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Gemini 3.6 Flash vs Mercury 2.5

Gemini 3.6 Flash leads Mercury 2.5 on coding, 62.5 to 1.6 on the Vibe Coding Index, and Mercury 2.5 costs 25.0x less per output token ($0.15 vs $3.75 per million). Updated October 21, 2026; 20 benchmarks have a result for both.

Gemini 3.6 FlashGoogle
Coding
62.5
Overall
68.3
Output $/M
$3.75
19
Mercury 2.5Inception
Coding
1.6
Overall
No data
Output $/M
$0.15
1
vs boards won of 20 shared

Gemini 3.6 Flash or Mercury 2.5: which to pick

Pick Gemini 3.6 Flash for coding: it scores 62.5 on the Vibe Coding Index against 1.6 for Mercury 2.5.

Pick Mercury 2.5 for output tokens 25.0x cheaper ($0.15 vs $3.75 per million) and work measured by CyberBench (Vals AI), where it leads.

Shared results
20
Gemini 3.6 Flash only
89
Mercury 2.5 only
2
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
Gemini 3.6 Flash 62.5 from 4 benchmarks, Mercury 2.5 1.6 from 2 benchmarks.
Widest category
Math, 33.0 points apart on average over 1 shared benchmark.
Widest single gap
Vibe Code Bench (Vals AI): Gemini 3.6 Flash 64.0% against Mercury 2.5 3.7%, 60.3 points apart on a 0 to 100 scale.
Output price
Gemini 3.6 Flash $3.75 against Mercury 2.5 $0.15 per million output tokens, 25.0x apart.
Context window
Gemini 3.6 Flash takes 1.05M tokens, Mercury 2.5 260K.

