Magnum v4 72B vs GPT-6 Luna Pro
GPT-6 Luna Pro costs 10.0x less per output token than Magnum v4 72B ($0.50 vs $5.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $5.00
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.50
Magnum v4 72B or GPT-6 Luna Pro: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
No benchmark has tested either model yet, so price and context are the only way to compare them.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Neither has a coding score yet.
One side not measured
Long documents
Listed context window
GPT-6 Luna Pro
GPT-6 Luna Pro takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna Pro
Magnum v4 72B $0.0050, GPT-6 Luna Pro $0.0003 per turn.
List prices
At a glance
- Coding index
- Magnum v4 72B not benchmarked, GPT-6 Luna Pro not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Magnum v4 72B $5.00 against GPT-6 Luna Pro $0.50 per million output tokens, 10.0x apart.
- Context window
- Magnum v4 72B takes 33K tokens, GPT-6 Luna Pro 1.05M.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
GPT-6 Luna Pro costs less
Repository review
50K input + 3K output tokens
GPT-6 Luna Pro costs less
Agent loop
200K input + 10K output tokens
GPT-6 Luna Pro costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | Magnum v4 72B | OpenAI GPT-6 Luna Pro |
|---|---|---|
| Maker | Anthracite | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | October 22, 2024 | September 22, 2026 |
| Context window | 33K | 1.05M |
| Input $/Mtok | $2.50 | $0.10 |
| Output $/Mtok | $5.00 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Magnum v4 72B better than GPT-6 Luna Pro for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, Magnum v4 72B or GPT-6 Luna Pro?
Magnum v4 72B costs $2.50 per million input tokens and $5.00 per million output tokens; GPT-6 Luna Pro costs $0.10 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, Magnum v4 72B or GPT-6 Luna Pro?
GPT-6 Luna Pro, with 1.05M tokens against 33K.
Has any benchmark tested both Magnum v4 72B and GPT-6 Luna Pro?
No benchmark has tested either model yet, so price and context are the only way to compare them. Data as of October 22, 2026.