GPT-6 Luna vs Grok 4.3
Compare GPT-6 Luna vs Grok 4.3 API pricing, token costs, context windows, and latency. See which model is cheaper and better for your stack.
GPT-6 Luna charges $0.10/1M input and $0.50/1M output tokens, compared to Grok 4.3 at $1.25/1M input and $2.50/1M output. GPT-6 Luna is 12.5x cheaper on input tokens. In reasoning benchmarks, GPT-6 Luna holds the higher AA Intelligence Index rating. For latency-critical streaming, GPT-6 Luna delivers 103 t/s (77.0s TTFT) while Grok 4.3 provides 79 t/s (23.0s TTFT).
Full Metric & Specification Comparison
| Metric | GPT-6 Luna | Grok 4.3 | Advantage |
|---|---|---|---|
| Provider | OpenAI | xAI | - |
| Input Token Cost (1M) | $0.100 | $1.250 | GPT-6 Luna |
| Output Token Cost (1M) | $0.500 | $2.500 | GPT-6 Luna |
| Context Window | 1,050k tokens | 1,000k tokens | GPT-6 Luna |
| AA Intelligence Index | 28/100 | Unrated | Unrated |
| Streaming Latency | Fast | Moderate | - |
Key Architectural Takeaways
- Input Token Cost: GPT-6 Luna is $0.10/1M vs Grok 4.3 at $1.25/1M
- Output Token Cost: GPT-6 Luna is $0.50/1M vs Grok 4.3 at $2.50/1M
- Context Window: GPT-6 Luna supports 1,050k tokens vs Grok 4.3 with 1,000k tokens
- Benchmark Intelligence Rating: not rated for one or both models
- Inference Latency: GPT-6 Luna is rated Fast vs Grok 4.3 rated Moderate
Calculate Your Team's Exact Bill Difference
Drag the sliders below to see real-time cost divergences based on your daily token consumption.
Deploying GPT-6 Luna over Grok 4.3 saves $32.25/mo ($387/yr) at this volume.
Frequently Asked Questions
Is GPT-6 Luna cheaper than Grok 4.3?
GPT-6 Luna is significantly more economical, charging $0.10/1M input compared to $1.25/1M for Grok 4.3.
Which model should I choose for production?
If your primary constraint is API cost and high query throughput, choose GPT-6 Luna. If you require peak reasoning accuracy and complex tool use, GPT-6 Luna is recommended.
How does context window compare between GPT-6 Luna and Grok 4.3?
GPT-6 Luna offers the larger context window (1,050k tokens), allowing you to pass significantly larger codebases or documents without chunking.
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