GPT-6 Luna Pro vs Qwen2.5 VL 72B Instruct
GPT-6 Luna Pro costs 2.0x less per output token than Qwen2.5 VL 72B Instruct ($0.50 vs $1.00 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $1.00
GPT-6 Luna Pro or Qwen2.5 VL 72B Instruct: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick GPT-6 Luna Pro for output tokens 2.0x cheaper ($0.50 vs $1.00 per million) and a larger context window (1.05M against 128K tokens).
No benchmark has tested both yet. GPT-6 Luna Pro has none yet; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead.
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 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GPT-6 Luna Pro
GPT-6 Luna Pro $0.0003, Qwen2.5 VL 72B Instruct $0.0013 per turn.
List prices
At a glance
- Coding index
- GPT-6 Luna Pro not benchmarked, Qwen2.5 VL 72B Instruct 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
- GPT-6 Luna Pro $0.50 against Qwen2.5 VL 72B Instruct $1.00 per million output tokens, 2.0x apart.
- Context window
- GPT-6 Luna Pro takes 1.05M tokens, Qwen2.5 VL 72B Instruct 128K.
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.
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-6 Luna Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| OSWorld | Not measured | 5.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 41 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-6 Luna Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| Kagi LLM Benchmark | Not measured | 36.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 22 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | GPT-6 Luna Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| GeoBench | Not measured | 62.0% | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 19 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | GPT-6 Luna Pro | Qwen2.5 VL 72B Instruct | Leader |
|---|---|---|---|
| LMArena Vision | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: English | Not measured | 1115 | Only Qwen2.5 VL 72B Instruct measured |
| LMArena Vision: Chinese | Not measured | 1107 | Only Qwen2.5 VL 72B Instruct measured |
| Neither model has a result on the other 45 boards here | |||
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 | OpenAI GPT-6 Luna Pro | Qwen2.5 VL 72B Instruct |
|---|---|---|
| Maker | OpenAI | Qwen |
| Weights | No public weights listed | Open weights |
| Released | September 22, 2026 | February 1, 2025 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $0.10 | $0.80 |
| Output $/Mtok | $0.50 | $1.00 |
| 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 GPT-6 Luna Pro better than Qwen2.5 VL 72B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GPT-6 Luna Pro for output tokens 2.0x cheaper ($0.50 vs $1.00 per million) and a larger context window (1.05M against 128K tokens).
Which is cheaper, GPT-6 Luna Pro or Qwen2.5 VL 72B Instruct?
GPT-6 Luna Pro costs $0.10 per million input tokens and $0.50 per million output tokens; Qwen2.5 VL 72B Instruct costs $0.80 and $1.00. Cost per solved task is known for neither of them.
Which has the bigger context window, GPT-6 Luna Pro or Qwen2.5 VL 72B Instruct?
GPT-6 Luna Pro, with 1.05M tokens against 128K.
Has any benchmark tested both GPT-6 Luna Pro and Qwen2.5 VL 72B Instruct?
No benchmark has tested both yet. GPT-6 Luna Pro has none yet; Qwen2.5 VL 72B Instruct on OSWorld, Kagi LLM Benchmark and GeoBench. Compare them on price and context instead. Data as of October 22, 2026.