Model comparison
DeepSeek V4.1 Flash vs GPT-5.6 Sol
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 50.3 on the Noometry Index. DeepSeek V4.1 Flash costs 30× 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 V4.1 Flash scores higher in 1 category and GPT-5.6 Sol in 7 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-5.6 Sol leads 85.5 to 48.4.
- The biggest single-benchmark swing is Surface Evolver Bench: 46.3% for DeepSeek V4.1 Flash and 93.1% for GPT-5.6 Sol.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $4 / $20 for GPT-5.6 Sol.
- GPT-5.6 Sol accepts more context: 1.05M tokens versus 1M.
- DeepSeek V4.1 Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4.1 Flash | GPT-5.6 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 50.3 | 64.4 |
| Released | 2026-09-09 | 2026-07-09 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 393K | 128K |
| Input $ / M tokens | $0.15 | $4 |
| Output $ / M tokens | $0.60 | $20 |
| Results tracked | 25 | 64 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GPT-5.6 Sol leads
DeepSeek V4.1 Flash: 52.8 (#29), GPT-5.6 Sol: 61.1 (#7)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena WebDev | 1620 | 1619 |
| SciCode | 51.9% | 57.1% |
| LMArena Coding | 1511 | 1493 |
| ALE-Bench | 1,092 | 2,177 |
| DeepSWE | — | 72.7% |
| FrontierCode | — | 47.5% |
| CursorBench | — | 41.7% |
| FrontierSWE | — | 32.2% |
| WeirdML | — | 89.4% |
| MirrorCode | — | 20% |
Agentic & Tool Use Not comparable
DeepSeek V4.1 Flash: —, GPT-5.6 Sol: 50.2 (#7)
| Benchmark | DeepSeek V4.1 Flash | 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 V4.1 Flash: 46.2 (#39), GPT-5.6 Sol: 74.7 (#6)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| NYT Connections (extended) | 89.6% | 93.8% |
| CritPt | 14.3% | 32.3% |
| LMArena Hard Prompts | 1485 | 1481 |
| DTBench | 89.9% | 96% |
| LMCA | 47% | 59.2% |
| Surface Evolver Bench | 46.3% | 93.1% |
| Epoch Capabilities Index | 155.01 | 161.99 |
| ARC-AGI-2 | — | 92.5% |
| SimpleBench | — | 71.7% |
| Kagi LLM Benchmark | — | 67% |
| ARC-AGI-1 | — | 97.5% |
| Chess Puzzles | — | 64% |
| EnigmaEval | — | 37.1% |
| EBR-Bench | — | 44.8% |
| Mystery Game Puzzles | — | 58% |
| Bench to the Future 3 | — | 0.14 |
Math GPT-5.6 Sol leads
DeepSeek V4.1 Flash: 48.4 (#52), GPT-5.6 Sol: 85.5 (#7)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| ProofBench | 54% | 83% |
| LMArena Math | 1493 | 1472 |
| FrontierMath (Tiers 1-3) | — | 89.1% |
| FrontierMath Tier 4 | — | 82.9% |
| OTIS Mock AIME 2024-2025 | — | 100% |
| FrontierMath Erdős | — | 0% |
Knowledge GPT-5.6 Sol leads
DeepSeek V4.1 Flash: 41.6 (#97), GPT-5.6 Sol: 64.3 (#16)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Expert | 1505 | 1517 |
| GPQA Diamond | — | 93.5% |
| SimpleQA Verified | — | 69.7% |
| Vectara Hallucination Rate | — | 12.4% |
Multimodal Not comparable
DeepSeek V4.1 Flash: —, GPT-5.6 Sol: 48.9 (#8)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Vision | — | 1281 |
| Blueprint-Bench 2 | — | 33.6% |
| Furniture Assembly | — | 56.7% |
| LMArena Document | — | 1483 |
Multilingual Too close to call
DeepSeek V4.1 Flash: 55.5 (#29), GPT-5.6 Sol: 55.4 (#32)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Non-English | 1452 | 1450 |
| LMArena Chinese | 1506 | 1523 |
| LMArena French | 1448 | 1475 |
| LMArena Russian | 1473 | 1466 |
| LMArena Spanish | 1499 | 1449 |
| LMArena German | — | 1476 |
| LMArena Japanese | — | 1475 |
| LMArena Korean | — | 1434 |
Instruction Following Too close to call
DeepSeek V4.1 Flash: 77.6 (#24), GPT-5.6 Sol: 77.8 (#16)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Instruction Following | 1475 | 1480 |
Long Context Too close to call
DeepSeek V4.1 Flash: 45.2 (#46), GPT-5.6 Sol: 45.3 (#42)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Longer Query | 1476 | 1479 |
Writing & Preference GPT-5.6 Sol leads
DeepSeek V4.1 Flash: 65.8 (#43), GPT-5.6 Sol: 73.5 (#12)
| Benchmark | DeepSeek V4.1 Flash | GPT-5.6 Sol |
|---|---|---|
| LMArena Text | 1465 | 1456 |
| LMArena Creative Writing | 1436 | 1450 |
| EQ-Bench Creative Writing | 1540 | 1972 |
| LMArena Multi-Turn | 1460 | 1456 |
| EQ-Bench 4 | — | 1250 |
Frequently asked questions
Is DeepSeek V4.1 Flash better than GPT-5.6 Sol?
GPT-5.6 Sol is the stronger model overall, scoring 64.4 to 50.3 on the Noometry Index. DeepSeek V4.1 Flash costs 30× 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 V4.1 Flash or GPT-5.6 Sol?
DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-5.6 Sol lists at $4 and $20.
Is DeepSeek V4.1 Flash or GPT-5.6 Sol better for coding?
GPT-5.6 Sol scores higher on coding benchmarks: 61.1 versus 52.8 in the Noometry coding category.
Which has the bigger context window?
GPT-5.6 Sol does, with 1.05M tokens against 1M.
How many benchmarks do DeepSeek V4.1 Flash and GPT-5.6 Sol share?
25 benchmarks have published results for both models. DeepSeek V4.1 Flash has 25 scored results on Noometry and GPT-5.6 Sol has 64.