Claude Opus 4.7 Anthropic 1000000
💰 Total Cost Calculation (from Plugin)
Output: $0.093750 (rounded ~ $0.09)
Output: $0.093750 (rounded ~ $0.09)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 15,000 output tokens:
- Input Cost: $0.062500 (rounded ~ $0.06)
- Output Cost: $0.093750 (rounded ~ $0.09)
- Total Cost: $0.133750 (rounded ~ $0.13)
- Cost per 1K tokens: $0.002058
- Tokens per dollar: 485,981 tokens
- Context Window: 1000000 tokens
Speed & Performance Analysis
With a processing speed of 260 tokens per second and 400ms time to first token:
- Processing Time: 4 minutes, 17.68 seconds
- Latency: 400 milliseconds to first token
- Base Throughput: 260 tokens/second
- Effective Throughput: 252 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for Claude Opus 4.7. 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 →GPT-5.4 Thinking OpenAI 1024000
💰 Total Cost Calculation (from Plugin)
Output: $0.112500 (rounded ~ $0.11)
Output: $0.112500 (rounded ~ $0.11)
Unit: $0.000000
Fees: $0.000000
Advanced Cost Breakdown (from Plugin)
Detailed Cost Analysis (from Plugin)
For 50,000 input tokens and 15,000 output tokens:
- Input Cost: $0.062500 (rounded ~ $0.06)
- Output Cost: $0.112500 (rounded ~ $0.11)
- Total Cost: $0.152500 (rounded ~ $0.15)
- Cost per 1K tokens: $0.002346
- Tokens per dollar: 426,230 tokens
- Context Window: 1024000 tokens
Speed & Performance Analysis
With a processing speed of 400 tokens per second and 220ms time to first token:
- Processing Time: 2 minutes, 47.56 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 388 tokens/second (temperature-adjusted)
Best Use Cases
Want this applied to YOUR actual stack?
This calculator shows the math for GPT-5.4 Thinking. 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 Claude Opus 4.7| Rank | AI Model & Provider | Total Cost | vs Claude Opus 4.7 | vs GPT-5.4 Thinking |
|---|---|---|---|---|
| 🏆 |
Mistral Small 3
Mistral AI
|
$0.001925 Best Value | ↓ 98.6% cheaper | ↓ 98.7% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.007625 (rounded ~ $0.01) | ↓ 94.3% cheaper | ↓ 95% cheaper |
| 🥉 |
Mistral Large 3
Mistral AI
|
$0.009625 | ↓ 92.8% cheaper | ↓ 93.7% cheaper |
| #4 |
Gemini 3.5 Flash-Lite
Google
|
$0.011775 (rounded ~ $0.01) | ↓ 91.2% cheaper | ↓ 92.3% cheaper |
| #5 |
Gemini 2.5 Flash
Google
|
$0.011775 (rounded ~ $0.01) | ↓ 91.2% cheaper | ↓ 92.3% cheaper |
| #6 |
Gemini 3.8 Flash
Google
|
$0.020063 | ↓ 85% cheaper | ↓ 86.8% cheaper |
| #7 |
GPT-5.4 mini
OpenAI
|
$0.022875 (rounded ~ $0.02) | ↓ 82.9% cheaper | ↓ 85% cheaper |
| #8 |
o4-mini Deep Research
OpenAI
|
$0.023000 (rounded ~ $0.02) | ↓ 82.8% cheaper | ↓ 84.9% cheaper |
| #9 |
o4-mini
OpenAI
|
$0.025300 (rounded ~ $0.03) | ↓ 81.1% cheaper | ↓ 83.4% cheaper |
| #10 |
Claude Haiku 4.5
Anthropic
|
$0.026750 (rounded ~ $0.03) | ↓ 80% cheaper | ↓ 82.5% cheaper |
| #11 |
Gemini 3.1 Flash
Google
|
$0.030500 | ↓ 77.2% cheaper | ↓ 80% cheaper |
| #12 |
GPT-5.6 Luna
OpenAI
|
$0.030500 | ↓ 77.2% cheaper | ↓ 80% cheaper |
| #13 |
Gemini 3.6 Flash
Google
|
$0.040125 | ↓ 70% cheaper | ↓ 73.7% cheaper |
| #14 |
Gemini 3.5 Flash
Google
|
$0.045750 (rounded ~ $0.05) | ↓ 65.8% cheaper | ↓ 70% cheaper |
| #15 |
Claude Sonnet 5
Anthropic
|
$0.053500 (rounded ~ $0.05) | ↓ 60% cheaper | ↓ 64.9% cheaper |
| #16 |
Grok 4.3
xAI
|
$0.062000 (rounded ~ $0.06) | ↓ 53.6% cheaper | ↓ 59.3% cheaper |
| #17 |
Grok 4.20 Beta
xAI
|
$0.062000 (rounded ~ $0.06) | ↓ 53.6% cheaper | ↓ 59.3% cheaper |
| #18 |
GPT-5.3 Codex Spark
OpenAI
|
$0.066500 (rounded ~ $0.07) | ↓ 50.3% cheaper | ↓ 56.4% cheaper |
| #19 |
GPT-5.3 Instant
OpenAI
|
$0.066500 (rounded ~ $0.07) | ↓ 50.3% cheaper | ↓ 56.4% cheaper |
| #20 |
GPT-5.6 Terra
OpenAI
|
$0.076250 (rounded ~ $0.08) | ↓ 43% cheaper | ↓ 50% cheaper |
| #21 |
Claude Sonnet 4.6
Anthropic
|
$0.080250 | ↓ 40% cheaper | ↓ 47.4% cheaper |
| #22 |
Gemini 2.5 Pro
