Model comparison
DeepSeek-V2 (MoE-236B, May 2024) vs GPT-5.6 Terra
GPT-5.6 Terra has enough public results to be ranked (#17); 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 Terra in 1 category; one gap is clear of the uncertainty.
- The widest gap is in coding, where GPT-5.6 Terra leads 57.7 to 40.4.
- 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 Terra | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 40.3 | 59.2 |
| Released | 2024-05-07 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | — | 1.05M |
| Max output | — | 128K |
| Input $ / M tokens | — | $2 |
| Output $ / M tokens | — | $12 |
| Results tracked | 10 | 52 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.6 Terra leads
DeepSeek-V2 (MoE-236B, May 2024): 40.4 (#139), GPT-5.6 Terra: 57.7 (#19)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| DeepSWE | — | 69.6% |
| FrontierCode | — | 41.3% |
| CursorBench | — | 41.3% |
| LMArena WebDev | — | 1522 |
| SciCode | — | 55% |
| WeirdML | — | 78.3% |
| BigCodeBench Instruct | 48.9% | — |
| LMArena Coding | — | 1484 |
| BigCodeBench Complete | 59.4% | — |
| ALE-Bench | — | 1,951 |
Agentic & Tool Use Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Terra: 40.1 (#25)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| APEX-Agents | — | 58.2% |
| BALROG | — | 53.2% |
| GDP.pdf | — | 24.7% |
| Vending-Bench 2 | — | 7,343 |
Reasoning Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Terra: 60.7 (#21)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| Epoch Capabilities Index | 124.77 | 159.62 |
| ARC-AGI-2 | — | 83.9% |
| SimpleBench | — | 48.9% |
| Kagi LLM Benchmark | — | 51.3% |
| NYT Connections (extended) | — | 78.4% |
| ARC-AGI-1 | — | 96.5% |
| CritPt | — | 30% |
| Chess Puzzles | — | 54% |
| LMArena Hard Prompts | — | 1468 |
| Mystery Game Puzzles | — | 35% |
| DTBench | — | 93.3% |
| LMCA | — | 55% |
| Surface Evolver Bench | — | 83.8% |
| 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 Terra: 81.6 (#12)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 86% |
| FrontierMath Tier 4 | — | 70.7% |
| OTIS Mock AIME 2024-2025 | — | 99.7% |
| ProofBench | — | 74% |
| LMArena Math | — | 1466 |
Knowledge Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Terra: 61.2 (#30)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| GPQA Diamond | — | 93.3% |
| SimpleQA Verified | — | 43.2% |
| LMArena Expert | — | 1492 |
| ARC (AI2) Challenge | 92.2% | — |
| MMLU | 78.4% | — |
| TriviaQA | 80% | — |
Multimodal Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Terra: 47.3 (#11)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| LMArena Vision | — | 1271 |
| Blueprint-Bench 2 | — | 30.8% |
| Furniture Assembly | — | 54.2% |
| LMArena Document | — | 1472 |
Multilingual Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Terra: 54.4 (#44)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| LMArena Non-English | — | 1439 |
| LMArena Chinese | — | 1513 |
| LMArena French | — | 1471 |
| LMArena German | — | 1460 |
| LMArena Japanese | — | 1457 |
| LMArena Korean | — | 1425 |
| LMArena Russian | — | 1450 |
| LMArena Spanish | — | 1448 |
Instruction Following Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Terra: 76.4 (#40)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| LMArena Instruction Following | — | 1454 |
Long Context Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Terra: 44.4 (#68)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| LMArena Longer Query | — | 1451 |
Writing & Preference Not comparable
DeepSeek-V2 (MoE-236B, May 2024): —, GPT-5.6 Terra: 70.2 (#23)
| Benchmark | DeepSeek-V2 (MoE-236B, May 2024) | GPT-5.6 Terra |
|---|---|---|
| LMArena Text | — | 1447 |
| LMArena Creative Writing | — | 1410 |
| EQ-Bench Creative Writing | — | 1855 |
| EQ-Bench 4 | — | 1234 |
| LMArena Multi-Turn | — | 1449 |
Frequently asked questions
Is DeepSeek-V2 (MoE-236B, May 2024) better than GPT-5.6 Terra?
GPT-5.6 Terra has enough public results to be ranked (#17); 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 Terra better for coding?
GPT-5.6 Terra scores higher on coding benchmarks: 57.7 versus 40.4 in the Noometry coding category.
How many benchmarks do DeepSeek-V2 (MoE-236B, May 2024) and GPT-5.6 Terra 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 Terra has 52.