DeepSeek V3.1 vs Mercury 2
DeepSeek V3.1 leads Mercury 2 on the Artificial Analysis Intelligence Index, 13.7 to 10.8, and Mercury 2 costs 2.0x less per output token ($0.75 vs $1.50 per million). Updated October 21, 2026; 8 benchmarks have a result for both.
DeepSeek V3.1 or Mercury 2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 for a larger context window (164K against 128K tokens) and work measured by Design Arena: SVG, where it leads.
- Shared results
- 8
- DeepSeek V3.1 only
- 3
- Mercury 2 only
- 34
- Like-for-like categories
- 2 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V3.1
DeepSeek V3.1 leads, 13.7 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 V3.1
DeepSeek V3.1 leads, 1123 against 1036.
Same board, both measured
Long documents
Listed context window
DeepSeek V3.1
DeepSeek V3.1 takes 164K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Mercury 2
DeepSeek V3.1 $0.0013, Mercury 2 $0.0006 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 not benchmarked, Mercury 2 not benchmarked.
- Widest category
- Web design, 10.3 points apart on average over 7 shared benchmarks.
- Widest single gap
- Design Arena: game dev: DeepSeek V3.1 1084 against Mercury 2 985, 12.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 $1.50 against Mercury 2 $0.75 per million output tokens, 2.0x apart.
- Context window
- DeepSeek V3.1 takes 164K 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 V3.1 | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Web design | 6 | 1 | ||
| All shared boards | 7 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index10 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2 | Leader |
|---|---|---|---|
| LMArena WebDev | Not measured | 1171 | Only Mercury 2 measured |
| AA Coding Index | Not measured | 31.1 | Only Mercury 2 measured |
| SciCode | Not measured | 37.7% | Only Mercury 2 measured |
| 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 | Not measured | 785.6 | Only Mercury 2 measured |
| 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 | Not measured | 1160 | Only Mercury 2 measured |
| LMArena WebDev: HTML | Not measured | 1199 | Only Mercury 2 measured |
| Neither model has a result on the other 29 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.1 | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 13.7 | 10.8 | DeepSeek V3.1 |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| τ²-Bench Telecom | Not measured | 70.8% | Only Mercury 2 measured |
| Neither model has a result on the other 39 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | Not measured | 77.0% | Only Mercury 2 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 17.1% | Only Mercury 2 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 |
| Kagi LLM Benchmark | 53.2% | Not measured | Only DeepSeek V3.1 measured |
| Neither model has a result on the other 16 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2 | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2 | Leader |
|---|---|---|---|
| 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 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index61
| Benchmark | DeepSeek V3.1 | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1123 | 1036 | DeepSeek V3.1 |
| Design Arena: UI components | 1085 | 998 | DeepSeek V3.1 |
| Design Arena: game dev | 1084 | 985 | DeepSeek V3.1 |
| Design Arena: data viz | 1095 | 1009 | DeepSeek V3.1 |
| Design Arena: 3D | 1077 | 983 | DeepSeek V3.1 |
| Design Arena: all code categories | 1112 | 1022 | DeepSeek V3.1 |
| Design Arena: SVG | 967 | 998 | Mercury 2 |
| Design Arena: ASCII art | Not measured | 988 | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index15 boards, none shared
| Benchmark | DeepSeek V3.1 | Mercury 2 | Leader |
|---|---|---|---|
| IFBench | Not measured | 69.8% | Only Mercury 2 measured |
| Long context reasoning (AA-LCR) | Not measured | 43.7% | Only Mercury 2 measured |
| DTBench | 82.7% | Not measured | Only DeepSeek V3.1 measured |
| LMCA | 24.3% | Not measured | Only DeepSeek V3.1 measured |
| 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 V3.1 | 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
Mercury 2 costs less
Repository review
50K input + 3K output tokens
Mercury 2 costs less
Agent loop
200K input + 10K output tokens
Mercury 2 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 V3.1 | Inception Mercury 2 |
|---|---|---|
| Maker | DeepSeek | Inception |
| Weights | Open weights | No public weights listed |
| Released | August 21, 2025 | March 4, 2026 |
| Context window | 164K | 128K |
| Input $/Mtok | $0.55 | $0.25 |
| Output $/Mtok | $1.50 | $0.75 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 better than Mercury 2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 for a larger context window (164K against 128K tokens) and work measured by Design Arena: SVG, where it leads.
Which is cheaper, DeepSeek V3.1 or Mercury 2?
DeepSeek V3.1 costs $0.55 per million input tokens and $1.50 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 V3.1 or Mercury 2?
DeepSeek V3.1, with 164K tokens against 128K.
How many benchmarks is the DeepSeek V3.1 vs Mercury 2 comparison based on?
8 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.1's coding index rests on 0 independent benchmarks and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.