Google
|
$0.095000 (rounded ~ $0.10) | ↓ 29% cheaper | ↓ 37.7% cheaper |
| #23 |
Gemini 3.1 Pro
Google
|
$0.122000 (rounded ~ $0.12) | ↓ 8.8% cheaper | ↓ 20% cheaper |
| #24 |
Claude Opus 5
Anthropic
|
$0.133750 (rounded ~ $0.13) | Same price | ↓ 12.3% cheaper |
| #25 |
Claude Opus 4.8
Anthropic
|
$0.133750 (rounded ~ $0.13) | Same price | ↓ 12.3% cheaper |
| #26 |
Claude Opus 4.6
Anthropic
|
$0.133750 (rounded ~ $0.13) | Same price | ↓ 12.3% cheaper |
| #27 |
GPT-5.4
OpenAI
|
$0.152500 (rounded ~ $0.15) | ↑ 14% more | Same price |
| #28 |
GPT-5.4 Thinking
OpenAI
|
$0.152500 (rounded ~ $0.15) | ↑ 14% more | Same price |
| #29 |
GPT-5.5 Instant
OpenAI
|
$0.152500 (rounded ~ $0.15) | ↑ 14% more | Same price |
| #30 |
GPT-5.6 Sol
OpenAI
|
$0.152500 (rounded ~ $0.15) | ↑ 14% more | Same price |
| #31 |
o3 Deep Research
OpenAI
|
$0.230000 | ↑ 72% more | ↑ 50.8% more |
| #32 |
Claude Fable 5.1
Anthropic
|
$0.263750 (rounded ~ $0.26) | ↑ 97.2% more | ↑ 73% more |
| #33 |
Claude Mythos 5.1
Anthropic
|
$0.263750 (rounded ~ $0.26) | ↑ 97.2% more | ↑ 73% more |
| #34 |
Claude Fable 5
Anthropic
|
$0.267500 (rounded ~ $0.27) | ↑ 100% more | ↑ 75.4% more |
| #35 |
Claude Mythos 5
Anthropic
|
$0.267500 (rounded ~ $0.27) | ↑ 100% more | ↑ 75.4% more |
| #36 |
GPT-5.5
OpenAI
|
$0.305000 (rounded ~ $0.31) | ↑ 128% more | ↑ 100% more |
| #37 |
o3 Pro
OpenAI
|
$0.460000 | ↑ 243.9% more | ↑ 201.6% more |
| #38 |
GPT-6 Astra
OpenAI
|
$0.535000 (rounded ~ $0.54) | ↑ 300% more | ↑ 250.8% more |
| #39 |
GPT-5.2 Pro
OpenAI
|
$0.798000 (rounded ~ $0.80) | ↑ 496.6% more | ↑ 423.3% more |
| #40 |
GPT-5.2 Pro
OpenAI
|
$0.798000 (rounded ~ $0.80) | ↑ 496.6% more | ↑ 423.3% more |
Mistral Small 3 Mistral AI
Gemini 3.1 Flash Lite Google
Mistral Large 3 Mistral AI
Gemini 3.5 Flash-Lite Google
Gemini 2.5 Flash Google
Gemini 3.8 Flash Google
GPT-5.4 mini OpenAI
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
Gemini 3.5 Flash Google
Claude Sonnet 5 Anthropic
Grok 4.3 xAI
Grok 4.20 Beta xAI
GPT-5.3 Codex Spark OpenAI
GPT-5.3 Instant OpenAI
GPT-5.6 Terra OpenAI
Claude Sonnet 4.6 Anthropic
Gemini 2.5 Pro Google
Gemini 3.1 Pro Google
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
Choosing the Right Model for Academic Literature Reviews
For mid-market SaaS platforms and research-heavy enterprises managing 50M-token monthly research pipelines, the choice between Claude Opus 4.7 and GPT-5.4 Thinking often comes down to the specific nature of your literature review workflow. Both models excel at synthesizing dense academic text, yet they differ significantly in their operational strengths.
Claude Opus 4.7 is frequently preferred for its long-context handling and nuanced synthesis. Its ability to maintain coherence across massive document sets makes it ideal for literature reviews where you need to track themes across 50+ papers simultaneously. It is particularly adept at maintaining academic tone and structural integrity in long-form drafting, reducing the need for extensive post-generation editing.
GPT-5.4 Thinking, by contrast, shines in reasoning-heavy tasks. If your research workflow involves complex comparative analysis, hypothesis testing, or identifying contradictions between conflicting academic studies, its reasoning chain provides a higher degree of verification. It is often the better choice when the drafting task requires high-level inference or complex, multi-step problem solving rather than just thematic summary.
When deploying these models at scale, consider your primary bottleneck. If your team spends more time organizing and synthesizing information, the contextual awareness of Claude is a significant advantage. If your team focuses on critical analysis and rigorous argument verification, the specialized reasoning pathways in GPT-5.4 Thinking often yield more robust results for high-stakes research.