GPT-5.4 mini OpenAI
💰 Total Cost Calculation (from Plugin)
Output: $0.000563
Output: $0.000563
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 10,000 input tokens and 500 output tokens:
- Input Cost: $0.001875
- Output Cost: $0.000563
- Total Cost: $0.001594
- Cost per 1K tokens: $0.000152
- Tokens per dollar: 6,588,235 tokens
- Context Window: 400000 tokens
Speed & Performance Analysis
With a processing speed of 500 tokens per second and 180ms time to first token:
- Processing Time: 21.60 seconds
- Latency: 180 milliseconds to first token
- Base Throughput: 500 tokens/second
- Effective Throughput: 490 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for GPT-5.4 mini. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
Get a $39 personalized AI Architecture Audit. PDF tailored to your stack, delivered in under 60 seconds. 7-day no-questions-asked refund.
Get my instant AI audit — $39 →✨ Market Recommendations AI Model Registry
← Back to GPT-5.4 mini| Rank | AI Model & Provider | Total Cost | vs GPT-5.4 mini |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.000175 Best Value | ↓ 89% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.000531 | ↓ 66.7% cheaper |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$0.000725 | ↓ 54.5% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.000725 | ↓ 54.5% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.000875 | ↓ 45.1% cheaper |
| #6 |
Gemini 3.8 Flash
Google
|
$0.001500 | ↓ 5.9% cheaper |
| #7 |
o4-mini Deep Research
OpenAI
|
$0.001875 | ↑ 17.6% more |
| #8 |
Claude Haiku 4.5
Anthropic
|
$0.002000 | ↑ 25.5% more |
| #9 |
o4-mini
OpenAI
|
$0.002063 | ↑ 29.4% more |
| #10 |
Gemini 3.1 Flash
Google
|
$0.002125 | ↑ 33.3% more |
| #11 |
GPT-5.6 Luna
OpenAI
|
$0.002125 | ↑ 33.3% more |
| #12 |
Gemini 3.6 Flash
Google
|
$0.003000 | ↑ 88.2% more |
| #13 |
Gemini 3.5 Flash
Google
|
$0.003188 | ↑ 100% more |
| #14 |
Claude Sonnet 5
Anthropic
|
$0.004000 | ↑ 151% more |
| #15 |
GPT-5.3 Codex Spark
OpenAI
|
$0.004156 | ↑ 160.8% more |
| #16 |
GPT-5.3 Instant
OpenAI
|
$0.004156 | ↑ 160.8% more |
| #17 |
GPT-5.6 Terra
OpenAI
|
$0.005313 (rounded ~ $0.01) | ↑ 233.3% more |
| #18 |
Gemini 2.5 Pro
Google
|
$0.005938 (rounded ~ $0.01) | ↑ 272.5% more |
| #19 |
Claude Sonnet 4.6
Anthropic
|
$0.006000 (rounded ~ $0.01) | ↑ 276.5% more |
| #20 |
Grok 4.3
xAI
|
$0.006500 (rounded ~ $0.01) | ↑ 307.8% more |
| #21 |
Grok 4.20 Beta
xAI
|
$0.006500 (rounded ~ $0.01) | ↑ 307.8% more |
| #22 |
Gemini 3.1 Pro
Google
|
$0.008500 (rounded ~ $0.01) | ↑ 433.3% more |
| #23 |
Claude Opus 4.7
Anthropic
|
$0.010000 | ↑ 527.5% more |
| #24 |
Claude Opus 5
Anthropic
|
$0.010000 | ↑ 527.5% more |
| #25 |
Claude Opus 4.8
Anthropic
|
$0.010000 | ↑ 527.5% more |
| #26 |
Claude Opus 4.6
Anthropic
|
$0.010000 | ↑ 527.5% more |
| #27 |
GPT-5.4
OpenAI
|
$0.010625 | ↑ 566.7% more |
| #28 |
GPT-5.4 Thinking
OpenAI
|
$0.010625 | ↑ 566.7% more |
| #29 |
GPT-5.5 Instant
OpenAI
|
$0.010625 | ↑ 566.7% more |
| #30 |
GPT-5.6 Sol
OpenAI
|
$0.010625 | ↑ 566.7% more |
| #31 |
o3 Deep Research
OpenAI
|
$0.018750 (rounded ~ $0.02) | ↑ 1076.5% more |
| #32 |
Claude Fable 5.1
Anthropic
|
$0.019063 | ↑ 1096.1% more |
| #33 |
Claude Mythos 5.1
Anthropic
|
$0.019063 | ↑ 1096.1% more |
| #34 |
Claude Fable 5
Anthropic
|
$0.020000 | ↑ 1154.9% more |
| #35 |
Claude Mythos 5
Anthropic
|
$0.020000 | ↑ 1154.9% more |
| #36 |
GPT-5.5
OpenAI
|
$0.021250 (rounded ~ $0.02) | ↑ 1233.3% more |
| #37 |
o3 Pro
OpenAI
|
$0.037500 (rounded ~ $0.04) | ↑ 2252.9% more |
| #38 |
GPT-6 Astra
OpenAI
|
$0.040000 | ↑ 2409.8% more |
| #39 |
GPT-5.2 Pro
OpenAI
|
$0.049875 | ↑ 3029.4% more |
| #40 |
GPT-5.2 Pro
OpenAI
|
$0.049875 | ↑ 3029.4% more |
Mistral Small 3 Mistral AI
Gemini 3.1 Flash Lite Google
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Gemini 3.8 Flash Google
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
o4-mini OpenAI
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Gemini 3.5 Flash Google
Claude Sonnet 5 Anthropic
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
GPT-5.6 Terra OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 5 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
GPT-5.5 Instant OpenAI
GPT-5.6 Sol OpenAI
o3 Deep Research OpenAI
Claude Fable 5.1 Anthropic
Claude Mythos 5.1 Anthropic
Claude Fable 5 Anthropic
Claude Mythos 5 Anthropic
GPT-5.5 OpenAI
o3 Pro OpenAI
GPT-6 Astra OpenAI
GPT-5.2 Pro OpenAI
GPT-5.2 Pro OpenAI
Optimizing Inline Code Suggestions for High-Volume IDEs
For enterprise-scale code generation, the balance between model speed and reasoning depth is critical. GPT-5.4-mini has emerged as a standout for inline code suggestions, where latency is the primary friction point for developers. Unlike larger, more compute-heavy models that can introduce perceptible delays during autocomplete, this model provides a snappy response time that maintains the developer’s flow state.
In high-volume environments where millions of suggestions are generated monthly, cost-efficiency is paramount. GPT-5.4-mini excels here by offering a compact parameter footprint without sacrificing the contextual awareness needed for multi-line code completions. It effectively manages large codebases by focusing on structural consistency and syntax accuracy, making it ideal for standard refactoring and boilerplate generation tasks.
When deploying at a scale of 100 million tokens per month, the economic advantage of using a dedicated ‘mini’ model becomes clear. However, recruiters and infra teams should note that while it is highly capable for routine coding, it may require occasional escalation to larger reasoning models for complex, multi-file architectural changes. By offloading 90% of standard suggestions to GPT-5.4-mini, engineering teams can significantly reduce overhead while maintaining developer productivity. This model’s integration into agentic workflows is particularly strong, allowing for sub-agent delegation where rapid, iterative changes are required. For platform leads, this represents a reliable, high-throughput solution that balances performance with budget discipline in production environments.