Model comparison
DeepSeek-V3.1 vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 42.8 on the Noometry Index. DeepSeek-V3.1 costs 19× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Last verified . 25 shared benchmarks.
DeepSeekSummary
- They share 25 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 0 categories and GPT-5.6 Sol in 8 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GPT-5.6 Sol leads 74.8 to 27.9.
- The biggest single-benchmark swing is WeirdML: 38.4% for DeepSeek-V3.1 and 89.4% for GPT-5.6 Sol.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 164K.
- DeepSeek-V3.1 has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.1 | GPT-5.6 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 42.8 | 65.0 |
| Released | 2025-08-21 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 164K | 1.05M |
| Max output | 8K | 128K |
| Input $ / M tokens | $0.25 | $4 |
| Output $ / M tokens | $0.95 | $20 |
| Results tracked | 27 | 65 |
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Category by category
Coding GPT-5.6 Sol leads
DeepSeek-V3.1: 40.3 (#144), GPT-5.6 Sol: 65.1 (#7)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| WeirdML | 38.4% | 89.4% |
| LMArena Coding | 1417 | 1498 |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| LMArena WebDev | — | 1618 |
| FrontierSWE | — | 32.2% |
| SciCode | — | 57.1% |
| GSO | — | 76.5% |
| MirrorCode | — | 20% |
| ALE-Bench | — | 2,177 |
Agentic & Tool Use Not comparable
DeepSeek-V3.1: —, GPT-5.6 Sol: 50.3 (#7)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| APEX-Agents | — | 51.4% |
| OSWorld 2.0 | — | 27.3% |
| τ²-bench Banking | — | 46.9% |
| PostTrainBench | — | 36.2% |
| BALROG | — | 60% |
| GBAEval | — | 52.6% |
| GDP.pdf | — | 30.7% |
| LMArena Search | — | 1257 |
| Vending-Bench 2 | — | 9,619 |
Reasoning GPT-5.6 Sol leads
DeepSeek-V3.1: 27.9 (#110), GPT-5.6 Sol: 74.8 (#8)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| SimpleBench | 40% | 71.7% |
| Kagi LLM Benchmark | 53.2% | 67% |
| LMArena Hard Prompts | 1417 | 1484 |
| DTBench | 82.7% | 96% |
| LMCA | 24.3% | 59.2% |
| Epoch Capabilities Index | 139.92 | 161.66 |
| ARC-AGI-2 | — | 92.5% |
| NYT Connections (extended) | — | 93.8% |
| ARC-AGI-1 | — | 97.5% |
| CritPt | — | 32.3% |
| Chess Puzzles | — | 64% |
| EnigmaEval | — | 37.1% |
| EBR-Bench | — | 44.8% |
| Mystery Game Puzzles | — | 58% |
| Surface Evolver Bench | — | 93.1% |
| Bench to the Future 3 | — | 0.14 |
| ForecastBench | 58 | — |
Math GPT-5.6 Sol leads
DeepSeek-V3.1: 38.9 (#122), GPT-5.6 Sol: 85.6 (#9)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| LMArena Math | 1420 | 1474 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| ProofBench | — | 83% |
| FrontierMath Erdős | — | 0% |
Knowledge GPT-5.6 Sol leads
DeepSeek-V3.1: 43.7 (#90), GPT-5.6 Sol: 64.3 (#18)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| Vectara Hallucination Rate | 5.5% | 12.4% |
| LMArena Expert | 1405 | 1516 |
| GPQA Diamond | — | 93.5% |
| SimpleQA Verified | — | 69.7% |
Multimodal Not comparable
DeepSeek-V3.1: —, GPT-5.6 Sol: 48.6 (#9)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | — | 1281 |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |
Multilingual GPT-5.6 Sol leads
DeepSeek-V3.1: 51.6 (#106), GPT-5.6 Sol: 55.3 (#32)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1400 | 1452 |
| LMArena Chinese | 1469 | 1527 |
| LMArena French | 1447 | 1477 |
| LMArena German | 1411 | 1476 |
| LMArena Japanese | 1378 | 1471 |
| LMArena Korean | 1337 | 1442 |
| LMArena Russian | 1405 | 1468 |
| LMArena Spanish | 1431 | 1441 |
Instruction Following GPT-5.6 Sol leads
DeepSeek-V3.1: 73.9 (#110), GPT-5.6 Sol: 77.7 (#16)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1400 | 1482 |
Long Context GPT-5.6 Sol leads
DeepSeek-V3.1: 36.3 (#232), GPT-5.6 Sol: 45.4 (#42)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1422 | 1480 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference GPT-5.6 Sol leads
DeepSeek-V3.1: 60.3 (#98), GPT-5.6 Sol: 73.3 (#12)
| Benchmark | DeepSeek-V3.1 | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1420 | 1457 |
| LMArena Creative Writing | 1401 | 1448 |
| EQ-Bench Creative Writing | 1436 | 1972 |
| LMArena Multi-Turn | 1408 | 1460 |
| EQ-Bench 4 | — | 1250 |
Frequently asked questions
Is DeepSeek-V3.1 better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 65.0 to 42.8 on the Noometry Index. DeepSeek-V3.1 costs 19× less per token, which makes it the better buy when GPT-5.6 Sol's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1 or GPT-5.6 Sol?
DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is DeepSeek-V3.1 or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 65.1 versus 40.3 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 164K.
How many benchmarks do DeepSeek-V3.1 and GPT-5.6 Sol share?
25 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and GPT-5.6 Sol has 65.