GPT-5 nano vs Claude Haiku 4.5
Benchmarks, API Token Pricing, Context Window & Speed Comparison
Related topics (12)
GPT-5 nano by OpenAI and Claude Haiku 4.5 by Anthropic 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 nano holds a notable pricing advantage for token ingestion. On output generation, GPT-5 nano is more economical per million generated tokens. In data-heavy architectures requiring extensive repository or document analysis, GPT-5 nano provides an expanded memory horizon with 400K tokens. In latency-sensitive production environments, GPT-5 nano 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.
Cambia y Compara Cualquier Modelo
Select any AI models from our verified benchmark database to generate live side-by-side matrices.
GPT-5 nano
El GPT-5 más económicoel smallest GPT-5 lane, construido para enrutamiento, extracción y clasificación.
$0.05 / 1M
$0.40 / 1M
400K tokens
34.5%
Claude Haiku 4.5
Vía rápidade Anthropic económico potencia de late 2025, ofreciendo sub-second primer-token latencia, nativo razonamiento modo, y 64K salida capacidad un ultra-bajo prices.
$1.00 / 1M
$5.00 / 1M
200K tokens
58.2%
Diferencias Clave de un Vistazo
- GPT-5 nano is 13.9x cheaper for API usage (input + output tokens combined).
- GPT-5 nano has a larger context window (400K vs 200K tokens).
- GPT-5 nano is faster at 250 tok/s vs 140 tok/s.
- Claude Haiku 4.5 leads on SWE-bench coding (58.2% vs 34.5%).
| Característica | GPT-5 nano | Claude Haiku 4.5 |
|---|---|---|
Intelligence Index Composite reasoning & knowledge score (0-100) | 35 | 54 |
Provider | OpenAI | Anthropic |
Input Price Cost per million input tokens | $0.05 / 1M | $1.00 / 1M |
Output Price Cost per million output tokens | $0.40 / 1M | $5.00 / 1M |
Blended Price Weighted 7:2:1 cache:input:output per 1M | $0.05 / 1M | $0.77 / 1M |
Context Window Maximum input + output tokens | 400K tokens~600 A4 pages of text | 200K tokens~300 A4 pages of text |
Max Output Tokens Per single request | 128K | 64K |
Throughput Speed Tokens generated per second | 250 tok/s | 140 tok/s |
SWE-bench Verified Real-world software engineering | 34.5% | 58.2% |
GPQA Diamond Graduate-level reasoning | 48% | 62.1% |
Cached Input Price Prompt caching discount per 1M tokens | $0.01 / 1M | $0.10 / 1M |
¿Quién gana en cada área?
At-a-glance decision frameworkPuntuaciones de Benchmarks Verificadas
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.
Latencia y Tiempo de Respuesta
Time to first token (TTFT) and estimated end-to-end response time for a typical 500-token response.
Simulador Interactivo de Costos de Tokens
Adjust sliders to calculate projected monthly cloud API spending with prompt caching discounts.
GPT-5 nano · Strengths & Key Advantages
- Extremely cheap un $0.05 / $0.40 por 1M tokens
- muy bajo latencia para alto-rendimiento canalizaciones
- Still supports razonamiento y herramienta calls
- Ideal as un primer-pass router en multi-modelo canalizaciones
Considerations & Trade-offs
- Weak en complejo razonamiento y de formato largo generación
- no suitable as un primary asistente modelo
Claude Haiku 4.5 · Strengths & Key Advantages
- Superb velocidad (140 tokens/sec) y ultra-responsive 280ms tiempo-un-primer-token
- Aggressive prompt cache precios ($0.10/M tokens) enabling ultra-cheap continuous monitoring
- primer Haiku nivel un incorporate nativo razonamiento modo y 64K maximum salida tokens
Considerations & Trade-offs
- menor rendimiento en complejo multi-repositorio refactorización que Sonnet y Opus
- Struggles con highly ambiguous matemático proofs
Token Use & Task Economics
Estimated cost and token consumption to complete one representative agentic task.
| Metric | GPT-5 nano | Claude Haiku 4.5 |
|---|---|---|
Cost per Task Estimated USD to finish one task | $0.06 | $0.70 |
Output Tokens / Task Tokens generated per task | 42K | 20K |
Reasoning Tokens / Task Thinking tokens burned per task | 18K | 10K |
Especificaciones Técnicas
Architecture, release timeline, capabilities, and modalities.
