DeepSeek V3.2 Exp vs Mercury 2
DeepSeek V3.2 Exp leads Mercury 2 on the Artificial Analysis Intelligence Index, 16.6 to 10.8, and DeepSeek V3.2 Exp costs 1.8x less per output token ($0.41 vs $0.75 per million). Updated October 22, 2026; 36 benchmarks have a result for both.
DeepSeek V3.2 Exp or Mercury 2: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 1.8x cheaper ($0.41 vs $0.75 per million) and a larger context window (164K against 128K tokens).
- Shared results
- 36
- DeepSeek V3.2 Exp only
- 23
- Mercury 2 only
- 6
- Like-for-like categories
- 7 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 V3.2 Exp
DeepSeek V3.2 Exp leads, 16.6 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 V3.2 Exp
DeepSeek V3.2 Exp leads, 1178 against 1036.
Same board, both measured
Long documents
Listed context window
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp takes 164K tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, Mercury 2 $0.0006 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, Mercury 2 not benchmarked.
- Widest category
- Agentic work, 36.8 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against Mercury 2 70.8%, 36.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against Mercury 2 $0.75 per million output tokens, 1.8x apart.
- Context window
- DeepSeek V3.2 Exp 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 V3.2 Exp | Bars | Mercury 2 | Ties |
|---|---|---|---|---|
| Coding | 5 | 1 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 4 | 1 | ||
| Knowledge | 3 | 0 | ||
| Web design | 7 | 0 | ||
| General | 12 | 1 | ||
| All shared boards | 32 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index51
| Benchmark | DeepSeek V3.2 Exp | Mercury 2 | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | 1171 | DeepSeek V3.2 Exp |
| AA Coding Index | Not measured | 31.1 | Only Mercury 2 measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SciCode | Not measured | 37.7% | Only Mercury 2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 26.5% | DeepSeek V3.2 Exp |
| WeirdML | 39.5% | 43.2% | Mercury 2 |
| ALE-Bench | Not measured | 785.6 | Only Mercury 2 measured |
| LMArena Text: coding | 1438 | 1390 | DeepSeek V3.2 Exp |
| LMArena Text: software and IT | 1438 | 1385 | DeepSeek V3.2 Exp |
| LMArena WebDev: React | Not measured | 1160 | Only Mercury 2 measured |
| LMArena WebDev: HTML | 1281 | 1199 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V3.2 Exp | Mercury 2 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 10.8 | DeepSeek V3.2 Exp |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | Mercury 2 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 1.6 | Only Mercury 2 measured |
| τ²-Bench Telecom | 33.9% | 70.8% | Mercury 2 |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | DeepSeek V3.2 Exp | Mercury 2 | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 77.0% | DeepSeek V3.2 Exp |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 17.1% | Mercury 2 |
| LMArena Text: hard prompts | 1429 | 1362 | DeepSeek V3.2 Exp |
| LMArena Text: expert | 1422 | 1358 | DeepSeek V3.2 Exp |
| LMArena Text: hard prompts in English | 1444 | 1374 | DeepSeek V3.2 Exp |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | Mercury 2 | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: math | 1422 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: mathematical work | 1433 | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V3.2 Exp | Mercury 2 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | 1443 | 1343 | DeepSeek V3.2 Exp |
| LMArena Text: medicine and healthcare | 1468 | 1363 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: business and finance | 1412 | 1349 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 13 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | DeepSeek V3.2 Exp | Mercury 2 | Leader |
|---|---|---|---|
| Design Arena: websites | 1178 | 1036 | DeepSeek V3.2 Exp |
| Design Arena: UI components | 1164 | 996 | DeepSeek V3.2 Exp |
| Design Arena: game dev | 1144 | 984 | DeepSeek V3.2 Exp |
| Design Arena: data viz | 1152 | 1009 | DeepSeek V3.2 Exp |
| Design Arena: 3D | 1148 | 982 | DeepSeek V3.2 Exp |
| Design Arena: all code categories | 1170 | 1021 | DeepSeek V3.2 Exp |
| Design Arena: SVG | 1031 | 998 | DeepSeek V3.2 Exp |
| Design Arena: ASCII art | Not measured | 988 | Only Mercury 2 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index121
| Benchmark | DeepSeek V3.2 Exp | Mercury 2 | Leader |
|---|---|---|---|
| IFBench | 54.1% | 69.8% | Mercury 2 |
| Long context reasoning (AA-LCR) | 72.3% | 43.7% | DeepSeek V3.2 Exp |
| DTBench | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMCA | 29.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1354 | DeepSeek V3.2 Exp |
| LMArena Text: instruction following | 1401 | 1328 | DeepSeek V3.2 Exp |
| LMArena Text: creative writing | 1404 | 1286 | DeepSeek V3.2 Exp |
| LMArena Text: English | 1441 | 1378 | DeepSeek V3.2 Exp |
| LMArena Text: non-English | 1404 | 1330 | DeepSeek V3.2 Exp |
| LMArena Text: Chinese | 1461 | 1414 | DeepSeek V3.2 Exp |
| LMArena Text: German | 1440 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Japanese | 1331 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | 1372 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: French | 1434 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Spanish | 1441 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Russian | 1412 | 1304 | DeepSeek V3.2 Exp |
| LMArena Text: Polish | 1435 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: multi-turn | 1422 | 1357 | DeepSeek V3.2 Exp |
| LMArena Text: longer queries | 1418 | 1329 | DeepSeek V3.2 Exp |
| LMArena Text: writing and language | 1407 | 1300 | DeepSeek V3.2 Exp |
| LMArena Text: entertainment and media | 1407 | 1314 | DeepSeek V3.2 Exp |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | 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 V3.2 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp 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 V3.2 Exp | Inception Mercury 2 |
|---|---|---|
| Maker | DeepSeek | Inception |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | March 4, 2026 |
| Context window | 164K | 128K |
| Input $/Mtok | $0.27 | $0.25 |
| Output $/Mtok | $0.41 | $0.75 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than Mercury 2 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 1.8x cheaper ($0.41 vs $0.75 per million) and a larger context window (164K against 128K tokens).
Which is cheaper, DeepSeek V3.2 Exp or Mercury 2?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 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 V3.2 Exp or Mercury 2?
DeepSeek V3.2 Exp, with 164K tokens against 128K.
How many benchmarks is the DeepSeek V3.2 Exp vs Mercury 2 comparison based on?
36 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.