Model comparison
DeepSeek V4 Flash vs GPT-4.1
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 35.9 on the Noometry Index.
Last verified . 32 shared benchmarks.
Summary
- They share 32 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 8 categories and GPT-4.1 in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 11.7.
- The biggest single-benchmark swing is ARC-AGI-1: 89% for DeepSeek V4 Flash and 5.5% for GPT-4.1.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $2 / $8 for GPT-4.1.
- GPT-4.1 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 | |
|---|---|---|
| Provider | DeepSeek | OpenAI |
| Noometry Index | 53.6 | 35.9 |
| Released | 2026-04-24 | 2025-04-14 |
| Weights | Open | Proprietary |
| Context window | 1M | 1.05M |
| Max output | 393K | 33K |
| Input $ / M tokens | $0.15 | $2 |
| Output $ / M tokens | $0.60 | $8 |
| Results tracked | 41 | 52 |
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Category by category
Coding DeepSeek V4 Flash leads
DeepSeek V4 Flash: 47.9 (#59), GPT-4.1: 34.4 (#238)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| WeirdML | 63% | 39% |
| LMArena Coding | 1457 | 1391 |
| ALE-Bench | 1,306 | 558.1 |
| SWE-bench Verified | — | 48.5% |
| FrontierCode | 18.8% | — |
| SWE-bench Verified (bash only) | — | 39.6% |
| Aider Polyglot | — | 52.4% |
| LMArena WebDev | 1582 | — |
| SciCode | 49.9% | — |
| CadEval | — | 42% |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, GPT-4.1: 34.7 (#43)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 54% |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#30), GPT-4.1: 11.7 (#339)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| ARC-AGI-2 | 61.4% | 0.4% |
| SimpleBench | 61.1% | 27% |
| Kagi LLM Benchmark | 52.2% | 52.3% |
| ARC-AGI-1 | 89% | 5.5% |
| Chess Puzzles | 33% | 6% |
| LMArena Hard Prompts | 1444 | 1384 |
| DTBench | 90.9% | 68.3% |
| LMCA | 41.7% | 25.6% |
| Epoch Capabilities Index | 154.49 | 136.78 |
| NYT Connections (extended) | 89.6% | — |
| CritPt | 16.6% | — |
| EnigmaEval | — | 2.2% |
| Mystery Game Puzzles | 34% | — |
| ForecastBench | — | 61.5 |
Math DeepSeek V4 Flash leads
DeepSeek V4 Flash: 60.3 (#37), GPT-4.1: 22.3 (#280)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 57.5% | 6% |
| OTIS Mock AIME 2024-2025 | 94.4% | 38.3% |
| LMArena Math | 1427 | 1370 |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| ProofBench | 56% | — |
| Omni-MATH | — | 47.1% |
| MATH Level 5 | — | 83% |
| FrontierMath (Feb 2025 set) | — | 5.5% |
| FrontierMath Tier 4 (v1) | — | 0% |
Knowledge DeepSeek V4 Flash leads
DeepSeek V4 Flash: 55.4 (#48), GPT-4.1: 37.1 (#160)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| GPQA Diamond | 91% | 66.9% |
| SimpleQA Verified | 33.6% | 31.1% |
| LMArena Expert | 1441 | 1364 |
| Humanity's Last Exam | — | 5.4% |
| MMLU-Pro | — | 81.1% |
| Vectara Hallucination Rate | — | 5.6% |
| GPQA (HELM) | — | 65.9% |
Multimodal Not comparable
DeepSeek V4 Flash: —, GPT-4.1: 38.2 (#67)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| LMArena Vision | — | 1211 |
| GeoBench | — | 72% |
Multilingual DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.0 (#72), GPT-4.1: 49.4 (#133)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| LMArena Non-English | 1420 | 1370 |
| LMArena Chinese | 1468 | 1382 |
| LMArena French | 1439 | 1382 |
| LMArena German | 1418 | 1381 |
| LMArena Japanese | 1406 | 1319 |
| LMArena Korean | 1384 | 1339 |
| LMArena Russian | 1428 | 1377 |
| LMArena Spanish | 1436 | 1376 |
Instruction Following DeepSeek V4 Flash leads
DeepSeek V4 Flash: 74.9 (#81), GPT-4.1: 71.3 (#153)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| LMArena Instruction Following | 1421 | 1367 |
| IFEval | — | 83.8% |
Long Context DeepSeek V4 Flash leads
DeepSeek V4 Flash: 43.8 (#85), GPT-4.1: 40.0 (#163)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| LMArena Longer Query | 1434 | 1385 |
| Fiction.LiveBench | — | 63.9% |
Writing & Preference DeepSeek V4 Flash leads
DeepSeek V4 Flash: 63.8 (#61), GPT-4.1: 57.6 (#125)
| Benchmark | DeepSeek V4 Flash | GPT-4.1 |
|---|---|---|
| LMArena Text | 1432 | 1383 |
| LMArena Creative Writing | 1403 | 1363 |
| EQ-Bench Creative Writing | 1559 | 1420 |
| LMArena Multi-Turn | 1449 | 1398 |
| WildBench | — | 85.4% |
Frequently asked questions
Is DeepSeek V4 Flash better than GPT-4.1?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 35.9 on the Noometry Index.
Which is cheaper, DeepSeek V4 Flash or GPT-4.1?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; GPT-4.1 lists at $2 and $8.
Is DeepSeek V4 Flash or GPT-4.1 better for coding?
DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 34.4 in the Noometry coding category.
Which has the bigger context window?
GPT-4.1 does, with 1.05M tokens against 1M.
How many benchmarks do DeepSeek V4 Flash and GPT-4.1 share?
32 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and GPT-4.1 has 52.