GPT-5.6 Luna vs Llama 4 Maverick
OpenAI vs Meta · Prices checked November 11, 2026
On price GPT-5.6 Luna edges it, $0.5000 per million tokens blended versus $0.5450, about 8 per cent less. A single 100K-input, 10K-output job costs $0.0320 on GPT-5.6 Luna against $0.0450 on Llama 4 Maverick.
Pricing and capacity
RatesUSD per million tokens. Prices checked November 11, 2026.
| Metric | GPT-5.6 Luna | Llama 4 Maverick | Difference |
|---|---|---|---|
| Input / 1M tokens | $0.2000 | $0.3500 | 43% |
| Output / 1M tokens | $1.20 | $1.00 | 17% |
| Blended · 70/30calculated | $0.5000 | $0.5450 | 8% |
| Blended · 50/50calculated | $0.7000 | $0.6750 | 4% |
| Blended · 80/20calculated | $0.4000 | $0.4800 | 17% |
| Blended · 20/80calculated | $1.00 | $0.8700 | 13% |
| Job cost · 100K + 10Kcalculated | $0.0320 | $0.0450 | 29% |
| Job cost · 1M + 100Kcalculated | $0.3200 | $0.4500 | 29% |
| Context window | 1,050,000 | 128,000 | 8.2× |
Input and output rates are as published by each provider. Blended and job-cost rows are calculated comparison metrics, not published prices — how they are derived.
These two swap places depending on direction of traffic: GPT-5.6 Luna leads on an 80/20 input-heavy blend, Llama 4 Maverick on a 20/80 output-heavy one. Capability favours GPT-5.6 Luna, ahead on 2 of 2 suites they both publish. The widest gap is on LMArena Elo, where GPT-5.6 Luna leads by 126 points. GPT-5.6 Luna reads more in one request: 1,050,000 tokens of context against 128,000.
Benchmarks
| Benchmark | GPT-5.6 Luna | Llama 4 Maverick | Leader |
|---|---|---|---|
| GPQA Diamond | 92.3% | 69.8% | GPT-5.6 Luna |
| LMArena Elo | 1453 Elo | 1327 Elo | GPT-5.6 Luna |
Only suites both models report are listed, so a missing score is never shown as a loss. Figures are provider-published or from public leaderboards and are not independently re-run.
What each model is for
GPT-5.6 Luna
OpenAI · Efficient
The fastest and most affordable GPT-5.6 tier, a deliberately low-reasoning lane for high-volume work where speed and cost are the binding constraints rather than depth. It keeps the family's 1M-token context window at $0.20/$1.20 per MTok, which is 25× cheaper than Sol on input, with cached input at $0.02.
- Lowest cost in the GPT-5.6 family, 25× cheaper input than Sol
- Fast responses
- High-volume throughput
- 1M-token context
Llama 4 Maverick
Meta · Frontier
The flagship Llama 4, an MoE-architecture model designed for cheap, high-throughput inference across the open-weights ecosystem.
- MoE for cheap inference
- Open weights
- Wide hosted availability
- Multimodal
Frequently asked questions
Which is cheaper, GPT-5.6 Luna or Llama 4 Maverick?
On price GPT-5.6 Luna edges it, $0.5000 per million tokens blended versus $0.5450, about 8 per cent less. A single 100K-input, 10K-output job costs $0.0320 on GPT-5.6 Luna against $0.0450 on Llama 4 Maverick. Blended cost weights input at 70% and output at 30%; price your own token split for an exact answer, since the cheaper model on a blended basis is not always cheaper for your workload.
Which performs better, GPT-5.6 Luna or Llama 4 Maverick?
Capability favours GPT-5.6 Luna, ahead on 2 of 2 suites they both publish. The widest gap is on LMArena Elo, where GPT-5.6 Luna leads by 126 points. These are the figures each provider or public leaderboard publishes, not results we re-run, and a benchmark average can hide the specific behaviour your workload depends on.
Which has the larger context window, GPT-5.6 Luna or Llama 4 Maverick?
GPT-5.6 Luna reads more in one request: 1,050,000 tokens of context against 128,000. The context window caps everything in a single request: prompt, retrieved documents, conversation history and the model’s own output all share it.
Can I compare GPT-5.6 Luna and Llama 4 Maverick on my own numbers?
Yes. Open both in the side-by-side comparison tool to see every field at once, or use the token calculator to price your actual prompt and monthly request volume against each model. Both are free and need no sign-up.
Price it for your own workload
Related matchups
Terms used on this page
Prices and scores are recomputed on every deploy from the catalogue. Prices checked November 11, 2026.