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DeepSeek V3.2 vs Mercury 2

DeepSeek V3.2 leads Mercury 2 on the Artificial Analysis Intelligence Index, 21.5 to 10.8, at about the same output price ($0.88 vs $0.75 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace Mercury 2
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
37
Mercury 2Inception
Coding
No data
Overall
No data
Output $/M
$0.75
1
vs boards won of 38 shared

DeepSeek V3.2 or Mercury 2: which to pick

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Mercury 2 has none, so the two cannot be ranked on coding.

Pick Mercury 2 for output tokens 1.2x cheaper ($0.75 vs $0.88 per million) and work measured by IFBench, where it leads.

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BMercury 2InceptionNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
38
DeepSeek V3.2 only
37
Mercury 2 only
4
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.

At a glance

Coding index
DeepSeek V3.2 30.0 from 3 benchmarks, Mercury 2 not benchmarked.
Widest category
Agentic work, 19.9 points apart on average over 1 shared benchmark.
Widest single gap
LMArena WebDev: React: DeepSeek V3.2 1350 against Mercury 2 1160, 31.7 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against Mercury 2 $0.75 per million output tokens, 1.2x apart.
Context window
DeepSeek V3.2 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.

Boards won, where both have a result
CategoryDeepSeek V3.2BarsMercury 2Ties
Coding70
Intelligence10
Agentic work10
Reasoning50
Knowledge30
Web design80
General121
All shared boards371
CodingLike-for-likeSix of these boards feed the Vibe Coding Index70
Coding benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev13621171DeepSeek V3.2
AA Coding Index44.231.1DeepSeek V3.2
LiveCodeBench (Vals AI)80.7%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)67.6%Not measuredOnly DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)5.1%Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
SciCodeNot measured37.7%Only Mercury 2 measured
Terminal-Bench Hard (Artificial Analysis run)35.6%26.5%DeepSeek V3.2
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
WeirdMLNot measured43.2%Only Mercury 2 measured
ALE-BenchNot measured785.6Only Mercury 2 measured
LMArena Text: coding14531390DeepSeek V3.2
LMArena Text: software and IT14491385DeepSeek V3.2
LMArena WebDev: React13501160DeepSeek V3.2
LMArena WebDev: HTML13711199DeepSeek V3.2
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
AA Intelligence Index21.510.8DeepSeek V3.2
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V3.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
AA Agentic IndexNot measured1.6Only Mercury 2 measured
τ²-Bench Telecom90.6%70.8%DeepSeek V3.2
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
Reasoning benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)84.0%77.0%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)24.6%17.1%DeepSeek V3.2
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: hard prompts14341362DeepSeek V3.2
LMArena Text: expert14361358DeepSeek V3.2
LMArena Text: hard prompts in English14461374DeepSeek V3.2
Neither model has a result on the other 14 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
AIME84.6%Not measuredOnly DeepSeek V3.2 measured
MGSM (Vals AI)86.0%Not measuredOnly DeepSeek V3.2 measured
AA Math Index92.0Not measuredOnly DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)92.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: math1435Not measuredOnly DeepSeek V3.2 measured
LMArena Text: mathematical work1440Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index30
Knowledge benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
MMLU Pro84.9%Not measuredOnly DeepSeek V3.2 measured
LegalBench (Vals AI)76.1%Not measuredOnly DeepSeek V3.2 measured
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%Not measuredOnly DeepSeek V3.2 measured
TaxEval (Vals AI)68.2%Not measuredOnly DeepSeek V3.2 measured
MedQA (Vals AI)93.9%Not measuredOnly DeepSeek V3.2 measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: science14461343DeepSeek V3.2
LMArena Text: medicine and healthcare14401363DeepSeek V3.2
LMArena Text: legal and government1425Not measuredOnly DeepSeek V3.2 measured
LMArena Text: business and finance14101349DeepSeek V3.2
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index80
Web design benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
Design Arena: websites11751036DeepSeek V3.2
Design Arena: UI components1146996DeepSeek V3.2
Design Arena: game dev1131984DeepSeek V3.2
Design Arena: data viz11581009DeepSeek V3.2
Design Arena: 3D1132982DeepSeek V3.2
Design Arena: all code categories11631021DeepSeek V3.2
Design Arena: SVG1032998DeepSeek V3.2
Design Arena: ASCII art1083988DeepSeek V3.2
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index121
General benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
IFBench60.7%69.8%Mercury 2
Long context reasoning (AA-LCR)73.3%43.7%DeepSeek V3.2
DTBench85.6%Not measuredOnly DeepSeek V3.2 measured
LMCA28.8%Not measuredOnly DeepSeek V3.2 measured
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251354DeepSeek V3.2
LMArena Text: instruction following14131328DeepSeek V3.2
LMArena Text: creative writing14001286DeepSeek V3.2
LMArena Text: English14391378DeepSeek V3.2
LMArena Text: non-English14091330DeepSeek V3.2
LMArena Text: Chinese14551414DeepSeek V3.2
LMArena Text: German1413Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Japanese1374Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Korean1370Not measuredOnly DeepSeek V3.2 measured
LMArena Text: French1429Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Spanish1424Not measuredOnly DeepSeek V3.2 measured
LMArena Text: Russian14241304DeepSeek V3.2
LMArena Text: Polish1419Not measuredOnly DeepSeek V3.2 measured
LMArena Text: multi-turn14271357DeepSeek V3.2
LMArena Text: longer queries14281329DeepSeek V3.2
LMArena Text: writing and language14071300DeepSeek V3.2
LMArena Text: entertainment and media13951314DeepSeek V3.2
Neither model has a result on the other 23 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 and Mercury 2
BenchmarkDeepSeek V3.2Mercury 2Leader
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$0.0007Fits in one request
Mercury 2$0.0006Fits in one request

Mercury 2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
Mercury 2$0.015Fits in one request

Mercury 2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
Mercury 2$0.058Exceeds the context window

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.

Specifications of DeepSeek V3.2 and Mercury 2
SpecificationDeepSeek V3.2Inception Mercury 2
MakerDeepSeekInception
WeightsOpen weightsNo public weights listed
ReleasedDecember 1, 2025March 4, 2026
Context window164K128K
Input $/Mtok$0.29$0.25
Output $/Mtok$0.88$0.75
Coding index30.0Not benchmarked
Independent measurements behind it31
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than Mercury 2 for coding?

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Mercury 2 has none, so the two cannot be ranked on coding. Pick Mercury 2 for output tokens 1.2x cheaper ($0.75 vs $0.88 per million) and work measured by IFBench, where it leads.

Which is cheaper, DeepSeek V3.2 or Mercury 2?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Mercury 2 costs $0.25 and $0.75. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or Mercury 2?

DeepSeek V3.2, with 164K tokens against 128K.

How many benchmarks is the DeepSeek V3.2 vs Mercury 2 comparison based on?

38 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 independent benchmarks and Mercury 2's on 1. Where only one model has a result, the page says so instead of naming a winner.

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