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Gemini 3.8 Flash vs Kimi K2.7 Code

Updated October 21, 2026. 45 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Gemini 3.8 FlashGoogle
Coding
70.5
Overall
40.9
Output $/M
$4.125
44
Kimi K2.7 CodeMoonshot AI
Coding
39.1
Overall
25.8
Output $/M
$3.35
1
vs boards won of 45 shared

Decision reading

Pick Gemini 3.8 Flash for coding: it scores 70.5 on the Vibe Coding Index against 39.1 for Kimi K2.7 Code.

Pick Kimi K2.7 Code when output tokens cost 1.2x less, or when a solved task costs $0.35 against $2.74.

Shared results
45
Gemini 3.8 Flash only
81
Kimi K2.7 Code only
6
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 3.8 Flash 70.5 from 2 benchmarks, Kimi K2.7 Code 39.1 from 1 benchmark.
Widest category
Knowledge, 33.2 points apart on average over 1 shared benchmark.
Widest single gap
Chess puzzles: Gemini 3.8 Flash 58.0% against Kimi K2.7 Code 14.0%, 44.0 points apart on a 0 to 100 scale.
Output price
Gemini 3.8 Flash $4.125 against Kimi K2.7 Code $3.35 per million output tokens, 1.2x apart.
Context window
Gemini 3.8 Flash takes 1.05M tokens, Kimi K2.7 Code 262K.

