GPT-5.4 Thinking OpenAI 1024000
💰 Total Cost Calculation (from Plugin)
Output: $0.015000 (rounded ~ $0.02)
Output: $0.015000 (rounded ~ $0.02)
Unit: $0.000000
Fees: $0.000000
Detailed Cost Analysis (from Plugin)
For 200,000 input tokens and 1,000 output tokens:
- Input Cost: $0.500000
- Output Cost: $0.015000 (rounded ~ $0.02)
- Total Cost: $0.290000
- Cost per 1K tokens: $0.001443
- Tokens per dollar: 693,103 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: 8 minutes, 57.86 seconds
- Latency: 220 milliseconds to first token
- Base Throughput: 400 tokens/second
- Effective Throughput: 374 tokens/second (temperature-adjusted)
Best Use Cases
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← Back to GPT-5.4 Thinking| Rank | AI Model & Provider | Total Cost | vs GPT-5.4 Thinking |
|---|---|---|---|
| 🏆 |
Grok Code Fast 1
xAI
|
$0.023500 (rounded ~ $0.02) Best Value | ↓ 91.9% cheaper |
| 🥈 |
Gemini 3.1 Flash Lite
Google
|
$0.029000 | ↓ 90% cheaper |
| 🥉 |
Gemini 2.5 Flash
Google
|
$0.035500 (rounded ~ $0.04) | ↓ 87.8% cheaper |
| #4 |
Mistral Large 3
Mistral AI
|
$0.056500 (rounded ~ $0.06) | ↓ 80.5% cheaper |
| #5 |
Kimi K2.5
Moonshot AI
|
$0.073200 (rounded ~ $0.07) | ↓ 74.8% cheaper |
| #6 |
GPT-5.4 mini
OpenAI
|
$0.087000 (rounded ~ $0.09) | ↓ 70% cheaper |
| #7 |
Claude Haiku 4.5
Anthropic
|
$0.115000 (rounded ~ $0.12) | ↓ 60.3% cheaper |
| #8 |
Kimi K2.6
Moonshot AI
|
$0.115150 (rounded ~ $0.12) | ↓ 60.3% cheaper |
| #9 |
Gemini 3.1 Flash
Google
|
$0.116000 (rounded ~ $0.12) | ↓ 60% cheaper |
| #10 |
Grok 4.3
xAI
|
$0.140000 | ↓ 51.7% cheaper |
| #11 |
Gemini 3.5 Flash
Google
|
$0.174000 (rounded ~ $0.17) | ↓ 40% cheaper |
| #12 |
Grok 4.20 Beta
xAI
|
$0.226000 (rounded ~ $0.23) | ↓ 22.1% cheaper |
| #13 |
GPT-5.4
OpenAI
|
$0.290000 | Same price |
| #14 |
Gemini 2.5 Pro
Google
|
$0.290000 | Same price |
| #15 |
Claude Sonnet 4.6
Anthropic
|
$0.345000 (rounded ~ $0.35) | ↑ 19% more |
| #16 |
Gemini 3.1 Pro
Google
|
$0.458000 (rounded ~ $0.46) | ↑ 57.9% more |
| #17 |
Claude Opus 4.7
Anthropic
|
$0.575000 (rounded ~ $0.58) | ↑ 98.3% more |
| #18 |
Claude Opus 4.8
Anthropic
|
$0.575000 (rounded ~ $0.58) | ↑ 98.3% more |
| #19 |
Claude Opus 4.6
Anthropic
|
$0.575000 (rounded ~ $0.58) | ↑ 98.3% more |
| #20 |
GPT-5.5 Instant
OpenAI
|
$0.580000 | ↑ 100% more |
| #21 |
GPT-5.5
OpenAI
|
$1.145000 (rounded ~ $1.15) | ↑ 294.8% more |
| #22 |
GPT-5.5
OpenAI
|
$1.145000 (rounded ~ $1.15) | ↑ 294.8% more |
Grok Code Fast 1 xAI
Gemini 3.1 Flash Lite Google
Gemini 2.5 Flash Google
Mistral Large 3 Mistral AI
Kimi K2.5 Moonshot AI
GPT-5.4 mini OpenAI
Claude Haiku 4.5 Anthropic
Kimi K2.6 Moonshot AI
Gemini 3.1 Flash Google
Grok 4.3 xAI
Gemini 3.5 Flash Google
Grok 4.20 Beta xAI
GPT-5.4 OpenAI
Gemini 2.5 Pro Google
Claude Sonnet 4.6 Anthropic
Gemini 3.1 Pro Google
Claude Opus 4.7 Anthropic
Claude Opus 4.8 Anthropic
Claude Opus 4.6 Anthropic
GPT-5.5 Instant OpenAI
GPT-5.5 OpenAI
GPT-5.5 OpenAI
VoiceAIengineersbuildingmulti-agentorchestratorsfaceadifficulttrade-offbetweenreasoningdepthandsystemlatency[1.1]. When managing 200,000 tokens per request for complex workflows, selecting a model with strong native reasoning is critical. GPT-5.4 Thinking excels here by providing deterministic, high-quality logic that reduces the need for expensive, multi-step error correction. This model is particularly effective for the orchestrator role, which must parse user intent, manage state, and delegate sub-tasks without breaking the flow of a sub-second voice interaction. Unlike generic text models, this variant is designed to handle the unpredictable nature of voice conversations—including interruptions and mid-sentence corrections—by maintaining a consistent execution context. By centralizing complex reasoning in the orchestrator, engineers can keep worker agents lightweight and faster, optimizing the overall pipeline. While reasoning models incur higher per-unit costs, the reduction in failed turns and agent-handoff errors often results in lower total cost-of-ownership for high-volume deployments. For teams prioritizing reliable orchestration over raw throughput speed, this model provides the necessary guardrails to maintain conversation integrity. It is best suited for the main decision-making layer of an agentic voice system where error recovery is prohibitively expensive.