GPT-5.4 Pro OpenAI 1024000
💰 Total Cost Calculation (from Plugin)
Output: $0.135000 (rounded ~ $0.14)
Output: $0.135000 (rounded ~ $0.14)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 20,000 input tokens and 1,500 output tokens:
- Input Cost: $0.300000
- Output Cost: $0.135000 (rounded ~ $0.14)
- Total Cost: $0.300000
- Cost per 1K tokens: $0.013953 (rounded ~ $0.01)
- Tokens per dollar: 71,667 tokens
- Context Window: 1024000 tokens
Speed & Performance Analysis
With a processing speed of 350 tokens per second and 250ms time to first token:
- Processing Time: 1 minute, 5.91 seconds
- Latency: 250 milliseconds to first token
- Base Throughput: 350 tokens/second
- Effective Throughput: 327 tokens/second (temperature-adjusted)
Best Use Cases
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This calculator shows the math for GPT-5.4 Pro. 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 →Claude Opus 4.8 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.009375
Output: $0.009375
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 20,000 input tokens and 1,500 output tokens:
- Input Cost: $0.025000 (rounded ~ $0.03)
- Output Cost: $0.009375
- Total Cost: $0.023125 (rounded ~ $0.02)
- Cost per 1K tokens: $0.001076
- Tokens per dollar: 929,730 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 280 tokens per second and 380ms time to first token:
- Processing Time: 1 minute, 22.34 seconds
- Latency: 380 milliseconds to first token
- Base Throughput: 280 tokens/second
- Effective Throughput: 262 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Claude Opus 4.8. 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 Pro| Rank | AI Model & Provider | Total Cost | vs GPT-5.4 Pro | vs Claude Opus 4.8 |
|---|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.000388 Best Value | ↓ 99.9% cheaper | ↓ 98.3% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.001250 | ↓ 99.6% cheaper | ↓ 94.6% cheaper |
| 🥉 |
Gemini 3.5 Flash-Lite
Google
|
$0.001763 | ↓ 99.4% cheaper | ↓ 92.4% cheaper |
| #4 |
Gemini 2.5 Flash
Google
|
$0.001763 | ↓ 99.4% cheaper | ↓ 92.4% cheaper |
| #5 |
Mistral Large 3
Mistral AI
|
$0.001938 | ↓ 99.4% cheaper | ↓ 91.6% cheaper |
| #6 |
Gemini 3.8 Flash
Google
|
$0.003469 | ↓ 98.8% cheaper | ↓ 85% cheaper |
| #7 |
GPT-5.4 mini
OpenAI
|
$0.003750 | ↓ 98.8% cheaper | ↓ 83.8% cheaper |
| #8 |
o4-mini Deep Research
OpenAI
|
$0.004250 | ↓ 98.6% cheaper | ↓ 81.6% cheaper |
| #9 |
Claude Haiku 4.5
Anthropic
|
$0.004625 | ↓ 98.5% cheaper | ↓ 80% cheaper |
| #10 |
o4-mini
OpenAI
|
$0.004675 | ↓ 98.4% cheaper | ↓ 79.8% cheaper |
| #11 |
Gemini 3.1 Flash
Google
|
$0.005000 (rounded ~ $0.01) | ↓ 98.3% cheaper | ↓ 78.4% cheaper |
| #12 |
GPT-5.6 Luna
OpenAI
|
$0.005000 (rounded ~ $0.01) | ↓ 98.3% cheaper | ↓ 78.4% cheaper |
| #13 |
Gemini 3.6 Flash
Google
|
$0.006938 (rounded ~ $0.01) | ↓ 97.7% cheaper | ↓ 70% cheaper |
| #14 |
Gemini 3.5 Flash
Google
|
$0.007500 (rounded ~ $0.01) | ↓ 97.5% cheaper | ↓ 67.6% cheaper |
| #15 |
Claude Sonnet 5
Anthropic
|
$0.009250 | ↓ 96.9% cheaper | ↓ 60% cheaper |
| #16 |
GPT-5.3 Codex Spark
OpenAI
|
$0.010063 | ↓ 96.6% cheaper | ↓ 56.5% cheaper |
| #17 |
GPT-5.3 Instant
OpenAI
|
$0.010063 | ↓ 96.6% cheaper | ↓ 56.5% cheaper |
| #18 |
GPT-5.6 Terra
OpenAI
|
$0.012500 (rounded ~ $0.01) | ↓ 95.8% cheaper | ↓ 45.9% cheaper |
| #19 |
Claude Sonnet 4.6
Anthropic
|
$0.013875 (rounded ~ $0.01) | ↓ 95.4% cheaper | ↓ 40% cheaper |
| #20 |
Grok 4.3
xAI
|
$0.014000 (rounded ~ $0.01) | ↓ 95.3% cheaper | ↓ 39.5% cheaper |
| #21 |
Grok 4.20 Beta
xAI
|
$0.014000 (rounded ~ $0.01) | ↓ 95.3% cheaper | ↓ 39.5% cheaper |
| #22 |
Gemini 2.5 Pro
Google
|
