GPT-6.1 Sol vs GPT-6 Luna
Compare GPT-6.1 Sol vs GPT-6 Luna API pricing, token costs, context windows, and latency. See which model is cheaper and better for your stack.
GPT-6.1 Sol charges $2.00/1M input and $10.00/1M output tokens, compared to GPT-6 Luna at $0.10/1M input and $0.50/1M output. GPT-6 Luna is 20.0x cheaper on input tokens. In reasoning benchmarks, GPT-6.1 Sol holds the higher AA Intelligence Index rating (55/100). For latency-critical streaming, GPT-6.1 Sol delivers 55 t/s (138.0s TTFT) while GPT-6 Luna provides 103 t/s (77.0s TTFT).
Full Metric & Specification Comparison
| Metric | GPT-6.1 Sol | GPT-6 Luna | Advantage |
|---|---|---|---|
| Provider | OpenAI | OpenAI | - |
| Input Token Cost (1M) | $2.000 | $0.100 | GPT-6 Luna |
| Output Token Cost (1M) | $10.000 | $0.500 | GPT-6 Luna |
| Context Window | 1,050k tokens | 1,050k tokens | Tie |
| AA Intelligence Index | 55/100 | 28/100 | GPT-6.1 Sol |
| Streaming Latency | Moderate | Fast | - |
Key Architectural Takeaways
- Input Token Cost: GPT-6.1 Sol is $2.00/1M vs GPT-6 Luna at $0.10/1M
- Output Token Cost: GPT-6.1 Sol is $10.00/1M vs GPT-6 Luna at $0.50/1M
- Context Window: GPT-6.1 Sol supports 1,050k tokens vs GPT-6 Luna with 1,050k tokens
- Benchmark Intelligence Rating: GPT-6.1 Sol scores 55/100 vs GPT-6 Luna at 28/100 (AA Index v5)
- Inference Latency: GPT-6.1 Sol is rated Moderate vs GPT-6 Luna rated Fast
Calculate Your Team's Exact Bill Difference
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Deploying GPT-6 Luna over GPT-6.1 Sol saves $99.75/mo ($1197/yr) at this volume.
Frequently Asked Questions
Is GPT-6.1 Sol cheaper than GPT-6 Luna?
GPT-6 Luna is significantly more economical, charging $0.10/1M input compared to $2.00/1M for GPT-6.1 Sol.
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-6.1 Sol is recommended.
How does context window compare between GPT-6.1 Sol and GPT-6 Luna?
GPT-6.1 Sol offers the larger context window (1,050k tokens), allowing you to pass significantly larger codebases or documents without chunking.
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