| Característica | GPT-5 nano | Claude Haiku 4.5 |
|---|---|---|
| Official API Model ID (SDK) | gpt-5-nano | claude-haiku-4-5-20251001 |
| API Protocol | OpenAI Responses API & Chat Completions v1 | Anthropic Messages API + AWS Bedrock + Vertex AI |
| Release Date | 2025-08 | 2025-10 |
| Knowledge Cutoff | September 2024 | July 2025 |
| Architecture | Nano unified reasoning model | Lightweight High-Speed Multimodal Transformer |
| Parameters | Confidential | Confidential |
| Enterprise Rate Limits (RPM / TPM) | 10,000 – 30,000 RPM (Tier 5 Enterprise) · 800,000 – 2,000,000 TPM | 4,000 – 10,000 RPM (Tier 4 + Priority Tier) · 800,000 – 2,000,000 TPM |
| Long-Context Tier (>128K/200K) | Flat rate ($0.05/1M in) across full 400K window | Standard rate up to 200K tokens; 2x input/output tier above 200K tokens |
| Prompt Cache Policy & TTL | Automatic prefix caching at $0.005/1M (no extra cache-write fee) | $0.1/1M cached read (90% off); 5-min default & 1-hour extended 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 · 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 Citations + Code Execution Tool + MCP Connectors |
| Official Fine-Tuning Support | Supervised Fine-Tuning (SFT), Direct Preference (DPO) & Reinforcement Fine-Tuning (RFT) | Custom Model Tuning via Amazon Bedrock & Prompt 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) |
| 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 | Anthropic RSP ASL-2 / ASL-3 Constitutional Safety Evaluated |
| 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 nano vs Claude Haiku 4.5: Respuestas Rápidas (FAQ)
Which is more intelligent, GPT-5 nano or Claude Haiku 4.5?
Claude Haiku 4.5 is more intelligent. It scores 54 compared with 35 on the composite Intelligence Index.
Which model is faster?
GPT-5 nano is faster, generating 250 tokens per second compared with 140 tokens per second.
Which model is cheaper to run?
GPT-5 nano is cheaper at $0.05 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 0.38s.
Which model has a larger context window?
GPT-5 nano has a larger context window, supporting 400K tokens compared with 200K tokens.
Veredicto
Verdict & Model Selection Recommendation: Choose GPT-5 nano if your priority is Bulk classification, tagging and cheap agent routing. Its performance in demanding tasks justifies its operational token cost. Choose Claude Haiku 4.5 if your workflow prioritizes High-volume customer interaction bots, real-time code autocomplete, log triage, and document routing. For large-scale batch processing or customer-facing conversational interfaces, its balance of speed and economics delivers superior ROI.
Preguntas frecuentes
How does GPT-5 nano compare to Claude Haiku 4.5 in API token pricing and cost per token?+
GPT-5 nano costs $0.05 per 1M input tokens and $0.40 per 1M output tokens, whereas Claude Haiku 4.5 costs $1.00 input and $5.00 output. Overall, GPT-5 nano 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 nano or Claude Haiku 4.5?+
GPT-5 nano supports a context window of 400K tokens (~300k words), while Claude Haiku 4.5 supports 200K tokens (~150k words). Therefore, GPT-5 nano can process more textual data and document context in a single prompt.
Which AI model is better for software engineering and coding: GPT-5 nano or Claude Haiku 4.5?+
GPT-5 nano scores 34.5% on SWE-bench Verified while Claude Haiku 4.5 scores 58.2%. For autonomous software engineering and multi-file debugging, Claude Haiku 4.5 holds the verified benchmark lead.
What is the difference in reasoning capability and extended thinking between GPT-5 nano and Claude Haiku 4.5?+
GPT-5 nano features native chain-of-thought extended thinking mode with configurable reasoning effort. In contrast, Claude Haiku 4.5 provides chain-of-thought reasoning depth for complex math and science.
How does inference speed (tokens per second) compare between GPT-5 nano and Claude Haiku 4.5?+
GPT-5 nano generates approximately 250 tokens per second, while Claude Haiku 4.5 delivers 140 tokens per second. In latency-sensitive production environments, GPT-5 nano provides faster time-to-first-token (TTFT) and higher streaming response velocity.
Can you implement a tiered LLM routing setup combining GPT-5 nano and Claude Haiku 4.5 to optimize costs?+
Yes! High-efficiency architectures route high-frequency routine classification and conversational tasks to GPT-5 nano 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
- Claude Haiku 4.5 vs GPT-5 Chat: Benchmarks, Pricing & Speed ComparisonComparar
- Claude Sonnet 4.5 vs Claude Haiku 4.5: Benchmarks, Pricing & Speed ComparisonComparar
- GPT-5 mini vs Claude Haiku 4.5: Benchmarks, Pricing & Speed ComparisonComparar
- GPT-5 mini vs GPT-5 nano: Benchmarks, Pricing & Speed ComparisonComparar
- GPT-5 nano vs GPT-5 Chat: Benchmarks, Pricing & Speed ComparisonComparar
- Aion-3.0 vs Aion-Mini: Benchmarks, Pricing & Speed ComparisonComparar
Las puntuaciones de benchmarks, precios por token y límites de tasa evolucionan rápidamente. Verifique siempre las especificaciones actuales en la documentación oficial del proveedor.
Fuentes: GetInfoUs Independent AI Benchmarks & Official API Specs