Model comparison
DeepSeek-V2 (MoE-236B, May 2024) vs GPT-5.6 Luna
GPT-5.6 Luna has enough public results to be ranked (#24); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Last verified . 1 shared benchmarks.
DeepSeekSummary
- They share 1 benchmark with published results for both. DeepSeek-V2 (MoE-236B, May 2024) scores higher in 0 categories and GPT-5.6 Luna in 1 category; one gap is clear of the uncertainty.
- The widest gap is in coding, where GPT-5.6 Luna leads 53.9 to 39.8.
- DeepSeek-V2 (MoE-236B, May 2024) has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.7 | 54.7 |
| Released | 2024-05-07 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 128K |
| Input $ / M tokens | — | $0.20 |
| Output $ / M tokens | — | $1.20 |
| Results tracked | 10 | 51 |
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Category by category
Coding GPT-5.6 Luna leads
DeepSeek-V2 (MoE-236B, May 2024): 39.8 (#141), GPT-5.6 Luna: 53.9 (#25)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| DeepSWE | — | 67.2% |
| FrontierCode | — | 39.8% |
| CursorBench | — | 35.9% |
| LMArena WebDev | — | 1517 |
| SciCode | — | 53.6% |
| WeirdML | — | 60.9% |
| BigCodeBench Instruct | 48.9% | — |
| LMArena Coding | — | 1466 |
| BigCodeBench Complete | 59.4% | — |
| ALE-Bench | — | 1,667 |
Agentic & Tool Use Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 35.3 (#37)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| BALROG | — | 45.6% |
| GDP.pdf | — | 22.7% |
| Vending-Bench 2 | — | 4,095 |
Reasoning Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 47.5 (#38)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| Epoch Capabilities Index | 124.78 | 156.32 |
| ARC-AGI-2 | — | 59.5% |
| SimpleBench | — | 46.8% |
| Kagi LLM Benchmark | — | 49.1% |
| NYT Connections (extended) | — | 69.4% |
| ARC-AGI-1 | — | 88% |
| CritPt | — | 20.6% |
| Chess Puzzles | — | 40% |
| LMArena Hard Prompts | — | 1452 |
| Mystery Game Puzzles | — | 21% |
| DTBench | — | 89.1% |
| LMCA | — | 48.5% |
| Surface Evolver Bench | — | 61.9% |
| BIG-Bench Hard | 78.8% | — |
| HellaSwag | 87.1% | — |
| PIQA | 83.9% | — |
| WinoGrande | 86.3% | — |
Math Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 77.5 (#12)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| 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
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 58.6 (#31)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| GPQA Diamond | — | 91.6% |
| SimpleQA Verified | — | 41% |
| LMArena Expert | — | 1479 |
| ARC (AI2) Challenge | 92.2% | — |
| MMLU | 78.4% | — |
| TriviaQA | 80% | — |
Multimodal Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 43.0 (#22)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Vision | — | 1259 |
| Blueprint-Bench 2 | — | 22.6% |
| Furniture Assembly | — | 42.5% |
| LMArena Document | — | 1457 |
Multilingual Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 53.0 (#73)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| 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
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 76.0 (#48)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Instruction Following | — | 1440 |
Long Context Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 43.9 (#78)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Longer Query | — | 1437 |
Writing & Preference Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Luna: 68.3 (#27)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Luna |
|---|---|---|
| LMArena Text | — | 1432 |
| LMArena Creative Writing | — | 1397 |
| EQ-Bench Creative Writing | — | 1829 |
| EQ-Bench 4 | — | 1156 |
| LMArena Multi-Turn | — | 1433 |
Frequently asked questions
Is DeepSeek-V2 (MoE-236B, May 2024) better than GPT-5.6 Luna?
GPT-5.6 Luna has enough public results to be ranked (#24); DeepSeek-V2 (MoE-236B, May 2024) does not yet, so treat this comparison as directional.
Is DeepSeek-V2 (MoE-236B, May 2024) or GPT-5.6 Luna better for coding?
GPT-5.6 Luna scores higher on coding benchmarks: 53.9 versus 39.8 in the Noometry coding category.
How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and GPT-5.6 Luna share?
1 benchmark has published results for both models. DeepSeek-V2 (MoE-236B, May 2024) has 10 scored results on Noometry and GPT-5.6 Luna has 51.