GPT-5 Pricing Calculator

GPT-5 Pricing: What You Actually Pay

GPT-5.4 is OpenAI’s current frontier model — the latest revision in the GPT-5 family. At $10.00 per million input tokens and $30.00 per million output tokens, it sits between the mid-tier GPT-4o and the most expensive closed models on the market. The calculator above gives you exact cost estimates based on your token volumes.

GPT-5.4 Token Pricing at a Glance

OpenAI uses the cl200k tokenizer, where 1 token averages about 4 characters of English text. Here’s how GPT-5.4 compares within OpenAI’s own lineup:

• GPT-5.4 — $10.00 input / $30.00 output per 1M tokens (256K context)
• GPT-4o — $2.50 input / $10.00 output per 1M tokens (128K context)
• GPT-4o Mini — $0.15 input / $0.60 output per 1M tokens (128K context)
• o3 — $10.00 input / $40.00 output per 1M tokens (200K context, reasoning model)

The 3:1 output-to-input cost ratio on GPT-5.4 is competitive. Claude Opus charges a 5:1 ratio ($15/$75), making GPT-5.4 significantly cheaper for tasks that generate long outputs like code, articles, or detailed analysis.

GPT-5.4 vs Competitors

How does GPT-5.4 pricing stack up against other frontier models?

• vs Claude Opus 4 — GPT-5.4 is 33% cheaper on input and 60% cheaper on output. For output-heavy workloads (code generation, long-form writing), the savings are substantial.
• vs Claude Sonnet 4 — GPT-5.4 costs about 3x more for input ($10 vs $3) and 2x more for output ($30 vs $15). Sonnet offers a quality-per-dollar sweet spot if you don’t need frontier performance.
• vs Gemini 2.5 Pro — Similar input pricing ($10 vs variable), but Gemini charges more for large context windows. GPT-5.4’s 256K flat-rate context is simpler to budget.
• vs Llama 4 Maverick (hosted) — Open-weight models through providers like Together AI cost $0.20-1.00 per million tokens. That’s 10-50x cheaper, though quality on complex reasoning tasks is lower.

The right choice depends on your workload. For tasks where GPT-5.4 and a cheaper model produce similar results — summarization, classification, basic extraction — you’re overpaying at $10/$30.

Where GPT-5.4 Delivers Value

GPT-5.4 justifies its pricing for specific workloads based on benchmark leadership (93.1 MMLU, 92.8 HumanEval):

• Code generation — highest HumanEval score means fewer broken completions and less debugging time
• Knowledge-intensive tasks — top MMLU performance for factual accuracy in research and analysis
• Long-output generation — the output cost advantage over Claude Opus makes it cheaper for reports, documentation, and articles
• Complex multi-step reasoning — frontier performance on chain-of-thought tasks where smaller models fail

For quick classification, short-answer extraction, or high-volume low-complexity tasks, drop to GPT-4o ($2.50/$10) or GPT-4o Mini ($0.15/$0.60). The quality difference won’t matter, but the cost difference will.

Cost Optimization Strategies

OpenAI provides several levers to reduce your GPT-5.4 spend without switching models:

• Batch API — 50% discount for async workloads with a 24-hour completion window. If latency isn’t critical, this is the single biggest cost reduction available.
• Prompt caching — repeated prompt prefixes (system prompts, few-shot examples) are billed at reduced rates on subsequent calls. Structure prompts with stable prefixes to maximize cache hits.
• Predicted outputs — for edit-style tasks, provide an expected output and only pay for the diff. Ideal for code refactoring and template-based generation.
• Model routing — use GPT-5.4 as your quality ceiling but route 70-80% of requests to GPT-4o or GPT-4o Mini based on task complexity. Most teams find that only 20-30% of queries actually need frontier performance.
• Fine-tuning — train GPT-4o Mini on GPT-5.4 outputs for your specific use case to get 80% of the quality at 2% of the cost. Best for repetitive, well-scoped tasks.

Common Billing Mistakes

Watch for these traps that inflate your OpenAI bill:

1. Ignoring context window costs — a full 256K context costs $2.56 per request just for input. If your app stuffs the context window with retrieval results, costs spike fast. Only include what the model actually needs.
2. Not using the Batch API — many teams run nightly analytics, content generation, or classification jobs in real-time when they could batch them and save 50%.
3. Oversizing the model for the task — GPT-5.4 for sentiment classification is like hiring a surgeon to put on a bandage. Audit your API calls and route simple tasks to Mini.
4. Forgetting output tokens in estimates — developers often calculate costs based on input length alone. For generative tasks, output tokens typically exceed input tokens and cost 3x more per token.

Plug your actual token volumes into the calculator above to see exactly what GPT-5.4 will cost — and use the model comparison to check whether a cheaper alternative fits your quality requirements.