$0.014375 (rounded ~ $0.01) | ↓ 95.2% cheaper | ↓ 37.8% cheaper |
| #23 |
Gemini 3.1 Pro
Google
|
$0.020000 | ↓ 93.3% cheaper | ↓ 13.5% cheaper |
| #24 |
Claude Opus 4.7
Anthropic
|
$0.023125 (rounded ~ $0.02) | ↓ 92.3% cheaper | Same price |
| #25 |
Claude Opus 5
Anthropic
|
$0.023125 (rounded ~ $0.02) | ↓ 92.3% cheaper | Same price |
| #26 |
Claude Opus 4.8
Anthropic
|
$0.023125 (rounded ~ $0.02) | ↓ 92.3% cheaper | Same price |
| #27 |
Claude Opus 4.6
Anthropic
|
$0.023125 (rounded ~ $0.02) | ↓ 92.3% cheaper | Same price |
| #28 |
GPT-5.4
OpenAI
|
$0.025000 (rounded ~ $0.03) | ↓ 91.7% cheaper | ↑ 8.1% more |
| #29 |
GPT-5.4 Thinking
OpenAI
|
$0.025000 (rounded ~ $0.03) | ↓ 91.7% cheaper | ↑ 8.1% more |
| #30 |
GPT-5.5 Instant
OpenAI
|
$0.025000 (rounded ~ $0.03) | ↓ 91.7% cheaper | ↑ 8.1% more |
| #31 |
GPT-5.6 Sol
OpenAI
|
$0.025000 (rounded ~ $0.03) | ↓ 91.7% cheaper | ↑ 8.1% more |
| #32 |
o3 Deep Research
OpenAI
|
$0.042500 (rounded ~ $0.04) | ↓ 85.8% cheaper | ↑ 83.8% more |
| #33 |
Claude Fable 5.1
Anthropic
|
$0.044375 (rounded ~ $0.04) | ↓ 85.2% cheaper | ↑ 91.9% more |
| #34 |
Claude Mythos 5.1
Anthropic
|
$0.044375 (rounded ~ $0.04) | ↓ 85.2% cheaper | ↑ 91.9% more |
| #35 |
Claude Fable 5
Anthropic
|
$0.046250 (rounded ~ $0.05) | ↓ 84.6% cheaper | ↑ 100% more |
| #36 |
Claude Mythos 5
Anthropic
|
$0.046250 (rounded ~ $0.05) | ↓ 84.6% cheaper | ↑ 100% more |
| #37 |
GPT-5.5
OpenAI
|
$0.050000 | ↓ 83.3% cheaper | ↑ 116.2% more |
| #38 |
o3 Pro
OpenAI
|
$0.085000 (rounded ~ $0.09) | ↓ 71.7% cheaper | ↑ 267.6% more |
| #39 |
GPT-6 Astra
OpenAI
|
$0.092500 (rounded ~ $0.09) | ↓ 69.2% cheaper | ↑ 300% more |
| #40 |
GPT-6 Astra
OpenAI
|
$0.092500 (rounded ~ $0.09) | ↓ 69.2% cheaper | ↑ 300% 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
GPT-5.4 mini OpenAI
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
Claude Sonnet 4.6 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
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-6 Astra OpenAI
For enterprise tutoring platforms managing 100 million tokens monthly, selecting the right model requires balancing high-fidelity reasoning with cost-effective throughput. When processing 20K-token 30-minute sessions, the choice between Claude Opus 4.8 and GPT-5.4 Pro often comes down to the specific nature of your tutoring interactions. Claude Opus 4.8 excels in long-context coherence, making it an excellent choice for maintaining student-teacher continuity across lengthy, complex educational modules where nuance and extended memory of previous interactions are paramount. Its architecture is particularly adept at handling multi-step reasoning, ensuring that pedagogical feedback remains grounded and consistent throughout a session.
Conversely, GPT-5.4 Pro offers robust tool integration and rapid reasoning capabilities, which are essential for platforms that rely on dynamic curriculum adjustment or real-time SQL data generation to tailor lessons on the fly. For a data analyst, the decision often hinges on how the model handles structured data outputs. GPT-5.4 Pro’s tool-calling efficiency frequently simplifies the extraction of student progress metrics, which can be critical for dashboarding performance. However, teams should consider the trade-offs in latency. While both models support large context windows, the overhead of managing state in a high-volume pipeline—where hundreds of concurrent tutors generate millions of tokens daily—demands rigorous testing of both models against your specific system prompts. We recommend profiling these models using your baseline 20K-token session logs to determine which architecture minimizes the need for complex, token-heavy prompt engineering.