GPT-5.4 mini OpenAI
💰 Total Cost Calculation (from Plugin)
Output: $0.016875 (rounded ~ $0.02)
Output: $0.016875 (rounded ~ $0.02)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 15,000 output tokens:
- Input Cost: $0.018750 (rounded ~ $0.02)
- Output Cost: $0.016875 (rounded ~ $0.02)
- Total Cost: $0.031406 (rounded ~ $0.03)
- Cost per 1K tokens: $0.000273
- Tokens per dollar: 3,661,692 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 minutes, 57.08 seconds
- Latency: 180 milliseconds to first token
- Base Throughput: 500 tokens/second
- Effective Throughput: 485 tokens/second (temperature-adjusted)
Best Use Cases
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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.
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💰 Total Cost Calculation (from Plugin)
Output: $0.005625 (rounded ~ $0.01)
Output: $0.005625 (rounded ~ $0.01)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 100,000 input tokens and 15,000 output tokens:
- Input Cost: $0.006250 (rounded ~ $0.01)
- Output Cost: $0.005625 (rounded ~ $0.01)
- Total Cost: $0.010469
- Cost per 1K tokens: $0.000091
- Tokens per dollar: 10,985,075 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 1,000 tokens per second and 80ms time to first token:
- Processing Time: 1 minute, 58.63 seconds
- Latency: 80 milliseconds to first token
- Base Throughput: 1,000 tokens/second
- Effective Throughput: 971 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Gemini 3.1 Flash Lite. Your decision needs more — current infrastructure, compliance requirements, actual workload patterns, volume tiers — that change which model is right for you.
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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 | vs Gemini 3.1 Flash Lite |
|---|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.003063 Best Value | ↓ 90.2% cheaper | ↓ 70.7% cheaper |
| 🥈 |
Devstral Small 2
Mistral AI
|
$0.003063 | ↓ 90.2% cheaper | ↓ 70.7% cheaper |
| 🥉 |
Nemotron 3 Super
NVIDIA
|
$0.008888 (rounded ~ $0.01) | ↓ 71.7% cheaper | ↓ 15.1% cheaper |
| #4 |
Gemini 3.1 Flash Lite
Google
|
$0.010469 | ↓ 66.7% cheaper | Same price |
| #5 |
Devstral 2
Mistral AI
|
$0.011125 (rounded ~ $0.01) | ↓ 64.6% cheaper | ↑ 6.3% more |
| #6 |
Gemini 3.5 Flash-Lite
Google
|
$0.015188 (rounded ~ $0.02) | ↓ 51.6% cheaper | ↑ 45.1% more |
| #7 |
Gemini 2.5 Flash
Google
|
$0.015188 (rounded ~ $0.02) | ↓ 51.6% cheaper | ↑ 45.1% more |
| #8 |
Mistral Large 3
Mistral AI
|
$0.015313 (rounded ~ $0.02) | ↓ 51.2% cheaper | ↑ 46.3% more |
| #9 |
Gemini 3.8 Flash
Google
|
$0.028594 (rounded ~ $0.03) | ↓ 9% cheaper | ↑ 173.1% more |
| #10 |
o4-mini Deep Research
OpenAI
|
$0.034375 (rounded ~ $0.03) | ↑ 9.5% more | ↑ 228.4% more |
| #11 |
o4-mini
OpenAI
|
$0.037813 (rounded ~ $0.04) | ↑ 20.4% more | ↑ 261.2% more |
| #12 |
Claude Haiku 4.5
Anthropic
|
$0.038125 (rounded ~ $0.04) | ↑ 21.4% more | ↑ 264.2% more |
| #13 |
Gemini 3.1 Flash
Google
|
$0.041875 (rounded ~ $0.04) | ↑ 33.3% more | ↑ 300% more |
| #14 |
GPT-5.6 Luna
OpenAI
|
$0.041875 (rounded ~ $0.04) | ↑ 33.3% more | ↑ 300% more |
| #15 |
Gemini 3.6 Flash
Google
|
$0.057188 (rounded ~ $0.06) | ↑ 82.1% more | ↑ 446.3% more |
| #16 |
Magistral Medium
Mistral AI
|
$0.057500 (rounded ~ $0.06) | ↑ 83.1% more | ↑ 449.3% more |
| #17 |
Gemini 3.5 Flash
Google
|
$0.062813 (rounded ~ $0.06) | ↑ 100% more | ↑ 500% more |
| #18 |
Claude Sonnet 5
Anthropic
|
$0.076250 (rounded ~ $0.08) | ↑ 142.8% more | ↑ 628.4% more |
| #19 |
GPT-5.3 Codex Spark
OpenAI
|
$0.086406 (rounded ~ $0.09) | ↑ 175.1% more | ↑ 725.4% more |
| #20 |
GPT-5.3 Instant
OpenAI
|
$0.086406 (rounded ~ $0.09) | ↑ 175.1% more | ↑ 725.4% more |
| #21 |
GPT-5.6 Terra
OpenAI
|
$0.104688 (rounded ~ $0.10) | ↑ 233.3% more | ↑ 900% more |
| #22 |
Grok 4.3
xAI
|
$0.107500 (rounded ~ $0.11) | ↑ 242.3% more | ↑ 926.9% more |
| #23 |
Grok 4.20 Beta
xAI
|
$0.107500 (rounded ~ $0.11) | ↑ 242.3% more | ↑ 926.9% more |
| #24 |
Claude Sonnet 4.6
Anthropic
|
$0.114375 (rounded ~ $0.11) | ↑ 264.2% more | ↑ 992.5% more |
