Model comparison
DeepSeek V4 Flash vs GPT-6.1 Sol
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 53.6 on the Noometry Index. DeepSeek V4 Flash costs 15× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Last verified . 28 shared benchmarks.
DeepSeekSummary
- They share 28 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 1 category and GPT-6.1 Sol in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GPT-6.1 Sol leads 93.7 to 60.3.
- The biggest single-benchmark swing is FrontierMath Tier 4: 24.4% for DeepSeek V4 Flash and 100% for GPT-6.1 Sol.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $2 / $10 for GPT-6.1 Sol.
- GPT-6.1 Sol accepts more context: 1.05M tokens versus 1M.
- DeepSeek V4 Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4 Flash | GPT-6.1 Sol | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 53.6 | 65.6 |
| Released | 2026-04-24 | 2026-09-29 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 393K | 128K |
| Input $ / M tokens | $0.15 | $2 |
| Output $ / M tokens | $0.60 | $10 |
| Results tracked | 41 | 34 |
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Category by category
Coding GPT-6.1 Sol leads
DeepSeek V4 Flash: 47.9 (#59), GPT-6.1 Sol: 63.2 (#8)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| FrontierCode | 18.8% | 50.2% |
| LMArena WebDev | 1582 | 1755 |
| SciCode | 49.9% | 55.8% |
| LMArena Coding | 1457 | 1487 |
| DeepSWE | — | 75.2% |
| WeirdML | 63% | — |
| ALE-Bench | 1,306 | — |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, GPT-6.1 Sol: 39.6 (#26)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| APEX-Agents | — | 60% |
| GDP.pdf | — | 32% |
Reasoning GPT-6.1 Sol leads
DeepSeek V4 Flash: 53.7 (#30), GPT-6.1 Sol: 81.9 (#2)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| ARC-AGI-2 | 61.4% | 94.2% |
| NYT Connections (extended) | 89.6% | 95.5% |
| ARC-AGI-1 | 89% | 98.5% |
| CritPt | 16.6% | 31.7% |
| Chess Puzzles | 33% | 61% |
| LMArena Hard Prompts | 1444 | 1466 |
| Mystery Game Puzzles | 34% | 80% |
| Epoch Capabilities Index | 154.49 | 166.09 |
| SimpleBench | 61.1% | — |
| Kagi LLM Benchmark | 52.2% | — |
| EBR-Bench | — | 54.3% |
| DTBench | 90.9% | — |
| LMCA | 41.7% | — |
Math GPT-6.1 Sol leads
DeepSeek V4 Flash: 60.3 (#37), GPT-6.1 Sol: 93.7 (#1)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 93.7% |
| FrontierMath Tier 4 | 24.4% | 100% |
| OTIS Mock AIME 2024-2025 | 94.4% | 100% |
| ProofBench | 56% | 99% |
| LMArena Math | 1427 | 1464 |
| MathArena Final-Answer Competitions | 76.5% | — |
Knowledge GPT-6.1 Sol leads
DeepSeek V4 Flash: 55.4 (#48), GPT-6.1 Sol: 71.8 (#4)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| GPQA Diamond | 91% | 95.4% |
| SimpleQA Verified | 33.6% | 73.9% |
| LMArena Expert | 1441 | 1502 |
Multimodal Not comparable
DeepSeek V4 Flash: —, GPT-6.1 Sol: 52.7 (#5)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Vision | — | 1288 |
| Furniture Assembly | — | 80% |
Multilingual GPT-6.1 Sol leads
DeepSeek V4 Flash: 53.0 (#72), GPT-6.1 Sol: 54.3 (#46)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Non-English | 1420 | 1438 |
| LMArena Chinese | 1468 | 1477 |
| LMArena Russian | 1428 | 1455 |
| LMArena French | 1439 | — |
| LMArena German | 1418 | — |
| LMArena Japanese | 1406 | — |
| LMArena Korean | 1384 | — |
| LMArena Spanish | 1436 | — |
Instruction Following GPT-6.1 Sol leads
DeepSeek V4 Flash: 74.9 (#81), GPT-6.1 Sol: 77.0 (#29)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Instruction Following | 1421 | 1468 |
Long Context GPT-6.1 Sol leads
DeepSeek V4 Flash: 43.8 (#85), GPT-6.1 Sol: 44.9 (#54)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Longer Query | 1434 | 1465 |
Writing & Preference Too close to call
DeepSeek V4 Flash: 63.8 (#61), GPT-6.1 Sol: 63.6 (#63)
| Benchmark | DeepSeek V4 Flash | GPT-6.1 Sol |
|---|---|---|
| LMArena Text | 1432 | 1447 |
| LMArena Creative Writing | 1403 | 1432 |
| LMArena Multi-Turn | 1449 | 1449 |
| EQ-Bench Creative Writing | 1559 | — |
Frequently asked questions
Is DeepSeek V4 Flash better than GPT-6.1 Sol?
GPT-6.1 Sol is the stronger model overall, scoring 65.6 to 53.6 on the Noometry Index. DeepSeek V4 Flash costs 15× less per token, which makes it the better buy when GPT-6.1 Sol's lead doesn't matter for your workload.
Which is cheaper, DeepSeek V4 Flash or GPT-6.1 Sol?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-6.1 Sol lists at $2 and $10.
Is DeepSeek V4 Flash or GPT-6.1 Sol better for coding?
GPT-6.1 Sol scores higher on coding benchmarks: 63.2 versus 47.9 in the Noometry coding category.
Which has the bigger context window?
GPT-6.1 Sol does, with 1.05M tokens against 1M.
How many benchmarks do DeepSeek V4 Flash and GPT-6.1 Sol share?
28 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-6.1 Sol has 34.