Model comparison
DeepSeek-R1 vs DeepSeek V4 Flash
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 42.3 on the Noometry Index.
Last verified . 29 shared benchmarks.
DeepSeekSummary
- They share 29 benchmarks with published results for both. DeepSeek-R1 scores higher in 1 category and DeepSeek V4 Flash in 7 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4 Flash leads 53.7 to 18.6.
- The biggest single-benchmark swing is ARC-AGI-1: 21.2% for DeepSeek-R1 and 89% for DeepSeek V4 Flash.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.50 / $2.15 for DeepSeek-R1.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 164K.
- DeepSeek V4 Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-R1 | DeepSeek V4 Flash | |
|---|---|---|
| Provider | DeepSeek | DeepSeek |
| Noometry Index | 42.3 | 53.6 |
| Released | 2025-01-20 | 2026-04-24 |
| Weights | Proprietary | Open |
| Context window | 164K | 1M |
| Max output | 64K | 393K |
| Input $ / M tokens | $0.50 | $0.15 |
| Output $ / M tokens | $2.15 | $0.60 |
| Results tracked | 52 | 41 |
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Category by category
Coding DeepSeek V4 Flash leads
DeepSeek-R1: 46.3 (#68), DeepSeek V4 Flash: 47.9 (#59)
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| SciCode | 35.7% | 49.9% |
| WeirdML | 41.6% | 63% |
| LMArena Coding | 1427 | 1457 |
| ALE-Bench | 804.12 | 1,306 |
| FrontierCode | — | 18.8% |
| Aider Polyglot | 71.4% | — |
| LMArena WebDev | — | 1582 |
| LiveBench Coding | 66.7% | — |
| AlgoTune | 1.7 | — |
Agentic & Tool Use Not comparable
DeepSeek-R1: 30.7 (#75), DeepSeek V4 Flash: —
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| DeepResearch Bench | 35.1% | — |
| BALROG | 34.9% | — |
| METR Time Horizons | 53.8% | — |
Reasoning DeepSeek V4 Flash leads
DeepSeek-R1: 18.6 (#278), DeepSeek V4 Flash: 53.7 (#30)
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| ARC-AGI-2 | 1.3% | 61.4% |
| SimpleBench | 40.8% | 61.1% |
| Kagi LLM Benchmark | 69.4% | 52.2% |
| ARC-AGI-1 | 21.2% | 89% |
| CritPt | 1.1% | 16.6% |
| LMArena Hard Prompts | 1416 | 1444 |
| Epoch Capabilities Index | 141.29 | 154.49 |
| NYT Connections (extended) | — | 89.6% |
| Chess Puzzles | — | 33% |
| LiveBench Reasoning | 83.2% | — |
| Mystery Game Puzzles | — | 34% |
| DTBench | — | 90.9% |
| LiveBench Data Analysis | 69.8% | — |
| LMCA | — | 41.7% |
| ForecastBench | 60 | — |
| LiveBench | 71.6% | — |
Math DeepSeek V4 Flash leads
DeepSeek-R1: 43.8 (#79), DeepSeek V4 Flash: 60.3 (#37)
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 66.4% | 94.4% |
| LMArena Math | 1400 | 1427 |
| FrontierMath (Tiers 1-3) | — | 57.5% |
| FrontierMath Tier 4 | — | 24.4% |
| MathArena Final-Answer Competitions | — | 76.5% |
| ProofBench | — | 56% |
| Omni-MATH | 42.4% | — |
| LiveBench Math | 80.7% | — |
| MATH Level 5 | 96.6% | — |
Knowledge DeepSeek V4 Flash leads
DeepSeek-R1: 44.5 (#87), DeepSeek V4 Flash: 55.4 (#48)
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| GPQA Diamond | 76.3% | 91% |
| LMArena Expert | 1394 | 1441 |
| SimpleQA Verified | — | 33.6% |
| MMLU-Pro | 79.3% | — |
| Confabulations | 12.7% | — |
| Vectara Hallucination Rate | 11.3% | — |
| GPQA (HELM) | 66.6% | — |
Multilingual Too close to call
DeepSeek-R1: 52.4 (#85), DeepSeek V4 Flash: 53.0 (#72)
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| LMArena Non-English | 1412 | 1420 |
| LMArena Chinese | 1442 | 1468 |
| LMArena French | 1417 | 1439 |
| LMArena German | 1404 | 1418 |
| LMArena Japanese | 1391 | 1406 |
| LMArena Korean | 1360 | 1384 |
| LMArena Russian | 1423 | 1428 |
| LMArena Spanish | 1411 | 1436 |
Instruction Following DeepSeek V4 Flash leads
DeepSeek-R1: 72.0 (#143), DeepSeek V4 Flash: 74.9 (#81)
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| LMArena Instruction Following | 1382 | 1421 |
| LiveBench Instruction Following | 80.5% | — |
| IFEval | 78.4% | — |
Long Context DeepSeek-R1 leads
DeepSeek-R1: 45.4 (#36), DeepSeek V4 Flash: 43.8 (#85)
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| LMArena Longer Query | 1391 | 1434 |
| Fiction.LiveBench | 75% | — |
Writing & Preference DeepSeek V4 Flash leads
DeepSeek-R1: 61.4 (#88), DeepSeek V4 Flash: 63.8 (#61)
| Benchmark | DeepSeek-R1 | DeepSeek V4 Flash |
|---|---|---|
| LMArena Text | 1428 | 1432 |
| LMArena Creative Writing | 1405 | 1403 |
| EQ-Bench Creative Writing | 1500 | 1559 |
| LMArena Multi-Turn | 1405 | 1449 |
| Short-Story Creative Writing | 83% | — |
| WildBench | 82.8% | — |
| LiveBench Language | 48.5% | — |
Frequently asked questions
Is DeepSeek-R1 better than DeepSeek V4 Flash?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 42.3 on the Noometry Index.
Which is cheaper, DeepSeek-R1 or DeepSeek V4 Flash?
DeepSeek V4 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek-R1 lists at $0.50 and $2.15.
Is DeepSeek-R1 or DeepSeek V4 Flash better for coding?
DeepSeek V4 Flash scores higher on coding benchmarks: 47.9 versus 46.3 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Flash does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-R1 and DeepSeek V4 Flash share?
29 benchmarks have published results for both models. DeepSeek-R1 has 52 scored results on Noometry and DeepSeek V4 Flash has 41.