Claude Haiku 4.5 vs GPT-5 Chat
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
Claude Haiku 4.5 by Anthropic and GPT-5 Chat 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 Chat holds a notable pricing advantage for token ingestion. On output generation, GPT-5 Chat is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, Claude Haiku 4.5 provides an expanded memory horizon with 200K tokens. In latency-sensitive production environments, both models deliver balanced throughput and low time-to-first-token latency for interactive 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 Haiku 4.5
深度推理模型Anthropic 推出的 Claude Haiku 4.5 人工智能模型，具备 200K 上下文窗口，输入价格为 $1/百万 Token，输出价格为 $5/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$1.00 / 1M
$5.00 / 1M
200K tokens
58.2%
GPT-5 Chat
深度推理模型OpenAI 推出的 GPT-5 Chat 人工智能模型，具备 128K 上下文窗口，输入价格为 $0/百万 Token，输出价格为 $0/百万 Token，专为高质量生产环境、推理与自动化工作流打造。
$0.00 / 1M
$0.00 / 1M
128K tokens
70%
核心差异一览
- GPT-5 Chat is 1.0x cheaper for API usage (input + output tokens combined).
- Claude Haiku 4.5 has a larger context window (200K vs 128K tokens).
- GPT-5 Chat is faster at 140 tok/s vs 140 tok/s.
- GPT-5 Chat leads on SWE-bench coding (70.0% vs 58.2%).
| Feature | Claude Haiku 4.5 | GPT-5 Chat |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 54 | 56 |
Provider | Anthropic | OpenAI |
Input Price Cost per million input tokens | $1.00 / 1M | $0.00 / 1M |
Output Price Cost per million output tokens | $5.00 / 1M | $0.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.77 / 1M | $0.00 / 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 | 64K | 16K |
Throughput Speed Tokens generated per second | 140 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 58.2% | 70% |
GPQA Diamond Graduate-level reasoning | 62.1% | 79% |
Cached Input Price Prompt caching discount per 1M tokens | $0.10 / 1M | $0.00 / 1M |
各项指标谁更胜一筹？
At-a-glance decision framework权威基准测试得分对比
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.
延迟与响应速度
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
实时 Token 成本模拟器
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
Claude Haiku 4.5 · Strengths & Key Advantages
- 由 Anthropic 研发的高精度 Lightweight High-Speed Multimodal Transformer 架构，支持 200K 上下文窗口
- 极具竞争力的 API 定价（输入 $1/1M，输出 $5/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
GPT-5 Chat · Strengths & Key Advantages
- 由 OpenAI 研发的高精度 Chat-tuned unified reasoning model 架构，支持 128K 上下文窗口
- 极具竞争力的 API 定价（输入 $0/1M，输出 $0/1M），支持高速推理与工具调用
- 在代码生成、逻辑推理与多语言处理基准测试中表现出色
Considerations & Trade-offs
- 超长上下文请求需要规划 Token 成本与并发配额
- 针对特定垂直领域任务建议结合系统提示词与检索增强（RAG）以获得最佳效果
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | Claude Haiku 4.5 | GPT-5 Chat |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.70 | $1.50 |
Output Tokens / Task Tokens generated per task | 20K | 54K |
Reasoning Tokens / Task Thinking tokens burned per task | 10K | 28K |
技术规格参数
Architecture, release timeline, capabilities, and modalities.
| Feature | Claude Haiku 4.5 | GPT-5 Chat |
|---|---|---|
| Official API Model ID (SDK) | claude-haiku-4-5-20251001 | gpt-5-chat |
| API Protocol | Anthropic Messages API + AWS Bedrock + Vertex AI | OpenAI Responses API & Chat Completions v1 |
| Release Date | 2025-10 | 2025-08 |
| Knowledge Cutoff | July 2025 | September 2024 |
| Architecture | Lightweight High-Speed Multimodal Transformer | Chat-tuned unified reasoning model |
| 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 | Free across full context window (only KV-cache VRAM scales) |
| Prompt Cache Policy & TTL | $0.1/1M cached read (90% off); 5-min default & 1-hour extended TTL | Automatic prefix KV-caching in vLLM / SGLang (0 cost) |
| 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 |
| 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 Haiku 4.5 vs GPT-5 Chat: 常见问题解答 (FAQ)
Which is more intelligent, Claude Haiku 4.5 or GPT-5 Chat?
GPT-5 Chat is more intelligent. It scores 56 compared with 54 on the composite Intelligence Index.
Which model is cheaper to run?
GPT-5 Chat is cheaper at $0.00 per 1M tokens blended, compared with $0.77 (7:2:1 cache:input:output ratio).
Which model has lower latency?
Claude Haiku 4.5 has lower latency, with a time to first token of 0.28s compared with 1.05s.
Which model has a larger context window?
Claude Haiku 4.5 has a larger context window, supporting 200K tokens compared with 128K tokens.
综合结论
Verdict & Model Selection Recommendation: Choose Claude Haiku 4.5 if your priority is High-volume customer interaction bots, real-time code autocomplete, log triage, and document routing. Its performance in demanding tasks justifies its operational token cost. Choose GPT-5 Chat if your workflow prioritizes Interactive chat and writing inside ChatGPT. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
常见问题 (FAQ)
How does Claude Haiku 4.5 compare to GPT-5 Chat in API token pricing and cost per token?+
Claude Haiku 4.5 costs $1.00 per 1M input tokens and $5.00 per 1M output tokens, whereas GPT-5 Chat costs $0.00 input and $0.00 output. Overall, GPT-5 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: Claude Haiku 4.5 or GPT-5 Chat?+
Claude Haiku 4.5 supports a context window of 200K tokens (~150k words), while GPT-5 Chat supports 128K tokens (~96k words). Therefore, Claude Haiku 4.5 can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: Claude Haiku 4.5 or GPT-5 Chat?+
Claude Haiku 4.5 scores 58.2% on SWE-bench Verified while GPT-5 Chat scores 70%. For autonomous software engineering and multi-file debugging, GPT-5 Chat holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between Claude Haiku 4.5 and GPT-5 Chat?+
Claude Haiku 4.5 features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, GPT-5 Chat provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between Claude Haiku 4.5 and GPT-5 Chat?+
Both Claude Haiku 4.5 and GPT-5 Chat deliver matching throughput at approximately 140 tokens per second. In latency-sensitive production environments, streaming response velocity is comparable, with real-world time-to-first-token (TTFT) determined primarily by provider routing and concurrency load.
Can you implement a tiered LLM routing setup combining Claude Haiku 4.5 and GPT-5 Chat to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5 Chat to save operational costs, and dynamically escalate complex architectural queries or reasoning tasks to Claude Haiku 4.5. 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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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.
数据来源: GetInfoUs Independent AI Benchmarks & Official API Specs