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
CategoryGemini 3.6 FlashBarsMercury 2.5Ties
Coding61
Intelligence10
Agentic work50
Reasoning10
Math10
Knowledge20
General30
All shared boards191
CodingLike-for-likeSix of these boards feed the Vibe Coding Index61
Coding benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
LMArena WebDev1538Not measuredOnly Gemini 3.6 Flash measured
AA Coding Index69.2Not measuredOnly Gemini 3.6 Flash measured
LiveCodeBench (Vals AI)88.1%Not measuredOnly Gemini 3.6 Flash measured
SWE-bench Verified (Vals AI)79.6%Not measuredOnly Gemini 3.6 Flash measured
Terminal-Bench 4 (Vals AI)Not measured0.0%Only Mercury 2.5 measured
Vibe Code Bench (Vals AI)64.0%3.7%Gemini 3.6 Flash
Code migration (Vals AI)30.9%4.5%Gemini 3.6 Flash
ProgramBench (Vals AI)0.0%Not measuredOnly Gemini 3.6 Flash measured
IOI (Vals AI)35.1%2.4%Gemini 3.6 Flash
Terminal-Bench 2.1 (Vals AI)73.8%34.5%Gemini 3.6 Flash
SkillsBench (Vals AI)Not measured18.1%Only Mercury 2.5 measured
CyberBench (Vals AI)45.4%45.5%Mercury 2.5
SciCode53.4%38.5%Gemini 3.6 Flash
FrontierCode34.4%Not measuredOnly Gemini 3.6 Flash measured
WeirdML56.1%Not measuredOnly Gemini 3.6 Flash measured
ALE-Bench715.5301.6Gemini 3.6 Flash
LMArena Text: coding1491Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: software and IT1492Not measuredOnly Gemini 3.6 Flash measured
LMArena WebDev: React1528Not measuredOnly Gemini 3.6 Flash measured
LMArena WebDev: HTML1547Not measuredOnly Gemini 3.6 Flash measured
LMArena WebDev: from an image1545Not measuredOnly Gemini 3.6 Flash measured
SciCode10.8%Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 17 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
AA Intelligence Index34.012.3Gemini 3.6 Flash
Epoch Capabilities Index154.5Not measuredOnly Gemini 3.6 Flash measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
Agentic work benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
AA Agentic Index29.0Not measuredOnly Gemini 3.6 Flash measured
Design Arena: agents building web apps1197Not measuredOnly Gemini 3.6 Flash measured
Design Arena: agents building full-stack apps1181Not measuredOnly Gemini 3.6 Flash measured
Design Arena: agents building mobile apps1199Not measuredOnly Gemini 3.6 Flash measured
Design Arena: agents building Android apps1203Not measuredOnly Gemini 3.6 Flash measured
Design Arena: agents building games1181Not measuredOnly Gemini 3.6 Flash measured
Design Arena: HTML slides1148Not measuredOnly Gemini 3.6 Flash measured
Design Arena: PowerPoint slides1146Not measuredOnly Gemini 3.6 Flash measured
Design Arena: spreadsheets1034Not measuredOnly Gemini 3.6 Flash measured
Finance Agent (Vals AI)56.3%18.9%Gemini 3.6 Flash
Tax Agent Bench (Vals AI)18.0%2.5%Gemini 3.6 Flash
Legal Research Bench (Vals AI)25.0%4.3%Gemini 3.6 Flash
Excel Modeling Benchmark (Vals AI)65.4%8.8%Gemini 3.6 Flash
BioMysteryBench (Vals AI)58.5%Not measuredOnly Gemini 3.6 Flash measured
Terminal-Bench-Science (Vals AI)4.3%0.0%Gemini 3.6 Flash
APEX-Agents46.9%Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 25 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
Reasoning benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
GPQA Diamond93.4%Not measuredOnly Gemini 3.6 Flash measured
MMMU Pro (Vals AI)88.4%Not measuredOnly Gemini 3.6 Flash measured
GPQA Diamond (Artificial Analysis run)92.8%Not measuredOnly Gemini 3.6 Flash measured
Humanity's Last Exam (Artificial Analysis run)40.8%11.8%Gemini 3.6 Flash
GPQA Diamond (Epoch AI run)94.1%Not measuredOnly Gemini 3.6 Flash measured
ARC-AGI91.2%Not measuredOnly Gemini 3.6 Flash measured
ARC-AGI-260.4%Not measuredOnly Gemini 3.6 Flash measured
Chess puzzles42.0%Not measuredOnly Gemini 3.6 Flash measured
Mystery game puzzles30.0%Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: hard prompts1486Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: expert1490Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: hard prompts in English1484Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: diagrams1314Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: homework1314Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 8 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index10
Math benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
OTIS Mock AIME94.2%Not measuredOnly Gemini 3.6 Flash measured
FrontierMath, tiers 1 to 358.9%Not measuredOnly Gemini 3.6 Flash measured
FrontierMath, tier 422.0%Not measuredOnly Gemini 3.6 Flash measured
ProofBench36.0%3.0%Gemini 3.6 Flash
LMArena Text: math1508Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: mathematical work1492Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index20
Knowledge benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
MMLU Pro89.3%Not measuredOnly Gemini 3.6 Flash measured
LegalBench (Vals AI)86.7%83.1%Gemini 3.6 Flash
CorpFin (Vals AI)63.3%Not measuredOnly Gemini 3.6 Flash measured
TaxEval (Vals AI)74.9%Not measuredOnly Gemini 3.6 Flash measured
MortgageTax (Vals AI)67.6%Not measuredOnly Gemini 3.6 Flash measured
MedCode (Vals AI)53.1%31.3%Gemini 3.6 Flash
SimpleQA Verified66.2%Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: science1489Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: medicine and healthcare1469Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: legal and government1479Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: business and finance1464Not measuredOnly Gemini 3.6 Flash measured
FACTS Benchmark Suite65.3%Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
Design Arena: websites1300Not measuredOnly Gemini 3.6 Flash measured
Design Arena: UI components1297Not measuredOnly Gemini 3.6 Flash measured
Design Arena: game dev1263Not measuredOnly Gemini 3.6 Flash measured
Design Arena: data viz1289Not measuredOnly Gemini 3.6 Flash measured
Design Arena: 3D1272Not measuredOnly Gemini 3.6 Flash measured
Design Arena: all code categories1290Not measuredOnly Gemini 3.6 Flash measured
Design Arena: ASCII art1281Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index30
General benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
Public Benefits Bench (Vals AI)56.6%45.3%Gemini 3.6 Flash
SAGE (Vals AI)48.7%Not measuredOnly Gemini 3.6 Flash measured
MedScribe (Vals AI)79.7%55.1%Gemini 3.6 Flash
Vals Multimodal Index65.1%Not measuredOnly Gemini 3.6 Flash measured
Long context reasoning (AA-LCR)80.0%71.7%Gemini 3.6 Flash
DTBench95.5%Not measuredOnly Gemini 3.6 Flash measured
LMCA44.9%Not measuredOnly Gemini 3.6 Flash measured
Furniture assembly23.3%Not measuredOnly Gemini 3.6 Flash measured
LMArena Text1480Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: instruction following1467Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: creative writing1466Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision1299Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: English1482Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: non-English1470Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: Chinese1531Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: German1480Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: Japanese1475Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: Korean1429Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: French1503Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: Spanish1476Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: Russian1488Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: Polish1473Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: multi-turn1481Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: longer queries1475Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: writing and language1470Not measuredOnly Gemini 3.6 Flash measured
LMArena Text: entertainment and media1456Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: English1295Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: Chinese1357Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: reading text1312Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: humor1289Not measuredOnly Gemini 3.6 Flash measured
LMArena Vision: creative writing1278Not measuredOnly Gemini 3.6 Flash measured
LMArena Document1456Not measuredOnly Gemini 3.6 Flash measured
Neither model has a result on the other 14 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 3.6 Flash and Mercury 2.5
BenchmarkGemini 3.6 FlashMercury 2.5Leader
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

