Model A

Google

65.3/100

Supported · Public rank #36

90% interval 53.9–76.8

Gemini 3.5 Flash-Lite vs Kimi K3

Updated August 19, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B

Kimi K3

Moonshot AI

80.5/100

Supported · Public rank #5

90% interval 77.7–83.4

Decision reading

Kimi K3 has the higher public score, 80.53 versus 65.32, and the 90% score intervals do not overlap.

1 results are shared. Category rows based on different benchmark sets are marked directional and do not name a winner.

Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

Long documents
Prompts that approach the documented context limit
Kimi K3
Kimi K3 has the larger documented context window.
Confidence: documented
Chat turn cost
1K fresh input + 500 output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Cache-heavy agent loop cost
200K cached + 20K fresh input + 10K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates

Show secondary and unsupported calls

Repository review cost
50K fresh input + 3K output tokens
Gemini 3.5 Flash-Lite
Gemini 3.5 Flash-Lite has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.
Confidence: listed-rates
Coding work
Code generation, repair, and software-engineering tasks
Not enough matched evidence
No shared weighted benchmark basis supports a winner.
Confidence: limited
Agentic work
Tool use, computer use, and multi-step task completion
Not enough matched evidence
The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.
Confidence: limited

What is actually comparable

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

4 Gemini 3.5 Flash-Lite only

1 Shared

39 Kimi K3 only

Shared results: 1
Gemini 3.5 Flash-Lite only: 4
Kimi K3 only: 39
Like-for-like categories: 0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row states whether both averages use the same weighted benchmark set. Directional and not-comparable rows remain visible, but they never receive a winner in this template.

Agentic

Directional only

Gemini 3.5 Flash-Lite: 63.4
Kimi K3: 89.5
Weighted basis: 2 vs 2 rows
Reading: Directional only

Coding

Not comparable

Gemini 3.5 Flash-Lite: 54.2
Kimi K3: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Reasoning

Not comparable

Gemini 3.5 Flash-Lite: 72.2
Kimi K3: Not measured
Weighted basis: 1 vs 0 rows
Reading: Not comparable

Knowledge

Not comparable

Gemini 3.5 Flash-Lite: Not measured
Kimi K3: 61.0
Weighted basis: 0 vs 2 rows
Reading: Not comparable

Math

Not comparable

Gemini 3.5 Flash-Lite: Not measured
Kimi K3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multilingual

Not comparable

Gemini 3.5 Flash-Lite: Not measured
Kimi K3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Multimodal

Not comparable

Gemini 3.5 Flash-Lite: Not measured
Kimi K3: 78.5
Weighted basis: 0 vs 3 rows
Reading: Not comparable

Instruction following

Not comparable

Gemini 3.5 Flash-Lite: Not measured
Kimi K3: Not measured
Weighted basis: 0 vs 0 rows
Reading: Not comparable

Category averages with the server-provided evidence basis for Gemini 3.5 Flash-Lite and Kimi K3
Category	Gemini 3.5 Flash-Lite	Kimi K3	Weighted basis	Reading
Agentic	63.4	89.5	Directional only2 vs 2 rows	Directional only
Coding	54.2	Not measured	Not comparable1 vs 0 rows	Not comparable
Reasoning	72.2	Not measured	Not comparable1 vs 0 rows	Not comparable
Knowledge	Not measured	61.0	Not comparable0 vs 2 rows	Not comparable
Math	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multilingual	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable
Multimodal	Not measured	78.5	Not comparable0 vs 3 rows	Not comparable
Instruction following	Not measured	Not measured	Not comparable0 vs 0 rows	Not comparable

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

Terminal-Bench 2.0
Agentic
Gemini 3.5 Flash-Lite: 54%Kimi K3: 88.3%Normalized gap 34.3Gemini 3.5 Flash-Lite source Kimi K3 source

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Gemini 3.5 Flash-Lite: $0.00155; Fits in one request
Kimi K3: $0.0105; Fits in one request

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.5 Flash-Lite: $0.0225; Fits in one request
Kimi K3: $0.195; Fits in one request

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemini 3.5 Flash-Lite: $0.037; Fits in one request
Kimi K3: $0.27; Fits in one request

Gemini 3.5 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

SpecificationGemini 3.5 Flash-LiteKimi K3

Context window

Maximum documented context; output-token limits may be lower.

