro?+
GPT-5.1 Chat features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Gemini 3 Pro provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5.1 Chat and Gemini 3 Pro?+
GPT-5.1 Chat generates approximately 132 tokens per second, while Gemini 3 Pro delivers 118 tokens per second. In latency-sensitive production environments, GPT-5.1 Chat provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5.1 Chat and Gemini 3 Pro to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5.1 Chat 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.
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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.
公的ソース: GetInfoUs Independent AI Benchmarks & Official API Specs