DeepSeek V4.1 Flash 1017 vs Mercury 2
DeepSeek V4.1 Flash 1017 leads Mercury 2 on the Artificial Analysis Intelligence Index, 39.5 to 10.8, and DeepSeek V4.1 Flash 1017 costs 1.3x less per output token ($0.60 vs $0.75 per million). Updated October 22, 2026; 15 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or Mercury 2: which to pick
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; Mercury 2 has none, so the two cannot be ranked on coding.
Compare them on price, context and the boards below.
- Shared results
- 15
- DeepSeek V4.1 Flash 1017 only
- 24
- Mercury 2 only
- 27
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only DeepSeek V4.1 Flash 1017 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 10.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Mercury 2 has a AA Agentic Index result, 1.6.
Directional only
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1311 against 1036.
Same board, both measured
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
Long documents
Listed context window
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, Mercury 2 $0.0006 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, Mercury 2 not benchmarked.
- Widest category
- Coding, 50.5 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: DeepSeek V4.1 Flash 1017 1667 against Mercury 2 1160, 73.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against Mercury 2 $0.75 per million output tokens, 1.3x apart.
- Context window
- DeepSeek V4.1 Flash 1017 takes 1.05M tokens, Mercury 2 128K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V4.1 Flash 1017 | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Web design | 7 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 15 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1664 | 1171 | DeepSeek V4.1 Flash 1017 |
| AA Coding Index | Not measured | 31.1 | Only Mercury 2 measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SciCode | 55.9% | 37.7% | DeepSeek V4.1 Flash 1017 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 26.5% | Only Mercury 2 measured |
| WeirdML | Not measured | 43.2% | Only Mercury 2 measured |
| ALE-Bench | 1622.0 | 785.6 | DeepSeek V4.1 Flash 1017 |
| LMArena Text: coding | Not measured | 1390 | Only Mercury 2 measured |
| LMArena Text: software and IT | Not measured | 1385 | Only Mercury 2 measured |
| LMArena WebDev: React | 1667 | 1160 | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev: HTML | 1687 | 1199 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 25 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 10.8 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| Design Arena: spreadsheets | 1245 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench browser | 76.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench long agentic | 50.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Finance Agent (Vals AI) | 57.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Tax Agent Bench (Vals AI) | 44.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | 61.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| τ²-Bench Telecom | Not measured | 70.8% | Only Mercury 2 measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 77.0% | Only Mercury 2 measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 17.1% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 93.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | 49.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: hard prompts | Not measured | 1362 | Only Mercury 2 measured |
| LMArena Text: expert | Not measured | 1358 | Only Mercury 2 measured |
| LMArena Text: hard prompts in English | Not measured | 1374 | Only Mercury 2 measured |
| Neither model has a result on the other 13 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tiers 1 to 3 | 71.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tier 4 | 46.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena Text: science | Not measured | 1343 | Only Mercury 2 measured |
| LMArena Text: medicine and healthcare | Not measured | 1363 | Only Mercury 2 measured |
| LMArena Text: business and finance | Not measured | 1349 | Only Mercury 2 measured |
| Neither model has a result on the other 14 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1311 | 1036 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1303 | 996 | DeepSeek V4.1 Flash 1017 |
| Design Arena: game dev | 1357 | 984 | DeepSeek V4.1 Flash 1017 |
| Design Arena: data viz | 1330 | 1009 | DeepSeek V4.1 Flash 1017 |
| Design Arena: 3D | 1358 | 982 | DeepSeek V4.1 Flash 1017 |
| Design Arena: all code categories | 1332 | 1021 | DeepSeek V4.1 Flash 1017 |
| Design Arena: SVG | Not measured | 998 | Only Mercury 2 measured |
| Design Arena: ASCII art | 1242 | 988 | DeepSeek V4.1 Flash 1017 |
| LiveBench design | 83.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| IFBench | Not measured | 69.8% | Only Mercury 2 measured |
| Long context reasoning (AA-LCR) | 82.7% | 43.7% | DeepSeek V4.1 Flash 1017 |
| LMArena Text | Not measured | 1354 | Only Mercury 2 measured |
| LMArena Text: instruction following | Not measured | 1328 | Only Mercury 2 measured |
| LMArena Text: creative writing | Not measured | 1286 | Only Mercury 2 measured |
| LMArena Text: English | Not measured | 1378 | Only Mercury 2 measured |
| LMArena Text: non-English | Not measured | 1330 | Only Mercury 2 measured |
| LMArena Text: Chinese | Not measured | 1414 | Only Mercury 2 measured |
| LMArena Text: Russian | Not measured | 1304 | Only Mercury 2 measured |
| LMArena Text: multi-turn | Not measured | 1357 | Only Mercury 2 measured |
| LMArena Text: longer queries | Not measured | 1329 | Only Mercury 2 measured |
| LMArena Text: writing and language | Not measured | 1300 | Only Mercury 2 measured |
| LMArena Text: entertainment and media | Not measured | 1314 | Only Mercury 2 measured |
| Neither model has a result on the other 31 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Mercury 2 | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash 1017 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 1017 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V4.1 Flash 1017 | Inception Mercury 2 |
|---|---|---|
| Maker | DeepSeek | Inception |
| Weights | Open weights | No public weights listed |
| Released | October 17, 2026 | March 4, 2026 |
| Context window | 1.05M | 128K |
| Input $/Mtok | $0.15 | $0.25 |
| Output $/Mtok | $0.60 | $0.75 |
| Coding index | 70.3 | Not benchmarked |
| Independent measurements behind it | 4 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than Mercury 2 for coding?
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; Mercury 2 has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.
Which is cheaper, DeepSeek V4.1 Flash 1017 or Mercury 2?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Mercury 2?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 128K.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Mercury 2 comparison based on?
15 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 independent benchmarks and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.