Gemini 3.5 Flash-Lite

Google latest Gemini model guide

Kimi K3

1.05M

API model ID

Gemini 3.5 Flash-Lite

gemini-3.5-flash-lite

Google Gemini 3.5 Flash-Lite model documentation

Kimi K3

Not sourced

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemini 3.5 Flash-Lite

$0.03 per 1M cached input tokens

Google Gemini API pricing

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

Gemini 3.5 Flash-Lite

text, image, video, audio, pdf

Google Gemini 3.5 Flash-Lite model documentation

Kimi K3

Not sourced

Documented outputs

Gemini 3.5 Flash-Lite

text

Google Gemini 3.5 Flash-Lite model documentation

Kimi K3

Not sourced

Provider availability

Gemini 3.5 Flash-Lite

Generally Available · Gemini API, Google AI Studio

Google latest Gemini model guide

Kimi K3

Not sourced

Reasoning profile

Gemini 3.5 Flash-Lite

Reasoning

Kimi K3

Reasoning

Weight access

Gemini 3.5 Flash-Lite

Proprietary

Kimi K3

Pending

License

Gemini 3.5 Flash-Lite

Proprietary

Kimi K3

Pending

Release date

Gemini 3.5 Flash-Lite

2026-07-21

Kimi K3

2026-07-16

If you already use one of these models

Deployment change: The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal: Kimi K3 has the higher public score, 80.53 versus 65.32, and the 90% score intervals do not overlap.
Workload cost: Repository review: $0.0225 vs $0.195. Cache-heavy agent loop: $0.037 vs $0.27.
Context tradeoff: Kimi K3 has the larger documented window (1.05M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence44 rows

Agentic

Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54%
Source
Kimi K388.3%
Source
Kimi K3 leads this result
OSWorld-Verified
Gemini 3.5 Flash-Lite74%
Source
Kimi K3—
Not directly comparable
BrowseComp
Gemini 3.5 Flash-Lite—
Kimi K391.2%
Source
Not directly comparable
DeepSearchQA
Gemini 3.5 Flash-Lite—
Kimi K395.0%
Source
Not directly comparable
Toolathlon-Verified
Gemini 3.5 Flash-Lite—
Kimi K373.2%
Source
Not directly comparable
MCP Atlas
Gemini 3.5 Flash-Lite—
Kimi K384.2%
Source
Not directly comparable
AutomationBench
Gemini 3.5 Flash-Lite—
Kimi K330.8%
Source
Not directly comparable
JobBench
Gemini 3.5 Flash-Lite—
Kimi K352.9%
Source
Not directly comparable
APEX-Agents
Gemini 3.5 Flash-Lite—
Kimi K337.6%
Source
Not directly comparable
SpreadsheetBench 2
Gemini 3.5 Flash-Lite—
Kimi K334.8%
Source
Not directly comparable
DECK-Bench
Gemini 3.5 Flash-Lite—
Kimi K373.5%
Source
Not directly comparable

Coding

Terminal-Bench 2.0
Gemini 3.5 Flash-Lite54.0%
Source
Kimi K3—
Not directly comparable
SWE-bench Pro
Gemini 3.5 Flash-Lite54.2%
Source
Kimi K3—
Not directly comparable
deepSwe
Gemini 3.5 Flash-Lite—
Kimi K367.5%
Source
Not directly comparable
cursorBench32
Gemini 3.5 Flash-Lite—
Kimi K360.8%
Source
Not directly comparable
FrontierSWE
Gemini 3.5 Flash-Lite—
Kimi K381.2%
Source
Not directly comparable
ProgramBench
Gemini 3.5 Flash-Lite—
Kimi K377.8%
Source
Not directly comparable
Kimi Code Bench v2
Gemini 3.5 Flash-Lite—
Kimi K372.9%
Source
Not directly comparable
sweMarathon
Gemini 3.5 Flash-Lite—
Kimi K342%
Source
Not directly comparable
PostTrain Bench
Gemini 3.5 Flash-Lite—
Kimi K336.6%
Source
Not directly comparable
MLS-Bench Lite
Gemini 3.5 Flash-Lite—
Kimi K348.3%
Source
Not directly comparable
VulcanBench v3
Gemini 3.5 Flash-Lite—
Kimi K373.9%
Source
Not directly comparable
APEX-SWE
Gemini 3.5 Flash-Lite—
Kimi K348.0%
Source
Not directly comparable
EEBench
Gemini 3.5 Flash-Lite—
Kimi K338.3%
Source
Not directly comparable
InferenceEval
Gemini 3.5 Flash-Lite—
Kimi K339.8%
Source
Not directly comparable
KernelBench Internal
Gemini 3.5 Flash-Lite—
Kimi K368.5%
Source
Not directly comparable

