Gemini 3.8 Flash vs Kimi K2.5

Updated October 21, 2026. 81 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace Gemini 3.8 Flash Replace Kimi K2.5

Gemini 3.8 FlashGoogle

Coding: 70.5
Overall: 40.9
Output $/M: $4.125

Kimi K2.5Moonshot AI

Coding: 58.5
Overall: 23.5
Output $/M: $2.50

vs boards won of 81 shared

Decision reading

Pick Gemini 3.8 Flash for coding: it scores 70.5 on the Vibe Coding Index against 58.5 for Kimi K2.5.

Pick Kimi K2.5 when output tokens cost 1.6x less, or when a solved task costs $0.24 against $2.74.

Model AGemini 3.8 FlashGoogle70.5/1002 independent benchmarks$2.74 per solved task Model BKimi K2.5Moonshot AI58.5/1002 independent benchmarks$0.24 per solved task

814517

Shared results: 81
Gemini 3.8 Flash only: 45
Kimi K2.5 only: 17
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.

Coding
Vibe Coding Index, four public coding benchmarks
Gemini 3.8 Flash
Gemini 3.8 Flash scores 70.5 against 58.5.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 40.9 against 23.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Gemini 3.8 Flash has a AA Agentic Index result, 40.2.
Directional only
Reasoning
GPQA Diamond
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 94.4% against 84.1%.
Same board, both measured
Math
AIME
No call
Only Kimi K2.5 has a AIME result, 95.6%.
Directional only
Knowledge
MMLU Pro
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 90.2% against 85.9%.
Same board, both measured
Web design
Design Arena: websites
Gemini 3.8 Flash
Gemini 3.8 Flash leads, 1301 against 1249.
Same board, both measured
General
Vals Index
No call
Only Gemini 3.8 Flash has a Vals Index result, 56.0%.
Directional only
Long documents
Listed context window
Gemini 3.8 Flash
Gemini 3.8 Flash takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Kimi K2.5
Gemini 3.8 Flash $0.0029, Kimi K2.5 $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
Kimi K2.5
Gemini 3.8 Flash $2.74, Kimi K2.5 $0.24.
Published attempt cost and pass rate

At a glance

Coding index: Gemini 3.8 Flash 70.5 from 2 benchmarks, Kimi K2.5 58.5 from 2 benchmarks.
Widest category: Coding, 24.3 points apart on average over 14 shared benchmarks.
Widest single gap: ARC-AGI-2: Gemini 3.8 Flash 89.2% against Kimi K2.5 11.8%, 77.4 points apart on a 0 to 100 scale.
Output price: Gemini 3.8 Flash $4.125 against Kimi K2.5 $2.50 per million output tokens, 1.6x apart.
Context window: Gemini 3.8 Flash takes 1.05M tokens, Kimi K2.5 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
Category	Gemini 3.8 Flash	Kimi K2.5
Coding	14	0
Intelligence	2	0
Agentic work	8	1
Reasoning	11	0
Math	2	0
Knowledge	8	1
Web design	8	0
General	25	1
All shared boards	78	3

CodingLike-for-likeFour of these boards feed the Vibe Coding Index140

Coding benchmarks for Gemini 3.8 Flash and Kimi K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	Leader
SWE-bench Verified	Not measured	70.8%	Only Kimi K2.5 measured
SWE-bench Multilingual	Not measured	67.3%	Only Kimi K2.5 measured
LiveBench coding	82.2%	Not measured	Only Gemini 3.8 Flash measured
LMArena WebDev	1585	1437	Gemini 3.8 Flash
AA Coding Index	76.3	46.8	Gemini 3.8 Flash
LiveCodeBench (Vals AI)	89.5%	83.9%	Gemini 3.8 Flash
SWE-bench Verified (Vals AI)	80.0%	70.0%	Gemini 3.8 Flash
Terminal-Bench 4 (Vals AI)	19.7%	Not measured	Only Gemini 3.8 Flash measured
Vibe Code Bench (Vals AI)	78.7%	17.5%	Gemini 3.8 Flash
Code migration (Vals AI)	36.5%	7.0%	Gemini 3.8 Flash
ProgramBench (Vals AI)	1.0%	Not measured	Only Gemini 3.8 Flash measured
IOI (Vals AI)	56.9%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench 2.1 (Vals AI)	81.3%	42.0%	Gemini 3.8 Flash
SWE-bench Multimodal (Vals AI)	40.8%	Not measured	Only Gemini 3.8 Flash measured
Vibe Code Bench 1-100 (Vals AI)	18.8%	Not measured	Only Gemini 3.8 Flash measured
SkillsBench (Vals AI)	58.0%	Not measured	Only Gemini 3.8 Flash measured
CyberBench (Vals AI)	43.8%	Not measured	Only Gemini 3.8 Flash measured
SciCode	56.6%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench Hard (Artificial Analysis run)	Not measured	34.8%	Only Kimi K2.5 measured
Terminal-Bench	Not measured	43.2%	Only Kimi K2.5 measured
FrontierCode	41.2%	Not measured	Only Gemini 3.8 Flash measured
FrontierSWE	19.6%	Not measured	Only Gemini 3.8 Flash measured
WeirdML	84.8%	45.6%	Gemini 3.8 Flash
ALE-Bench	1269.7	821.6	Gemini 3.8 Flash
LMArena Text: coding	1508	1483	Gemini 3.8 Flash
LMArena Text: software and IT	1507	1471	Gemini 3.8 Flash
LMArena WebDev: React	1577	1429	Gemini 3.8 Flash
LMArena WebDev: HTML	1616	1426	Gemini 3.8 Flash
LMArena WebDev: from an image	1593	1439	Gemini 3.8 Flash
Neither model has a result on the other 9 boards here

IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20

Intelligence benchmarks for Gemini 3.8 Flash and Kimi K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	Leader
AA Intelligence Index	40.9	23.5	Gemini 3.8 Flash
Epoch Capabilities Index	157.4	148.1	Gemini 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 K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	Leader
AA Agentic Index	40.2	Not measured	Only Gemini 3.8 Flash measured
Design Arena: agents building web apps	1238	1136	Gemini 3.8 Flash
Design Arena: agents building full-stack apps	1244	1114	Gemini 3.8 Flash
Design Arena: agents building mobile apps	1236	1147	Gemini 3.8 Flash
Design Arena: agents building Android apps	1078	1124	Kimi K2.5
Design Arena: agents building Godot games	1273	1219	Gemini 3.8 Flash
Design Arena: PowerPoint slides	1178	Not measured	Only Gemini 3.8 Flash measured
Design Arena: spreadsheets	1052	Not measured	Only Gemini 3.8 Flash measured
LiveBench research	79.1%	Not measured	Only Gemini 3.8 Flash measured
LiveBench browser	64.0%	Not measured	Only Gemini 3.8 Flash measured
LiveBench long agentic	30.1%	Not measured	Only Gemini 3.8 Flash measured
SREBench (Vals AI)	11.1%	Not measured	Only Gemini 3.8 Flash measured
Finance Agent (Vals AI)	61.4%	35.8%	Gemini 3.8 Flash
Tax Agent Bench (Vals AI)	32.1%	Not measured	Only Gemini 3.8 Flash measured
Legal Research Bench (Vals AI)	38.9%	15.9%	Gemini 3.8 Flash
Harvey's Legal Agent Benchmark (Vals AI)	12.1%	Not measured	Only Gemini 3.8 Flash measured
Excel Modeling Benchmark (Vals AI)	72.2%	28.5%	Gemini 3.8 Flash
BioMysteryBench (Vals AI)	62.2%	Not measured	Only Gemini 3.8 Flash measured
Terminal-Bench-Science (Vals AI)	8.6%	Not measured	Only Gemini 3.8 Flash measured
Vals Science Index	35.8%	Not measured	Only Gemini 3.8 Flash measured
Time Horizon Index (Vals AI)	18.8%	Not measured	Only Gemini 3.8 Flash measured
CUA-bench (Vals AI)	4.2%	Not measured	Only Gemini 3.8 Flash measured
τ²-Bench Telecom	Not measured	95.9%	Only Kimi K2.5 measured
APEX-Agents	64.3%	Not measured	Only Gemini 3.8 Flash measured
Remote Labor Index	5.8%	Not measured	Only Gemini 3.8 Flash measured
OSWorld	Not measured	63.3%	Only Kimi K2.5 measured
Vending-Bench 2	$5,094	$1,198	Gemini 3.8 Flash
Neither model has a result on the other 12 boards here

ReasoningLike-for-likePublic boards, shown as published, kept out of the index110

