Model comparison
DeepSeek V4.1 Flash vs Mercury 2
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 39.1 on the Noometry Index.
Last verified . 15 shared benchmarks.
DeepSeekSummary
- They share 15 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 7 categories and Mercury 2 in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where DeepSeek V4.1 Flash leads 50.2 to 23.8.
- The biggest single-benchmark swing is CritPt: 14.3% for DeepSeek V4.1 Flash and 0.8% for Mercury 2.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.25 / $0.75 for Mercury 2.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 128K.
- DeepSeek V4.1 Flash has downloadable open weights; the other is API-only.
Side by side
| DeepSeek V4.1 Flash | Mercury 2 | |
|---|---|---|
| Provider | DeepSeek | Inception |
| Noometry Index | 52.8 | 39.1 |
| Released | 2026-09-09 | 2026-02-20 |
| Weights | Open | Proprietary |
| Context window | 1M | 128K |
| Max output | 393K | 50K |
| Input $ / M tokens | $0.15 | $0.25 |
| Output $ / M tokens | $0.60 | $0.75 |
| Results tracked | 37 | 17 |
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Category by category
Coding DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 52.9 (#32), Mercury 2: 33.5 (#255)
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| LMArena WebDev | 1619 | 1171 |
| SciCode | 51.9% | 38.7% |
| LMArena Coding | 1506 | 1391 |
| ALE-Bench | 1,092 | 785.58 |
| WeirdML | — | 43.2% |
Agentic & Tool Use Not comparable
DeepSeek V4.1 Flash: 31.2 (#69), Mercury 2: —
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| APEX-Agents | 39.5% | — |
| GDP.pdf | 19.8% | — |
Reasoning DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 50.2 (#36), Mercury 2: 23.8 (#170)
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| CritPt | 14.3% | 0.8% |
| LMArena Hard Prompts | 1483 | 1362 |
| NYT Connections (extended) | 89.6% | — |
| Mystery Game Puzzles | 43% | — |
| DTBench | 89.9% | — |
| LMCA | 47% | — |
| Surface Evolver Bench | 46.3% | — |
| Epoch Capabilities Index | 154.9 | — |
Math Not comparable
DeepSeek V4.1 Flash: 66.7 (#25), Mercury 2: —
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
| ProofBench | 54% | — |
| LMArena Math | 1477 | — |
Knowledge DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 57.9 (#38), Mercury 2: 36.2 (#172)
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| LMArena Expert | 1506 | 1358 |
| GPQA Diamond | 89.8% | — |
| Vectara Hallucination Rate | — | 12.3% |
Multimodal Not comparable
DeepSeek V4.1 Flash: 39.1 (#61), Mercury 2: —
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| LMArena Vision | 1277 | — |
| Furniture Assembly | 34.2% | — |
Multilingual DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 55.0 (#35), Mercury 2: 46.6 (#157)
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| LMArena Non-English | 1448 | 1331 |
| LMArena Chinese | 1497 | 1417 |
| LMArena Russian | 1471 | 1304 |
| LMArena French | 1452 | — |
| LMArena German | 1484 | — |
| LMArena Japanese | 1412 | — |
| LMArena Korean | 1452 | — |
| LMArena Spanish | 1459 | — |
Instruction Following DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 77.3 (#26), Mercury 2: 70.2 (#165)
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| LMArena Instruction Following | 1474 | 1329 |
Long Context DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 45.2 (#47), Mercury 2: 40.5 (#154)
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| LMArena Longer Query | 1475 | 1330 |
Writing & Preference DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 65.4 (#48), Mercury 2: 53.8 (#155)
| Benchmark | DeepSeek V4.1 Flash | Mercury 2 |
|---|---|---|
| LMArena Text | 1462 | 1355 |
| LMArena Creative Writing | 1435 | 1289 |
| LMArena Multi-Turn | 1457 | 1358 |
| EQ-Bench Creative Writing | 1540 | — |
Frequently asked questions
Is DeepSeek V4.1 Flash better than Mercury 2?
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 39.1 on the Noometry Index.
Which is cheaper, DeepSeek V4.1 Flash or Mercury 2?
DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Mercury 2 lists at $0.25 and $0.75.
Is DeepSeek V4.1 Flash or Mercury 2 better for coding?
DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 33.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4.1 Flash does, with 1M tokens against 128K.
How many benchmarks do DeepSeek V4.1 Flash and Mercury 2 share?
15 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Mercury 2 has 17.