| #25 |
Gemini 2.5 Pro
Google
|
$0.123438 (rounded ~ $0.12) | ↑ 293% more | ↑ 1079.1% more |
| #26 |
Gemini 3.1 Pro
Google
|
$0.167500 (rounded ~ $0.17) | ↑ 433.3% more | ↑ 1500% more |
| #27 |
Claude Opus 4.7
Anthropic
|
$0.190625 | ↑ 507% more | ↑ 1720.9% more |
| #28 |
Claude Opus 5
Anthropic
|
$0.190625 | ↑ 507% more | ↑ 1720.9% more |
| #29 |
Claude Opus 4.8
Anthropic
|
$0.190625 | ↑ 507% more | ↑ 1720.9% more |
| #30 |
Claude Opus 4.6
Anthropic
|
$0.190625 | ↑ 507% more | ↑ 1720.9% more |
| #31 |
GPT-5.4
OpenAI
|
$0.209375 | ↑ 566.7% more | ↑ 1900% more |
| #32 |
GPT-5.4 Thinking
OpenAI
|
$0.209375 | ↑ 566.7% more | ↑ 1900% more |
| #33 |
GPT-5.5 Instant
OpenAI
|
$0.209375 | ↑ 566.7% more | ↑ 1900% more |
| #34 |
GPT-5.6 Sol
OpenAI
|
$0.209375 | ↑ 566.7% more | ↑ 1900% more |
| #35 |
o3 Deep Research
OpenAI
|
$0.343750 (rounded ~ $0.34) | ↑ 994.5% more | ↑ 3183.6% more |
| #36 |
Claude Fable 5.1
Anthropic
|
$0.376563 (rounded ~ $0.38) | ↑ 1099% more | ↑ 3497% more |
| #37 |
Claude Mythos 5.1
Anthropic
|
$0.376563 (rounded ~ $0.38) | ↑ 1099% more | ↑ 3497% more |
| #38 |
Claude Fable 5
Anthropic
|
$0.381250 (rounded ~ $0.38) | ↑ 1113.9% more | ↑ 3541.8% more |
| #39 |
Claude Mythos 5
Anthropic
|
$0.381250 (rounded ~ $0.38) | ↑ 1113.9% more | ↑ 3541.8% more |
| #40 |
GPT-5.5
OpenAI
|
$0.418750 (rounded ~ $0.42) | ↑ 1233.3% more | ↑ 3900% more |
| #41 |
o3 Pro
OpenAI
|
$0.687500 (rounded ~ $0.69) | ↑ 2089.1% more | ↑ 6467.2% more |
| #42 |
GPT-6 Astra
OpenAI
|
$0.762500 (rounded ~ $0.76) | ↑ 2327.9% more | ↑ 7183.6% more |
| #43 |
GPT-5.2 Pro
OpenAI
|
$1.036875 (rounded ~ $1.04) | ↑ 3201.5% more | ↑ 9804.5% more |
| #44 |
GPT-5.2 Pro
OpenAI
|
$1.036875 (rounded ~ $1.04) | ↑ 3201.5% more | ↑ 9804.5% more |
Mistral Small 3 Mistral AI
Devstral Small 2 Mistral AI
Nemotron 3 Super NVIDIA
Gemini 3.1 Flash Lite Google
Devstral 2 Mistral AI
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
o4-mini OpenAI
Claude Haiku 4.5 Anthropic
Gemini 3.1 Flash Google
GPT-5.6 Luna OpenAI
Gemini 3.6 Flash Google
Magistral Medium Mistral AI
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
Grok 4.3 xAI
Grok 4.20 Beta xAI
Claude Sonnet 4.6 Anthropic
Gemini 2.5 Pro Google
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
Evaluating Efficiency in Medical Document Structuring
In the realm of clinical note generation, the move from unstructured doctor’s notes to formalized medical records requires high precision in information extraction. When comparing GPT-5.4 mini and Gemini 3.1 Flash Lite for a 100K-token workload, researchers must weigh the trade-offs between logic density and multimodal flexibility. For an indie developer or a PhD student building a clinical MVP, these ‘mini’ tier models offer a sophisticated balance of performance and resource management, especially when processing high volumes of patient encounters daily.
- GPT-5.4 mini: This model is particularly strong at following rigid formatting instructions, such as adhering to specific ICD-11 coding standards or strict SOAP note structures. Its reasoning capabilities are finely tuned for identifying contradictions within a single document, making it a reliable choice for quality assurance in clinical records.
- Gemini 3.1 Flash Lite: Google’s offering shines in its handling of long-context nuances and its ability to integrate with broader ecosystem tools. If the clinical note generation involves referencing previous patient history or dense lab results across a larger context, this model’s architectural efficiency provides a smoother experience for the end-user.
The decision between these two often comes down to the specific ‘flavor’ of the clinical data. If the primary task is logical deduction and entity extraction from short, dense dictations, the OpenAI model’s instruction-following is top-tier. Conversely, if the researcher plans to expand the tool to include visual analysis of charts or scans alongside the text, Gemini’s native multimodal roots offer a more scalable path forward for a growing medical informatics project.