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)
Multimodal Input Details
Resolution: Medium
Tokens: 5,160,000
Cost: $0.000000
Detailed Cost Analysis (from Plugin)
For 1,000 input tokens and 500 output tokens:
- Input Cost: $0.967688 (rounded ~ $0.97)
- Output Cost: $0.000563
- Total Cost: $0.968250 (rounded ~ $0.97)
- Cost per 1K tokens: $0.000188
- Tokens per dollar: 5,330,751 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: 3 hours, 39.33 seconds
- Latency: 180 milliseconds to first token
- Base Throughput: 500 tokens/second
- Effective Throughput: 476 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to GPT-5.4 mini| Rank | AI Model & Provider | Total Cost | vs GPT-5.4 mini |
|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.129063 Best Value | ↓ 86.7% cheaper |
| 🥈 |
Ministral 3 (14B)
Mistral AI
|
$0.258175 (rounded ~ $0.26) | ↓ 73.3% cheaper |
| 🥉 |
Gemini 3.1 Flash Lite
Google
|
$0.322750 (rounded ~ $0.32) | ↓ 66.7% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.387388 (rounded ~ $0.39) | ↓ 60% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.645563 (rounded ~ $0.65) | ↓ 33.3% cheaper |
| #6 |
o4-mini Deep Research
OpenAI
|
$1.290750 | ↑ 33.3% more |
| #7 |
Claude Haiku 4.5
Anthropic
|
$1.290875 | ↑ 33.3% more |
| #8 |
o4-mini
OpenAI
|
$1.419825 | ↑ 46.6% more |
| #9 |
Grok 4.3
xAI
|
$1.613125 (rounded ~ $1.61) | ↑ 66.6% more |
| #10 |
Gemini 3.5 Flash
Google
|
$1.936500 (rounded ~ $1.94) | ↑ 100% more |
| #11 |
GPT-5.3 Codex Spark
OpenAI
|
$2.259688 | ↑ 133.4% more |
| #12 |
GPT-5.3 Instant
OpenAI
|
$2.259688 | ↑ 133.4% more |
| #13 |
Llama 4 Maverick (400B)
Meta AI
|
$2.415450 (rounded ~ $2.42) | ↑ 149.5% more |
| #14 |
Gemini 3.1 Flash
Google
|
$2.582000 (rounded ~ $2.58) | ↑ 166.7% more |
| #15 |
Claude Sonnet 4.6
Anthropic
|
$3.872625 (rounded ~ $3.87) | ↑ 300% more |
| #16 |
Claude Opus 4.7
Anthropic
|
$6.454375 (rounded ~ $6.45) | ↑ 566.6% more |
| #17 |
Claude Opus 4.8
Anthropic
|
$6.454375 (rounded ~ $6.45) | ↑ 566.6% more |
| #18 |
Claude Opus 4.6
Anthropic
|
$6.454375 (rounded ~ $6.45) | ↑ 566.6% more |
| #19 |
Gemini 2.5 Pro
Google
|
$6.455000 (rounded ~ $6.46) | ↑ 566.7% more |
| #20 |
GPT-5.5 Instant
OpenAI
|
$6.455000 (rounded ~ $6.46) | ↑ 566.7% more |
| #21 |
Gemini 3.1 Pro
Google
|
$10.326500 (rounded ~ $10.33) | ↑ 966.5% more |
| #22 |
o3 Deep Research
OpenAI
|
$12.907500 (rounded ~ $12.91) | ↑ 1233.1% more |
| #23 |
GPT-5.4
OpenAI
|
$12.908125 (rounded ~ $12.91) | ↑ 1233.1% more |
| #24 |
GPT-5.4 Thinking
OpenAI
|
$12.908125 (rounded ~ $12.91) | ↑ 1233.1% more |
| #25 |
o3 Pro
OpenAI
|
$25.815000 (rounded ~ $25.82) | ↑ 2566.2% more |
| #26 |
GPT-5.5
OpenAI
|
$25.816250 (rounded ~ $25.82) | ↑ 2566.3% more |
| #27 |
GPT-5.2 Pro
OpenAI
|
$27.116250 (rounded ~ $27.12) | ↑ 2700.5% more |
| #28 |
GPT-5.5 Pro
OpenAI
|
$38.730000 | ↑ 3900% more |
| #29 |
GPT-5.5 Pro
OpenAI
|
$38.730000 | ↑ 3900% more |
Mistral Small 3 Mistral AI
Ministral 3 (14B) Mistral AI
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
o4-mini Deep Research OpenAI
Claude Haiku 4.5 Anthropic
o4-mini OpenAI
Grok 4.3 xAI
Gemini 3.5 Flash Google
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
Llama 4 Maverick (400B) Meta AI
Gemini 3.1 Flash Google
Claude Sonnet 4.6 Anthropic
Claude Opus 4.7 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
Gemini 2.5 Pro Google
GPT-5.5 Instant OpenAI
Gemini 3.1 Pro Google
o3 Deep Research OpenAI
GPT-5.4 OpenAI
GPT-5.4 Thinking OpenAI
o3 Pro OpenAI
GPT-5.5 OpenAI
GPT-5.2 Pro OpenAI
GPT-5.5 Pro OpenAI
GPT-5.5 Pro OpenAI
Scaling Image Captioning with GPT-5.4 mini
Automated image tagging and captioning at scale requires a delicate balance between visual recognition accuracy and throughput efficiency. As your e-commerce catalog or media library grows toward the 10,000 image mark, the operational overhead of running heavy-duty reasoning models becomes unsustainable.
GPT-5.4 mini addresses this specific challenge by offering a streamlined profile optimized for high-volume vision tasks. Unlike larger, more general-purpose models that expend computational resources on deep reasoning chains, this model focuses on rapid visual processing and concise text generation. For product teams shipping AI features where latency directly impacts user experience, the responsiveness of this model is a significant differentiator.
In scenarios involving product catalogs, the model excels at descriptive metadata generation, identifying key attributes from visual inputs without needing complex chain-of-thought prompting. This makes it an ideal workhorse for batch-processing marketing assets. Its efficiency allows for rapid iteration during the A/B testing of your content strategy.
When planning your implementation, consider the consistency of your visual dataset. If your images are uniform—such as consistent e-commerce product shots against white backgrounds—GPT-5.4 mini can be tuned to deliver high-quality, standardized captions across your entire repository. For teams managing massive pipelines, the efficiency gained here allows you to reallocate budget toward more complex, high-stakes conversational agents, ensuring your overall AI strategy remains lean and scalable as you grow.