Model comparison
DeepSeek Coder 6.7B vs GPT-5.2
GPT-5.2 has enough public results to be ranked (#34); DeepSeek Coder 6.7B 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 Coder 6.7B scores higher in 0 categories and GPT-5.2 in 1 category; one gap is clear of the uncertainty.
- The widest gap is in coding, where GPT-5.2 leads 51.6 to 35.8.
- DeepSeek Coder 6.7B has downloadable open weights; the other is API-only.
Side by side
| DeepSeek Coder 6.7B | GPT-5.2 | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 37.6 | 54.1 |
| Released | 2023-11-02 | 2025-12-11 |
| Weights | Open | Proprietary |
| Context window | — | 400K |
| Max output | — | 128K |
| Input $ / M tokens | — | $1.75 |
| Output $ / M tokens | — | $14 |
| Results tracked | 9 | 67 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.2 leads
DeepSeek Coder 6.7B: 35.8 (#219), GPT-5.2: 51.6 (#37)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| SWE-bench Verified | — | 73.8% |
| SWE-bench Verified (bash only) | — | 72.8% |
| LMArena WebDev | — | 1416 |
| SWE-bench Multilingual | — | 66.7% |
| GSO | — | 27.4% |
| WeirdML | — | 72.2% |
| BigCodeBench Instruct | 35.5% | — |
| LMArena Coding | — | 1447 |
| BigCodeBench Complete | 43.8% | — |
| ALE-Bench | — | 1,294 |
| AlgoTune | — | 2.05 |
| HumanEval+ | 71.3% | — |
| MBPP+ | 65.6% | — |
Agentic & Tool Use Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 40.2 (#24)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| Terminal-Bench | — | 64.9% |
| Berkeley Function Calling Leaderboard | — | 55.9% |
| GDPval | — | 49.7% |
| Remote Labor Index | — | 2.5% |
| τ²-bench Airline | — | 83% |
| τ²-bench Banking | — | 32.2% |
| τ²-bench Retail | — | 81.6% |
| τ²-bench Telecom | — | 89.7% |
| DeepResearch Bench | — | 41.1% |
| LMArena Search | — | 1207 |
| METR Time Horizons | — | 75.3% |
| Vending-Bench 2 | — | 3,591 |
Reasoning Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 50.2 (#35)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| Epoch Capabilities Index | 89.62 | 153.45 |
| ARC-AGI-2 | — | 52.9% |
| SimpleBench | — | 45.8% |
| Kagi LLM Benchmark | — | 73.3% |
| NYT Connections (extended) | — | 83.6% |
| ARC-AGI-1 | — | 86.2% |
| Chess Puzzles | — | 49% |
| EnigmaEval | — | 10.4% |
| EBR-Bench | — | 23% |
| LMArena Hard Prompts | — | 1445 |
| Mystery Game Puzzles | — | 23% |
| DTBench | — | 90.9% |
| LMCA | — | 43.9% |
| ForecastBench | — | 60.1 |
| WinoGrande | 57.6% | — |
Math Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 60.0 (#38)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | — | 67.4% |
| FrontierMath Tier 4 | — | 31.7% |
| MathArena Final-Answer Competitions | — | 72% |
| OTIS Mock AIME 2024-2025 | — | 96.1% |
| ProofBench | — | 15% |
| LMArena Math | — | 1440 |
| FrontierMath (Feb 2025 set) | — | 40.7% |
| FrontierMath Tier 4 (v1) | — | 18.8% |
| GSM8K | 21.3% | — |
Knowledge Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 59.3 (#32)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| GPQA Diamond | — | 91.4% |
| Humanity's Last Exam | — | 27.8% |
| SimpleQA Verified | — | 37.1% |
| Vectara Hallucination Rate | — | 8.4% |
| LMArena Expert | — | 1445 |
| ARC (AI2) Challenge | 36.4% | — |
| MMLU | 36.4% | — |
Multimodal Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 51.3 (#7)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| LMArena Vision | — | 1268 |
| VPCT | — | 84% |
| Furniture Assembly | — | 38.3% |
| LMArena Document | — | 1405 |
Multilingual Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 53.4 (#67)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| LMArena Non-English | — | 1425 |
| LMArena Chinese | — | 1460 |
| LMArena French | — | 1455 |
| LMArena German | — | 1448 |
| LMArena Japanese | — | 1420 |
| LMArena Korean | — | 1392 |
| LMArena Russian | — | 1440 |
| LMArena Spanish | — | 1433 |
Instruction Following Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 74.7 (#89)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| LMArena Instruction Following | — | 1417 |
Long Context Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 44.0 (#78)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| CL-bench | — | 18.2% |
| LMArena Longer Query | — | 1428 |
Writing & Preference Not comparable
DeepSeek Coder 6.7B: —, GPT-5.2: 66.8 (#32)
| Benchmark | DeepSeek Coder 6.7B | GPT-5.2 |
|---|---|---|
| LMArena Text | — | 1439 |
| LMArena Creative Writing | — | 1401 |
| EQ-Bench Creative Writing | — | 1703 |
| LMArena Multi-Turn | — | 1458 |
Frequently asked questions
Is DeepSeek Coder 6.7B better than GPT-5.2?
GPT-5.2 has enough public results to be ranked (#34); DeepSeek Coder 6.7B does not yet, so treat this comparison as directional.
Is DeepSeek Coder 6.7B or GPT-5.2 better for coding?
GPT-5.2 scores higher on coding benchmarks: 51.6 versus 35.8 in the Noometry coding category.
How many benchmarks do DeepSeek Coder 6.7B and GPT-5.2 share?
1 benchmark has published results for both models. DeepSeek Coder 6.7B has 9 scored results on Noometry and GPT-5.2 has 67.