Model comparison
DeepSeek V4 Flash vs GPT-4.1 nano
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 27.9 on the Noometry Index.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and GPT-4.1 nano in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 8.5.
- The biggest single-benchmark swing is ARC-AGI-1: 89% for DeepSeek V4 Flash and 0% for GPT-4.1 nano.
- GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.15 / $0.60 for DeepSeek V4 Flash.
- GPT-4.1 nano 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-4.1 nano | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 53.6 | 27.9 |
| Released | 2026-04-24 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 393K | 33K |
| Input $ / M tokens | $0.15 | $0.10 |
| Output $ / M tokens | $0.60 | $0.40 |
| Results tracked | 41 | 38 |
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Category by category
Coding DeepSeek V4 Flash leads
DeepSeek V4 Flash: 47.9 (#59), GPT-4.1 nano: 24.1 (#330)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| SciCode | 49.9% | 25.9% |
| WeirdML | 63% | 19% |
| LMArena Coding | 1457 | 1306 |
| FrontierCode | 18.8% | — |
| Aider Polyglot | — | 8.9% |
| LMArena WebDev | 1582 | — |
| ALE-Bench | 1,306 | — |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, GPT-4.1 nano: 26.5 (#104)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 33% |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#30), GPT-4.1 nano: 8.5 (#349)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| ARC-AGI-2 | 61.4% | 0% |
| Kagi LLM Benchmark | 52.2% | 33.3% |
| ARC-AGI-1 | 89% | 0% |
| CritPt | 16.6% | 0% |
| LMArena Hard Prompts | 1444 | 1286 |
| DTBench | 90.9% | 52.5% |
| LMCA | 41.7% | 5.5% |
| Epoch Capabilities Index | 154.49 | 129.62 |
| SimpleBench | 61.1% | — |
| NYT Connections (extended) | 89.6% | — |
| Chess Puzzles | 33% | — |
| Mystery Game Puzzles | 34% | — |
Math DeepSeek V4 Flash leads
DeepSeek V4 Flash: 60.3 (#37), GPT-4.1 nano: 26.9 (#252)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 94.4% | 28.9% |
| LMArena Math | 1427 | 1274 |
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| ProofBench | 56% | — |
| Omni-MATH | — | 36.7% |
| MATH Level 5 | — | 70% |
| FrontierMath (Feb 2025 set) | — | 1% |
Knowledge DeepSeek V4 Flash leads
DeepSeek V4 Flash: 55.4 (#48), GPT-4.1 nano: 21.8 (#273)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| GPQA Diamond | 91% | 48.9% |
| SimpleQA Verified | 33.6% | 6% |
| LMArena Expert | 1441 | 1272 |
| MMLU-Pro | — | 55% |
| GPQA (HELM) | — | 50.7% |
Multimodal Not comparable
DeepSeek V4 Flash: —, GPT-4.1 nano: 29.2 (#113)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Vision | — | 1063 |
Multilingual DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.0 (#72), GPT-4.1 nano: 41.6 (#205)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Non-English | 1420 | 1260 |
| LMArena Chinese | 1468 | 1270 |
| LMArena German | 1418 | 1288 |
| LMArena Japanese | 1406 | 1198 |
| LMArena Russian | 1428 | 1261 |
| LMArena French | 1439 | — |
| LMArena Korean | 1384 | — |
| LMArena Spanish | 1436 | — |
Instruction Following DeepSeek V4 Flash leads
DeepSeek V4 Flash: 74.9 (#81), GPT-4.1 nano: 67.8 (#193)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Instruction Following | 1421 | 1267 |
| IFEval | — | 84.3% |
Long Context DeepSeek V4 Flash leads
DeepSeek V4 Flash: 43.8 (#85), GPT-4.1 nano: 23.7 (#296)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Longer Query | 1434 | 1283 |
| Fiction.LiveBench | — | 25% |
Writing & Preference DeepSeek V4 Flash leads
DeepSeek V4 Flash: 63.8 (#61), GPT-4.1 nano: 40.5 (#243)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 nano |
|---|---|---|
| LMArena Text | 1432 | 1285 |
| LMArena Creative Writing | 1403 | 1260 |
| EQ-Bench Creative Writing | 1559 | 946 |
| LMArena Multi-Turn | 1449 | 1277 |
| WildBench | — | 81.2% |
Frequently asked questions
Is DeepSeek V4 Flash better than GPT-4.1 nano?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 27.9 on the Noometry Index.
Which is cheaper, DeepSeek V4 Flash or GPT-4.1 nano?
GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; DeepSeek V4 Flash lists at $0.15 and $0.60.
Is DeepSeek V4 Flash or GPT-4.1 nano better for coding?
DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 24.1 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 nano does, with 1.05M tokens against 1M.
How many benchmarks do DeepSeek V4 Flash and GPT-4.1 nano share?
27 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-4.1 nano has 38.