Reasoning

MRCRv2
Gemini 3.5 Flash-Lite72.2%
Source
Kimi K3—
Not directly comparable

Knowledge

GPQA
Gemini 3.5 Flash-Lite—
Kimi K393.5%
Source
Not directly comparable
GPQA-D
Gemini 3.5 Flash-Lite—
Kimi K393.5%
Source
Not directly comparable
HLE
Gemini 3.5 Flash-Lite—
Kimi K356%
Source
Not directly comparable
HLE w/o tools
Gemini 3.5 Flash-Lite—
Kimi K343.5%
Source
Not directly comparable

Multimodal

OfficeQA Pro
Gemini 3.5 Flash-Lite—
Kimi K363.3%
Source
Not directly comparable
MMMU-Pro
Gemini 3.5 Flash-Lite—
Kimi K381.6%
Source
Not directly comparable
MMMU-Pro w/ Python
Gemini 3.5 Flash-Lite—
Kimi K383.4%
Source
Not directly comparable
CharXiv w/o tools
Gemini 3.5 Flash-Lite—
Kimi K384.8%
Source
Not directly comparable
CharXiv
Gemini 3.5 Flash-Lite—
Kimi K391.3%
Source
Not directly comparable
MathVision
Gemini 3.5 Flash-Lite—
Kimi K394.3%
Source
Not directly comparable
MathVision w/ Python
Gemini 3.5 Flash-Lite—
Kimi K397.8%
Source
Not directly comparable
BabyVision w/ Python
Gemini 3.5 Flash-Lite—
Kimi K385.7%
Source
Not directly comparable
ZeroBench
Gemini 3.5 Flash-Lite—
Kimi K323.0%
Source
Not directly comparable
ZeroBench w/ Python
Gemini 3.5 Flash-Lite—
Kimi K341.0%
Source
Not directly comparable
WorldVQA ForceAnswer
Gemini 3.5 Flash-Lite—
Kimi K351.0%
Source
Not directly comparable
OmniDocBench
Gemini 3.5 Flash-Lite—
Kimi K391.1%
Source
Not directly comparable
PerceptionBench
Gemini 3.5 Flash-Lite—
Kimi K358.5%
Source
Not directly comparable

Frequently asked questions

Which is better, Gemini 3.5 Flash-Lite or Kimi K3?

Kimi K3 has the higher public score, 80.53 versus 65.32, and the 90% score intervals do not overlap. That is the clearest overall quality signal in the current public data.

Which is better for coding, Gemini 3.5 Flash-Lite or Kimi K3?

The published evidence does not provide a shared weighted coding basis for both models, so BenchLM does not name a coding winner.

Which is better for agentic tasks, Gemini 3.5 Flash-Lite or Kimi K3?

The current agentic tasks averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Gemini 3.5 Flash-Lite or Kimi K3?

For the stated presets, chat costs $0.00155 on Gemini 3.5 Flash-Lite and $0.0105 on Kimi K3; repository review costs $0.0225 and $0.195; the cache-heavy agent loop costs $0.037 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.5 Flash-Lite or Kimi K3?

Kimi K3 has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 19, 2026

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Gemini 3.5 Flash-Lite vs Kimi K3

Which one for your work

Long documents

Chat turn cost

Cache-heavy agent loop cost

Repository review cost

Coding work

Agentic work

What is actually comparable

Category results, on a stated basis

Agentic

Coding

Reasoning

Knowledge

Math

Multilingual

Multimodal

Instruction following

Shape of the matched evidence

What each workload costs

Chat turn

Repository review

Cache-heavy agent loop

Specification differences

Context window

API model ID

Cached-input rate

Documented inputs

Documented outputs

Provider availability

Reasoning profile

Weight access

License

Release date

Benchmark evidence

Agentic

Coding

Reasoning

Knowledge

Multimodal

Frequently asked questions

Which is better, Gemini 3.5 Flash-Lite or Kimi K3?

Which is better for coding, Gemini 3.5 Flash-Lite or Kimi K3?

Which is better for agentic tasks, Gemini 3.5 Flash-Lite or Kimi K3?

Which costs less, Gemini 3.5 Flash-Lite or Kimi K3?

Which has the larger context window, Gemini 3.5 Flash-Lite or Kimi K3?

Related comparisons

Watch Gemini 3.5 Flash-Lite vs Kimi K3