Claude 3.5 Haiku vs GPT-4o mini
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude 3.5 Haiku by Anthropic and GPT-4o mini 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-4o mini holds a notable pricing advantage for token ingestion. On output generation, GPT-4o mini is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude 3.5 Haiku provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, GPT-4o mini 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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Claude 3.5 Haiku
高性能 AI モデルAnthropic が提供する Claude 3.5 Haiku は、最大 200K トークンのコンテキストウィンドウを備え、入力 $0.8/100万トークン・出力 $4/100万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。
$0.80 / 1M
$4.00 / 1M
200K tokens
40.6%
GPT-4o mini
高性能 AI モデルOpenAI が提供する GPT-4o mini 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 $0.15/100万トークン・出力 $0.6/100万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。
$0.15 / 1M
$0.60 / 1M
128K tokens
24.5%
ひと目でわかる主な違い
- GPT-4o mini is 5.6x cheaper for API usage (input + output tokens combined).
- Claude 3.5 Haiku has a larger context window (200K vs 128K tokens).
- GPT-4o mini is faster at 140 tok/s vs 130 tok/s.
- Claude 3.5 Haiku leads on SWE-bench coding (40.6% vs 24.5%).
| Feature | Claude 3.5 Haiku | GPT-4o mini |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 36 | 30 |
Provider | Anthropic | OpenAI |
Input Price Cost per million input tokens | $0.80 / 1M | $0.15 / 1M |
Output Price Cost per million output tokens | $4.00 / 1M | $0.60 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.62 / 1M | $0.14 / 1M |
Context Window Maximum input + output tokens | 200K tokens~300 A4 pages of text | 128K tokens~192 A4 pages of text |
Max Output Tokens Per single request | 8K | 16K |
Throughput Speed Tokens generated per second | 130 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 40.6% | 24.5% |
GPQA Diamond Graduate-level reasoning | 41.6% | 40.2% |
Cached Input Price Prompt caching discount per 1M tokens | $0.08 / 1M | $0.07 / 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.
Claude 3.5 Haiku · Strengths & Key Advantages
- Anthropic の高度な Optimized Lightweight Transformer アーキテクチャと 200K 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 $0.8/1M、出力 $4/1M）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-4o mini · Strengths & Key Advantages
- OpenAI の高度な Small Multimodal Dense 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 $0.15/1M、出力 $0.6/1M）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 | Claude 3.5 Haiku | GPT-4o mini |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.90 | $0.16 |
Output Tokens / Task Tokens generated per task | 45K | 45K |
Reasoning Tokens / Task Thinking tokens burned per task | 0K | 0K |
技術仕様・スペック
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude 3.5 Haiku | GPT-4o mini |
|---|---|---|
| Official API Model ID (SDK) | claude-3-5-haiku-20241101 | gpt-4o-mini |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2024-11 | 2024-07 |
| Knowledge Cutoff | July 2024 | October 2023 |
| Architecture | Optimized Lightweight Transformer | Small Multimodal Dense |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 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) | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens | Flat rate ($0.15/1M in) across full 128K window |
| Prompt Cache Policy & TTL | $0.08/1M cached read (90% off); 5-min default & 1-hour extended TTL | Automatic prefix caching at $0.075/1M (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 Citations + Code Execution Tool + MCP Connectors | Native Web Search tool + Hosted Code Interpreter & File Search RAG |
| Official Fine-Tuning Support | Custom Model Tuning via Amazon Bedrock & Prompt 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) | 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated | 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 |
Claude 3.5 Haiku vs GPT-4o mini: よくある質問 (FAQ)
Which is more intelligent, Claude 3.5 Haiku or GPT-4o mini?
Claude 3.5 Haiku is more intelligent. It scores 36 compared with 30 on the composite Intelligence Index.
Which model is faster?
GPT-4o mini is faster, generating 140 tokens per second compared with 130 tokens per second.
Which model is cheaper to run?
GPT-4o mini is cheaper at $0.14 per 1M tokens blended, compared with $0.62 (7:2:1 cache:input:output ratio).
Which model has lower latency?
GPT-4o mini has lower latency, with a time to first token of 0.31s compared with 0.38s.
Which model has a larger context window?
Claude 3.5 Haiku has a larger context window, supporting 200K tokens compared with 128K tokens.
総合評価
Verdict & Model Selection Recommendation: Choose Claude 3.5 Haiku if your priority is Rapid customer support chatbots, data extraction agents, and interactive tool-using loops. Its performance in demanding tasks justifies its operational token cost. Choose GPT-4o mini if your workflow prioritizes Data extraction, customer support routing, and lightweight web scraping. 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 Claude 3.5 Haiku compare to GPT-4o mini in API token pricing and cost per token?+
Claude 3.5 Haiku costs $0.80 per 1M input tokens and $4.00 per 1M output tokens, whereas GPT-4o mini costs $0.15 input and $0.60 output. Overall, GPT-4o mini 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: Claude 3.5 Haiku or GPT-4o mini?+
Claude 3.5 Haiku supports a context window of 200K tokens (~150k words), while GPT-4o mini supports 128K tokens (~96k words). Therefore, Claude 3.5 Haiku can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Claude 3.5 Haiku or GPT-4o mini?+
Claude 3.5 Haiku scores 40.6% on SWE-bench Verified while GPT-4o mini scores 24.5%. For autonomous software engineering and multi-file debugging, Claude 3.5 Haiku holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Claude 3.5 Haiku and GPT-4o mini?+
Claude 3.5 Haiku is optimized for low-latency direct generation. In contrast, GPT-4o mini focuses on standard fast inference.
How does inference speed (tokens per second) compare between Claude 3.5 Haiku and GPT-4o mini?+
Claude 3.5 Haiku generates approximately 130 tokens per second, while GPT-4o mini delivers 140 tokens per second. In latency-sensitive production environments, GPT-4o mini provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining Claude 3.5 Haiku and GPT-4o mini to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-4o mini to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude 3.5 Haiku. 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