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 3.8 FlashBarsKimi K2.7 CodeTies
Coding160
Intelligence20
Agentic work81
Reasoning40
Math30
Knowledge10
Web design80
General20
All shared boards441
CodingLike-for-likeFour of these boards feed the Vibe Coding Index160
Coding benchmarks for Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
LiveBench coding82.2%Not measuredOnly Gemini 3.8 Flash measured
LMArena WebDev15851474Gemini 3.8 Flash
AA Coding Index76.360.8Gemini 3.8 Flash
LiveCodeBench (Vals AI)89.5%82.0%Gemini 3.8 Flash
SWE-bench Verified (Vals AI)80.0%78.2%Gemini 3.8 Flash
Terminal-Bench 4 (Vals AI)19.7%Not measuredOnly Gemini 3.8 Flash measured
Vibe Code Bench (Vals AI)78.7%47.2%Gemini 3.8 Flash
Code migration (Vals AI)36.5%25.4%Gemini 3.8 Flash
ProgramBench (Vals AI)1.0%0.0%Gemini 3.8 Flash
IOI (Vals AI)56.9%Not measuredOnly Gemini 3.8 Flash measured
Terminal-Bench 2.1 (Vals AI)81.3%67.0%Gemini 3.8 Flash
SWE-bench Multimodal (Vals AI)40.8%Not measuredOnly Gemini 3.8 Flash measured
Vibe Code Bench 1-100 (Vals AI)18.8%Not measuredOnly Gemini 3.8 Flash measured
SkillsBench (Vals AI)58.0%50.0%Gemini 3.8 Flash
CyberBench (Vals AI)43.8%Not measuredOnly Gemini 3.8 Flash measured
SciCode56.6%47.8%Gemini 3.8 Flash
Terminal-Bench Hard (Artificial Analysis run)Not measured44.7%Only Kimi K2.7 Code measured
FrontierCode41.2%30.1%Gemini 3.8 Flash
FrontierSWE19.6%Not measuredOnly Gemini 3.8 Flash measured
WeirdML84.8%54.1%Gemini 3.8 Flash
ALE-Bench1269.7886.2Gemini 3.8 Flash
LMArena Text: coding1508Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: software and IT1507Not measuredOnly Gemini 3.8 Flash measured
LMArena WebDev: React15771475Gemini 3.8 Flash
LMArena WebDev: HTML16161451Gemini 3.8 Flash
LMArena WebDev: from an image15931513Gemini 3.8 Flash
Neither model has a result on the other 12 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
AA Intelligence Index40.925.8Gemini 3.8 Flash
Epoch Capabilities Index157.4150.1Gemini 3.8 Flash
Agentic workLike-for-likePublic boards, shown as published, kept out of the index81
Agentic work benchmarks for Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
AA Agentic Index40.221.0Gemini 3.8 Flash
Design Arena: agents building web apps12381176Gemini 3.8 Flash
Design Arena: agents building full-stack apps12441175Gemini 3.8 Flash
Design Arena: agents building mobile apps12361143Gemini 3.8 Flash
Design Arena: agents building Android apps10781143Kimi K2.7 Code
Design Arena: agents building Godot games12731147Gemini 3.8 Flash
Design Arena: agents building gamesNot measured1130Only Kimi K2.7 Code measured
Design Arena: HTML slidesNot measured1201Only Kimi K2.7 Code measured
Design Arena: PowerPoint slides11781154Gemini 3.8 Flash
Design Arena: spreadsheets1052Not measuredOnly Gemini 3.8 Flash measured
LiveBench research79.1%Not measuredOnly Gemini 3.8 Flash measured
LiveBench browser64.0%Not measuredOnly Gemini 3.8 Flash measured
LiveBench long agentic30.1%Not measuredOnly Gemini 3.8 Flash measured
SREBench (Vals AI)11.1%Not measuredOnly Gemini 3.8 Flash measured
Finance Agent (Vals AI)61.4%Not measuredOnly Gemini 3.8 Flash measured
Tax Agent Bench (Vals AI)32.1%Not measuredOnly Gemini 3.8 Flash measured
Legal Research Bench (Vals AI)38.9%Not measuredOnly Gemini 3.8 Flash measured
Harvey's Legal Agent Benchmark (Vals AI)12.1%Not measuredOnly Gemini 3.8 Flash measured
Excel Modeling Benchmark (Vals AI)72.2%Not measuredOnly Gemini 3.8 Flash measured
BioMysteryBench (Vals AI)62.2%Not measuredOnly Gemini 3.8 Flash measured
Terminal-Bench-Science (Vals AI)8.6%Not measuredOnly Gemini 3.8 Flash measured
Vals Science Index35.8%Not measuredOnly Gemini 3.8 Flash measured
Time Horizon Index (Vals AI)18.8%Not measuredOnly Gemini 3.8 Flash measured
CUA-bench (Vals AI)4.2%Not measuredOnly Gemini 3.8 Flash measured
τ²-Bench TelecomNot measured90.1%Only Kimi K2.7 Code measured
APEX-Agents64.3%37.6%Gemini 3.8 Flash
Remote Labor Index5.8%Not measuredOnly Gemini 3.8 Flash measured
Vending-Bench 2$5,094$5,083Gemini 3.8 Flash
Neither model has a result on the other 11 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
Reasoning benchmarks for Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
GPQA Diamond94.4%Not measuredOnly Gemini 3.8 Flash measured
LiveBench reasoning89.3%Not measuredOnly Gemini 3.8 Flash measured
MysteryMechanism (Vals AI)36.5%Not measuredOnly Gemini 3.8 Flash measured
MMMU Pro (Vals AI)89.1%Not measuredOnly Gemini 3.8 Flash measured
GPQA Diamond (Artificial Analysis run)95.3%89.6%Gemini 3.8 Flash
Humanity's Last Exam (Artificial Analysis run)47.8%35.0%Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)95.4%87.9%Gemini 3.8 Flash
ARC-AGI98.5%Not measuredOnly Gemini 3.8 Flash measured
ARC-AGI-289.2%Not measuredOnly Gemini 3.8 Flash measured
SimpleBenchNot measured57.9%Only Kimi K2.7 Code measured
Chess puzzles58.0%14.0%Gemini 3.8 Flash
Mystery game puzzles47.0%Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: hard prompts1509Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: expert1526Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: hard prompts in English1504Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: diagrams1345Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: homework1351Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 4 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index30
Math benchmarks for Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
OTIS Mock AIME98.9%95.6%Gemini 3.8 Flash
FrontierMath, tiers 1 to 368.4%54.0%Gemini 3.8 Flash
FrontierMath, tier 422.0%12.2%Gemini 3.8 Flash
ProofBench48.0%Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: math1532Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: mathematical work1519Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index10
Knowledge benchmarks for Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
MMLU Pro90.2%Not measuredOnly Gemini 3.8 Flash measured
LegalBench (Vals AI)87.0%Not measuredOnly Gemini 3.8 Flash measured
TaxEval (Vals AI)74.5%Not measuredOnly Gemini 3.8 Flash measured
MortgageTax (Vals AI)65.3%Not measuredOnly Gemini 3.8 Flash measured
MedCode (Vals AI)48.1%Not measuredOnly Gemini 3.8 Flash measured
SimpleQA Verified69.7%36.5%Gemini 3.8 Flash
LMArena Text: science1523Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: medicine and healthcare1508Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: legal and government1497Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: business and finance1479Not measuredOnly Gemini 3.8 Flash measured
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 Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
Design Arena: websites13011264Gemini 3.8 Flash
Design Arena: UI components13181259Gemini 3.8 Flash
Design Arena: game dev13051220Gemini 3.8 Flash
Design Arena: data viz12691226Gemini 3.8 Flash
Design Arena: 3D12951241Gemini 3.8 Flash
Design Arena: all code categories13041253Gemini 3.8 Flash
Design Arena: SVG12951176Gemini 3.8 Flash
Design Arena: ASCII art12641196Gemini 3.8 Flash
LiveBench design72.7%Not measuredOnly Gemini 3.8 Flash measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
General benchmarks for Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
Vals Index56.0%Not measuredOnly Gemini 3.8 Flash measured
LiveBench data analysis54.0%Not measuredOnly Gemini 3.8 Flash measured
Public Benefits Bench (Vals AI)65.3%Not measuredOnly Gemini 3.8 Flash measured
SAGE (Vals AI)35.1%Not measuredOnly Gemini 3.8 Flash measured
MedScribe (Vals AI)84.5%Not measuredOnly Gemini 3.8 Flash measured
IFBenchNot measured63.1%Only Kimi K2.7 Code measured
Long context reasoning (AA-LCR)84.0%79.3%Gemini 3.8 Flash
DTBench95.7%Not measuredOnly Gemini 3.8 Flash measured
LMCA52.9%Not measuredOnly Gemini 3.8 Flash measured
Surface Evolver Bench76.9%48.8%Gemini 3.8 Flash
Furniture assembly31.7%Not measuredOnly Gemini 3.8 Flash measured
LMArena Text1499Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: instruction following1492Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: creative writing1488Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision1315Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: English1498Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: non-English1491Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: Chinese1553Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: German1501Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: Japanese1507Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: Korean1464Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: French1496Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: Spanish1487Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: Russian1514Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: Polish1497Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: multi-turn1503Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: longer queries1507Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: writing and language1494Not measuredOnly Gemini 3.8 Flash measured
LMArena Text: entertainment and media1480Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: English1318Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: Chinese1416Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: reading text1333Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: humor1321Not measuredOnly Gemini 3.8 Flash measured
LMArena Vision: creative writing1299Not measuredOnly Gemini 3.8 Flash measured
Neither model has a result on the other 12 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Gemini 3.8 Flash and Kimi K2.7 Code
BenchmarkGemini 3.8 FlashKimi K2.7 CodeLeader
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 3.8 Flash$0.0029Fits in one request
Kimi K2.7 Code$0.0023Fits in one request

