GPT-5 vs GPT-5.1
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5 by OpenAI 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, both models offer identical input token rates. On output generation, costs are matched. In data-heavy architectures requiring extensive repository or document analysis, both models provide an identical memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5 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
高度な推論モデルOpenAI が提供する GPT-5 は、最大 400K トークンのコンテキストウィンドウを備え、入力 $1.25/100万トークン・出力 $10/100万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。
$1.25 / 1M
$10.00 / 1M
400K tokens
74.9%
GPT-5.1
高度な推論モデルOpenAI が提供する GPT-5.1 は、最大 400K トークンのコンテキストウィンドウを備え、入力 $1.25/100万トークン・出力 $10/100万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。
$1.25 / 1M
$10.00 / 1M
400K tokens
74.9%
ひと目でわかる主な違い
- Both models cost the same under this workload — choose on capability, not price.
- GPT-5 is faster at 135 tok/s vs 130 tok/s.
- GPT-5.1 leads on SWE-bench coding (74.9% vs 74.9%).
| Feature | GPT-5 | GPT-5.1 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 58 | 60 |
Provider | OpenAI | OpenAI |
Input Price Cost per million input tokens | $1.25 / 1M | $1.25 / 1M |
Output Price Cost per million output tokens | $10.00 / 1M | $10.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $1.34 / 1M | $1.34 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 400K tokens~600 A4 pages of text |
Max Output Tokens Per single request | 128K | 128K |
Throughput Speed Tokens generated per second | 135 tok/s | 130 tok/s |
SWE-bench Verified Real-world software engineering | 74.9% | 74.9% |
GPQA Diamond Graduate-level reasoning | 85.7% | 72.1% |
Cached Input Price Prompt caching discount per 1M tokens | $0.13 / 1M | $0.13 / 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 · Strengths & Key Advantages
- OpenAI の高度な Unified reasoning model (fast + thinking modes) アーキテクチャと 400K 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 $1.25/1M、出力 $10/1M）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
GPT-5.1 · Strengths & Key Advantages
- OpenAI の高度な Unified Reasoning (Instant & Thinking) アーキテクチャと 400K 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 $1.25/1M、出力 $10/1M）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 | GPT-5.1 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $1.55 | $1.45 |
Output Tokens / Task Tokens generated per task | 55K | 52K |
Reasoning Tokens / Task Thinking tokens burned per task | 29K | 28K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | GPT-5 | GPT-5.1 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5 | gpt-5-1 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-08 | 2025-11 |
| Knowledge Cutoff | September 2024 | October 2024 |
| Architecture | Unified reasoning model (fast + thinking modes) | Unified Reasoning (Instant & Thinking) |
| 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) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($1.25/1M in) across full 400K window | Flat rate ($1.25/1M in) across full 400K window |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.125/1M (no extra cache-write fee) | 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 | Native Web Search tool + Hosted Code Interpreter & File Search RAG | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | 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) | 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 | OpenAI Preparedness Framework Audited & System Card Published | 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 | text, vision |
| Output Modalities | text | text |
GPT-5 vs GPT-5.1: よくある質問 (FAQ)
Which is more intelligent, GPT-5 or GPT-5.1?
GPT-5.1 is more intelligent. It scores 60 compared with 58 on the composite Intelligence Index.
Which model is faster?
GPT-5 is faster, generating 135 tokens per second compared with 130 tokens per second.
Which model has lower latency?
GPT-5 has lower latency, with a time to first token of 1.15s compared with 1.20s.
総合評価
Verdict & Model Selection Recommendation: Choose GPT-5 if your priority is A default frontier model for production apps and agents. 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.
よくある質問 (FAQ)
How does GPT-5 compare to GPT-5.1 in API token pricing and cost per token?+
Both GPT-5 and GPT-5.1 share matched API token pricing at $1.25 per 1M input tokens and $10.00 per 1M output tokens. Because their token economics are identical, your selection should be guided by benchmark latency, memory horizon, and reasoning capabilities rather than operational token fees. Both models support prompt caching discounts of up to 90% for repeated prompt prefixes.
Which model has a larger context window: GPT-5 or GPT-5.1?+
Both GPT-5 and GPT-5.1 offer an identical context window of 400K tokens (~300k words). Both architectures provide equal capacity for large document ingestion, multi-file codebases, and extended multi-turn conversation histories.
Which AI model is better for software engineering and coding: GPT-5 or GPT-5.1?+
Both models achieve an identical 74.9% score on SWE-bench Verified, demonstrating matched software engineering capabilities.
What is the difference in reasoning capability and extended thinking between GPT-5 and GPT-5.1?+
GPT-5 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 GPT-5 and GPT-5.1?+
GPT-5 generates approximately 135 tokens per second, while GPT-5.1 delivers 130 tokens per second. In latency-sensitive production environments, GPT-5 provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5 and GPT-5.1 to optimize costs?+
Yes! Tiered multi-model routing can optimize for latency and accuracy: you can route high-frequency conversational turns and latency-sensitive queries to GPT-5, and route deep architectural or complex reasoning queries to GPT-5. This balanced routing strategy maximizes system throughput and answer quality.
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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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