Reasoning benchmarks for Gemini 3.8 Flash and Kimi K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	Leader
GPQA Diamond	94.4%	84.1%	Gemini 3.8 Flash
LiveBench reasoning	89.3%	Not measured	Only Gemini 3.8 Flash measured
MysteryMechanism (Vals AI)	36.5%	Not measured	Only Gemini 3.8 Flash measured
MMMU Pro (Vals AI)	89.1%	84.3%	Gemini 3.8 Flash
GPQA Diamond (Artificial Analysis run)	95.3%	87.9%	Gemini 3.8 Flash
Humanity's Last Exam (Artificial Analysis run)	47.8%	30.7%	Gemini 3.8 Flash
GPQA Diamond (Epoch AI run)	95.4%	Not measured	Only Gemini 3.8 Flash measured
ARC-AGI	98.5%	65.3%	Gemini 3.8 Flash
ARC-AGI-2	89.2%	11.8%	Gemini 3.8 Flash
SimpleBench	Not measured	46.8%	Only Kimi K2.5 measured
Chess puzzles	58.0%	Not measured	Only Gemini 3.8 Flash measured
Mystery game puzzles	47.0%	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: hard prompts	1509	1453	Gemini 3.8 Flash
LMArena Text: expert	1526	1466	Gemini 3.8 Flash
LMArena Text: hard prompts in English	1504	1460	Gemini 3.8 Flash
LMArena Vision: diagrams	1345	1282	Gemini 3.8 Flash
LMArena Vision: homework	1351	1297	Gemini 3.8 Flash
Neither model has a result on the other 4 boards here

MathLike-for-likePublic boards, shown as published, kept out of the index20

Math benchmarks for Gemini 3.8 Flash and Kimi K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	Leader
AIME	Not measured	95.6%	Only Kimi K2.5 measured
OTIS Mock AIME	98.9%	Not measured	Only Gemini 3.8 Flash measured
FrontierMath, tiers 1 to 3	68.4%	Not measured	Only Gemini 3.8 Flash measured
FrontierMath, tier 4	22.0%	Not measured	Only Gemini 3.8 Flash measured
ProofBench	48.0%	Not measured	Only Gemini 3.8 Flash measured
LMArena Text: math	1532	1470	Gemini 3.8 Flash
LMArena Text: mathematical work	1519	1473	Gemini 3.8 Flash
Neither model has a result on the other 6 boards here

KnowledgeLike-for-likePublic boards, shown as published, kept out of the index81

Knowledge benchmarks for Gemini 3.8 Flash and Kimi K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	Leader
MMLU Pro	90.2%	85.9%	Gemini 3.8 Flash
LegalBench (Vals AI)	87.0%	Not measured	Only Gemini 3.8 Flash measured
CaseLaw (Vals AI)	Not measured	58.7%	Only Kimi K2.5 measured
CorpFin (Vals AI)	Not measured	68.3%	Only Kimi K2.5 measured
TaxEval (Vals AI)	74.5%	74.2%	Gemini 3.8 Flash
MortgageTax (Vals AI)	65.3%	66.5%	Kimi K2.5
MedQA (Vals AI)	Not measured	94.4%	Only Kimi K2.5 measured
MedCode (Vals AI)	48.1%	39.3%	Gemini 3.8 Flash
SimpleQA Verified	69.7%	34.3%	Gemini 3.8 Flash
LMArena Text: science	1523	1459	Gemini 3.8 Flash
LMArena Text: medicine and healthcare	1508	1448	Gemini 3.8 Flash
LMArena Text: legal and government	1497	1441	Gemini 3.8 Flash
LMArena Text: business and finance	1479	1427	Gemini 3.8 Flash
Neither model has a result on the other 4 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 K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	Leader
Design Arena: websites	1301	1249	Gemini 3.8 Flash
Design Arena: UI components	1318	1236	Gemini 3.8 Flash
Design Arena: game dev	1305	1207	Gemini 3.8 Flash
Design Arena: data viz	1269	1219	Gemini 3.8 Flash
Design Arena: 3D	1295	1205	Gemini 3.8 Flash
Design Arena: all code categories	1304	1236	Gemini 3.8 Flash
Design Arena: SVG	1295	1149	Gemini 3.8 Flash
Design Arena: ASCII art	1264	1165	Gemini 3.8 Flash
LiveBench design	72.7%	Not measured	Only Gemini 3.8 Flash measured