Gemini 3.6 Flash$0.0026Fits in one request
Mercury 2.5$0.0001Fits in one request

Mercury 2.5 costs less

Repository review

50K input + 3K output tokens

Gemini 3.6 Flash$0.049Fits in one request
Mercury 2.5$0.0024Fits in one request

Mercury 2.5 costs less

Agent loop

200K input + 10K output tokens

Gemini 3.6 Flash$0.188Fits in one request
Mercury 2.5$0.0095Fits in one request

Mercury 2.5 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 Gemini 3.6 Flash and Mercury 2.5
SpecificationGoogle Gemini 3.6 FlashInception Mercury 2.5
MakerGoogleInception
WeightsNo public weights listedNo public weights listed
ReleasedJuly 21, 2026September 8, 2026
Context window1.05M260K
Input $/Mtok$0.75$0.04
Output $/Mtok$3.75$0.15
Coding index62.51.6
Independent measurements behind it42
Cost per solved task$1.49Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.6 Flash better than Mercury 2.5 for coding?

Pick Gemini 3.6 Flash for coding: it scores 62.5 on the Vibe Coding Index against 1.6 for Mercury 2.5. Pick Mercury 2.5 for output tokens 25.0x cheaper ($0.15 vs $3.75 per million) and work measured by CyberBench (Vals AI), where it leads.

Which is cheaper, Gemini 3.6 Flash or Mercury 2.5?

Gemini 3.6 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; Mercury 2.5 costs $0.04 and $0.15. Cost per solved task is known for Gemini 3.6 Flash only.

Which has the bigger context window, Gemini 3.6 Flash or Mercury 2.5?

Gemini 3.6 Flash, with 1.05M tokens against 260K.

How many benchmarks is the Gemini 3.6 Flash vs Mercury 2.5 comparison based on?

20 benchmarks have a result for both models as of October 21, 2026. Gemini 3.6 Flash's coding index rests on 4 independent benchmarks and Mercury 2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.

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<p style="font:13px/1.4 sans-serif;margin:6px 0 0">Source: <a href="https://llmbenchmark.ai/compare/gemini-3-6-flash-vs-inception-mercury-2-5">LLMBenchmark.ai, Gemini 3.6 Flash vs Mercury 2.5</a></p>