Model comparison
DeepSeek-V3 vs GPT-6 Sol
GPT-6 Sol is the stronger model overall, scoring 49.2 to 39.2 on the Noometry Index. DeepSeek-V3 costs 9.9× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Last verified . 15 shared benchmarks.
DeepSeekSummary
- They share 15 benchmarks with published results for both. DeepSeek-V3 scores higher in 0 categories and GPT-6 Sol in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-6 Sol leads 49.3 to 20.0.
- DeepSeek-V3 is cheaper at $0.24 / $0.90 per million input/output tokens, against $2 / $10 for GPT-6 Sol.
- GPT-6 Sol accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3 | GPT-6 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 39.2 | 49.2 |
| Released | 2024-12-26 | 2026-09-22 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 164K | 128K |
| Input $ / M tokens | $0.24 | $2 |
| Output $ / M tokens | $0.90 | $10 |
| Results tracked | 60 | 22 |
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Category by category
Coding GPT-6 Sol leads
DeepSeek-V3: 41.9 (#99), GPT-6 Sol: 59.6 (#11)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| LMArena Coding | 1368 | 1453 |
| DeepSWE | — | 68.8% |
| FrontierCode | — | 49.3% |
| Aider Polyglot | 55.1% | — |
| LMArena WebDev | — | 1692 |
| SciCode | 35.8% | — |
| WeirdML | 36.1% | — |
| BigCodeBench Instruct | 50% | — |
| LiveBench Coding | 70.9% | — |
| BigCodeBench Complete | 62.2% | — |
| HumanEval+ | 86.6% | — |
| MBPP+ | 73% | — |
Agentic & Tool Use Not comparable
DeepSeek-V3: —, GPT-6 Sol: 33.5 (#49)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| GDP.pdf | — | 28% |
| METR Time Horizons | 49.6% | — |
Reasoning GPT-6 Sol leads
DeepSeek-V3: 20.0 (#226), GPT-6 Sol: 49.3 (#34)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| LMArena Hard Prompts | 1365 | 1420 |
| SimpleBench | 27.2% | — |
| Kagi LLM Benchmark | 52.3% | — |
| NYT Connections (extended) | — | 90.1% |
| CritPt | 0% | — |
| EBR-Bench | — | 53.3% |
| LiveBench Reasoning | 65.8% | — |
| DTBench | 64.8% | — |
| LiveBench Data Analysis | 60.9% | — |
| LMCA | 15.5% | — |
| BIG-Bench Hard | 87.5% | — |
| Epoch Capabilities Index | 135.95 | — |
| ForecastBench | 59.1 | — |
| HellaSwag | 88.9% | — |
| LiveBench | 66.9% | — |
| PIQA | 84.7% | — |
| WinoGrande | 85.2% | — |
Math GPT-6 Sol leads
DeepSeek-V3: 30.4 (#208), GPT-6 Sol: 35.8 (#135)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| LMArena Math | 1373 | 1404 |
| OTIS Mock AIME 2024-2025 | 37.8% | — |
| Omni-MATH | 40.3% | — |
| LiveBench Math | 73.5% | — |
| MATH Level 5 | 75.5% | — |
| FrontierMath (Feb 2025 set) | 1.7% | — |
Knowledge GPT-6 Sol leads
DeepSeek-V3: 37.5 (#147), GPT-6 Sol: 43.0 (#87)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| Vectara Hallucination Rate | 6.1% | 6.5% |
| LMArena Expert | 1352 | 1449 |
| GPQA Diamond | 67.6% | — |
| MMLU-Pro | 72.3% | — |
| Confabulations | 26.1% | — |
| GPQA (HELM) | 53.8% | — |
| ARC (AI2) Challenge | 95.3% | — |
| MMLU | 87.2% | — |
| TriviaQA | 82.9% | — |
Multimodal Not comparable
DeepSeek-V3: —, GPT-6 Sol: 47.6 (#9)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| Furniture Assembly | — | 58.3% |
Multilingual GPT-6 Sol leads
DeepSeek-V3: 48.8 (#138), GPT-6 Sol: 51.0 (#113)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| LMArena Non-English | 1358 | 1389 |
| LMArena Chinese | 1390 | 1416 |
| LMArena Russian | 1372 | 1407 |
| LMArena Spanish | 1358 | 1377 |
| LMArena French | 1386 | — |
| LMArena German | 1374 | — |
| LMArena Japanese | 1333 | — |
| LMArena Korean | 1321 | — |
Instruction Following GPT-6 Sol leads
DeepSeek-V3: 73.0 (#125), GPT-6 Sol: 74.4 (#97)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| LMArena Instruction Following | 1345 | 1406 |
| LiveBench Instruction Following | 81.5% | — |
| IFEval | 83.2% | — |
Long Context GPT-6 Sol leads
DeepSeek-V3: 34.0 (#248), GPT-6 Sol: 43.2 (#100)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| LMArena Longer Query | 1353 | 1415 |
| Fiction.LiveBench | 50% | — |
Writing & Preference GPT-6 Sol leads
DeepSeek-V3: 57.6 (#124), GPT-6 Sol: 72.3 (#15)
| Benchmark | DeepSeek-V3 | GPT-6 Sol |
|---|---|---|
| LMArena Text | 1375 | 1396 |
| LMArena Creative Writing | 1364 | 1392 |
| EQ-Bench Creative Writing | 1472 | 2125 |
| LMArena Multi-Turn | 1389 | 1405 |
| Short-Story Creative Writing | 77% | — |
| WildBench | 83% | — |
| LiveBench Language | 49.1% | — |
Frequently asked questions
Is DeepSeek-V3 better than GPT-6 Sol?
GPT-6 Sol is the stronger model overall, scoring 49.2 to 39.2 on the Noometry Index. DeepSeek-V3 costs 9.9× less per token, which makes it the better buy when GPT-6 Sol's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3 or GPT-6 Sol?
DeepSeek-V3 is cheaper. It lists at $0.24 per million input tokens and $0.90 per million output tokens; GPT-6 Sol lists at $2 and $10.
Is DeepSeek-V3 or GPT-6 Sol better for coding?
GPT-6 Sol scores higher on coding benchmarks: 59.6 versus 41.9 in the Noometry coding category.
Which has the bigger context window?
GPT-6 Sol does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3 and GPT-6 Sol share?
15 benchmarks have published results for both models. DeepSeek-V3 has 60 scored results on Noometry and GPT-6 Sol has 22.