Model comparison
GPT-5.6 Luna vs Solar Pro 3
GPT-5.6 Luna has enough public results to be ranked (#24); Solar Pro 3 does not yet, so treat this comparison as directional.
Last verified . 2 shared benchmarks.
UpstageSummary
- They share 2 benchmarks with published results for both. GPT-5.6 Luna scores higher in 2 categories and Solar Pro 3 in 0 categories; 2 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Luna leads 47.5 to 21.7.
- The biggest single-benchmark swing is SciCode: 53.6% for GPT-5.6 Luna and 24.7% for Solar Pro 3.
- Solar Pro 3 is cheaper at $0.25 / $0.25 per million input/output tokens, against $0.20 / $1.20 for GPT-5.6 Luna.
- GPT-5.6 Luna accepts more context: 1.05M tokens versus 131K.
Side by side
| GPT-5.6 Luna | Solar Pro 3 | |
|---|---|---|
| Provider | OpenAI | Upstage |
| Noometry Index | 54.7 | 33.3 |
| Released | 2026-07-09 | 2026-01 |
| Weights | Proprietary | Proprietary |
| Context window | 1.05M | 131K |
| Max output | 128K | 8K |
| Input $ / M tokens | $0.20 | $0.25 |
| Output $ / M tokens | $1.20 | $0.25 |
| Results tracked | 51 | 2 |
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Category by category
Coding GPT-5.6 Luna leads
GPT-5.6 Luna: 53.9 (#25), Solar Pro 3: 34.2
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| SciCode | 53.6% | 24.7% |
| DeepSWE | 67.2% | — |
| FrontierCode | 39.8% | — |
| CursorBench | 35.9% | — |
| LMArena WebDev | 1517 | — |
| WeirdML | 60.9% | — |
| LMArena Coding | 1466 | — |
| ALE-Bench | 1,667 | — |
Agentic & Tool Use Not comparable
GPT-5.6 Luna: 35.3 (#37), Solar Pro 3: —
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| BALROG | 45.6% | — |
| GDP.pdf | 22.7% | — |
| Vending-Bench 2 | 4,095 | — |
Reasoning GPT-5.6 Luna leads
GPT-5.6 Luna: 47.5 (#38), Solar Pro 3: 21.7
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| CritPt | 20.6% | 0% |
| ARC-AGI-2 | 59.5% | — |
| SimpleBench | 46.8% | — |
| Kagi LLM Benchmark | 49.1% | — |
| NYT Connections (extended) | 69.4% | — |
| ARC-AGI-1 | 88% | — |
| Chess Puzzles | 40% | — |
| LMArena Hard Prompts | 1452 | — |
| Mystery Game Puzzles | 21% | — |
| DTBench | 89.1% | — |
| LMCA | 48.5% | — |
| Surface Evolver Bench | 61.9% | — |
| Epoch Capabilities Index | 156.32 | — |
Math Not comparable
GPT-5.6 Luna: 77.5 (#12), Solar Pro 3: —
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 82.1% | — |
| FrontierMath Tier 4 | 61% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 60% | — |
| LMArena Math | 1455 | — |
Knowledge Not comparable
GPT-5.6 Luna: 58.6 (#31), Solar Pro 3: —
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| GPQA Diamond | 91.6% | — |
| SimpleQA Verified | 41% | — |
| LMArena Expert | 1479 | — |
Multimodal Not comparable
GPT-5.6 Luna: 43.0 (#22), Solar Pro 3: —
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| LMArena Vision | 1259 | — |
| Blueprint-Bench 2 | 22.6% | — |
| Furniture Assembly | 42.5% | — |
| LMArena Document | 1457 | — |
Multilingual Not comparable
GPT-5.6 Luna: 53.0 (#73), Solar Pro 3: —
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| LMArena Non-English | 1418 | — |
| LMArena Chinese | 1469 | — |
| LMArena French | 1457 | — |
| LMArena German | 1459 | — |
| LMArena Japanese | 1416 | — |
| LMArena Korean | 1414 | — |
| LMArena Russian | 1428 | — |
| LMArena Spanish | 1445 | — |
Instruction Following Not comparable
GPT-5.6 Luna: 76.0 (#48), Solar Pro 3: —
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| LMArena Instruction Following | 1440 | — |
Long Context Not comparable
GPT-5.6 Luna: 43.9 (#78), Solar Pro 3: —
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| LMArena Longer Query | 1437 | — |
Writing & Preference Not comparable
GPT-5.6 Luna: 68.3 (#27), Solar Pro 3: —
| Benchmark | GPT-5.6 Luna | Solar Pro 3 |
|---|---|---|
| LMArena Text | 1432 | — |
| LMArena Creative Writing | 1397 | — |
| EQ-Bench Creative Writing | 1829 | — |
| EQ-Bench 4 | 1156 | — |
| LMArena Multi-Turn | 1433 | — |
Frequently asked questions
Is GPT-5.6 Luna better than Solar Pro 3?
GPT-5.6 Luna has enough public results to be ranked (#24); Solar Pro 3 does not yet, so treat this comparison as directional.
Which is cheaper, GPT-5.6 Luna or Solar Pro 3?
Solar Pro 3 is cheaper. It lists at $0.25 per million input tokens and $0.25 per million output tokens; GPT-5.6 Luna lists at $0.20 and $1.20.
Is GPT-5.6 Luna or Solar Pro 3 better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 53.9 versus 34.2 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Luna does, with 1.05M tokens against 131K.
How many benchmarks do GPT-5.6 Luna and Solar Pro 3 share?
2 benchmarks have published results for both models. GPT-5.6 Luna has 51 scored results on Noometry and Solar Pro 3 has 2.