GeneralLike-for-likePublic boards, shown as published, kept out of the index251

General benchmarks for Gemini 3.8 Flash and Kimi K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	Leader
Vals Index	56.0%	Not measured	Only Gemini 3.8 Flash measured
LiveBench data analysis	54.0%	Not measured	Only Gemini 3.8 Flash measured
Public Benefits Bench (Vals AI)	65.3%	Not measured	Only Gemini 3.8 Flash measured
SAGE (Vals AI)	35.1%	49.9%	Kimi K2.5
MedScribe (Vals AI)	84.5%	76.4%	Gemini 3.8 Flash
IFBench	Not measured	70.2%	Only Kimi K2.5 measured
Long context reasoning (AA-LCR)	84.0%	78.0%	Gemini 3.8 Flash
DTBench	95.7%	Not measured	Only Gemini 3.8 Flash measured
LMCA	52.9%	Not measured	Only Gemini 3.8 Flash measured
Fiction.LiveBench	Not measured	86.1%	Only Kimi K2.5 measured
CL-bench	Not measured	19.3%	Only Kimi K2.5 measured
Surface Evolver Bench	76.9%	Not measured	Only Gemini 3.8 Flash measured
Furniture assembly	31.7%	Not measured	Only Gemini 3.8 Flash measured
CL-bench Life	Not measured	13.2%	Only Kimi K2.5 measured
LMArena Text	1499	1445	Gemini 3.8 Flash
LMArena Text: instruction following	1492	1431	Gemini 3.8 Flash
LMArena Text: creative writing	1488	1422	Gemini 3.8 Flash
LMArena Vision	1315	1269	Gemini 3.8 Flash
LMArena Text: English	1498	1453	Gemini 3.8 Flash
LMArena Text: non-English	1491	1433	Gemini 3.8 Flash
LMArena Text: Chinese	1553	1494	Gemini 3.8 Flash
LMArena Text: German	1501	1441	Gemini 3.8 Flash
LMArena Text: Japanese	1507	1423	Gemini 3.8 Flash
LMArena Text: Korean	1464	1412	Gemini 3.8 Flash
LMArena Text: French	1496	1454	Gemini 3.8 Flash
LMArena Text: Spanish	1487	1450	Gemini 3.8 Flash
LMArena Text: Russian	1514	1435	Gemini 3.8 Flash
LMArena Text: Polish	1497	1445	Gemini 3.8 Flash
LMArena Text: multi-turn	1503	1444	Gemini 3.8 Flash
LMArena Text: longer queries	1507	1445	Gemini 3.8 Flash
LMArena Text: writing and language	1494	1425	Gemini 3.8 Flash
LMArena Text: entertainment and media	1480	1417	Gemini 3.8 Flash
LMArena Vision: English	1318	1274	Gemini 3.8 Flash
LMArena Vision: Chinese	1416	1328	Gemini 3.8 Flash
LMArena Vision: reading text	1333	1276	Gemini 3.8 Flash
LMArena Vision: humor	1321	1284	Gemini 3.8 Flash
LMArena Vision: captioning	Not measured	1257	Only Kimi K2.5 measured
LMArena Vision: recognizing things	Not measured	1285	Only Kimi K2.5 measured
LMArena Vision: creative writing	1299	1269	Gemini 3.8 Flash
Neither model has a result on the other 7 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 K2.5
Benchmark	Gemini 3.8 Flash	Kimi K2.5	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

Gemini 3.8 Flash$0.0029Fits in one request

Kimi K2.5$0.0018Fits in one request

Kimi K2.5 costs less

Repository review

50K input + 3K output tokens

Gemini 3.8 Flash$0.054Fits in one request

Kimi K2.5$0.033Fits in one request

Kimi K2.5 costs less

Agent loop

200K input + 10K output tokens

Gemini 3.8 Flash$0.206Fits in one request

Kimi K2.5$0.125Fits in one request

Kimi K2.5 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 3.8 Flash and Kimi K2.5
Specification	Google Gemini 3.8 Flash	MoonshotAI Kimi K2.5
Maker	Google	Moonshot AI
Weights	No public weights listed	Open weights
Released	September 2, 2026	January 27, 2026
Context window	1.05M	262K
Input $/Mtok	$0.825	$0.50
Output $/Mtok	$4.125	$2.50
Coding index	70.5	58.5
Independent measurements behind it	2	2
Cost per solved task	$2.74	$0.24
Source	OpenRouter listing	OpenRouter listing

Questions

Is Gemini 3.8 Flash better than Kimi K2.5 for coding?

Pick Gemini 3.8 Flash for coding: it scores 70.5 on the Vibe Coding Index against 58.5 for Kimi K2.5. Pick Kimi K2.5 when output tokens cost 1.6x less, or when a solved task costs $0.24 against $2.74.

Which is cheaper, Gemini 3.8 Flash or Kimi K2.5?

Gemini 3.8 Flash costs $0.825 per million input tokens and $4.125 per million output tokens; Kimi K2.5 costs $0.50 and $2.50. Per solved SWE-bench Verified task, Gemini 3.8 Flash costs $2.74 and Kimi K2.5 $0.24.

Which has the bigger context window, Gemini 3.8 Flash or Kimi K2.5?

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

How many benchmarks is the Gemini 3.8 Flash vs Kimi K2.5 comparison based on?

81 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 K2.5's on 2. Where only one model has a result, the page says so instead of naming a winner.

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Decision reading

Which one for your work

Coding

Intelligence

Agentic work

Reasoning

Math

Knowledge

Web design

General

Long documents

Everyday cost

Cost per solved task

At a glance

Benchmark by benchmark

What each workload costs

Chat turn

Repository review

Agent loop

Specification differences

Questions

Compare next