Kimi K2.7 Code costs less

Repository review

50K input + 3K output tokens

Gemini 3.8 Flash$0.054Fits in one request
Kimi K2.7 Code$0.044Fits in one request

Kimi K2.7 Code costs less

Agent loop

200K input + 10K output tokens

Gemini 3.8 Flash$0.206Fits in one request
Kimi K2.7 Code$0.168Fits in one request

Kimi K2.7 Code 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 3.8 Flash and Kimi K2.7 Code
SpecificationGoogle Gemini 3.8 FlashMoonshotAI Kimi K2.7 Code
MakerGoogleMoonshot AI
WeightsNo public weights listedOpen weights
ReleasedSeptember 2, 2026June 12, 2026
Context window1.05M262K
Input $/Mtok$0.825$0.6712
Output $/Mtok$4.125$3.35
Coding index70.539.1
Independent measurements behind it21
Cost per solved task$2.74$0.35
SourceOpenRouter listingOpenRouter listing

Questions

Is Gemini 3.8 Flash better than Kimi K2.7 Code for coding?

Pick Gemini 3.8 Flash for coding: it scores 70.5 on the Vibe Coding Index against 39.1 for Kimi K2.7 Code. Pick Kimi K2.7 Code when output tokens cost 1.2x less, or when a solved task costs $0.35 against $2.74.

Which is cheaper, Gemini 3.8 Flash or Kimi K2.7 Code?

Gemini 3.8 Flash costs $0.825 per million input tokens and $4.125 per million output tokens; Kimi K2.7 Code costs $0.6712 and $3.35. Per solved SWE-bench Verified task, Gemini 3.8 Flash costs $2.74 and Kimi K2.7 Code $0.35.

Which has the bigger context window, Gemini 3.8 Flash or Kimi K2.7 Code?

Gemini 3.8 Flash, with 1.05M tokens against 262K.

How many benchmarks is the Gemini 3.8 Flash vs Kimi K2.7 Code comparison based on?

45 benchmarks have a result for both models as of October 21, 2026. Gemini 3.8 Flash's coding index rests on 2 independent benchmarks and Kimi K2.7 Code's on 1. Where only one model has a result, the page says so instead of naming a winner.

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