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Model A
Gemini 3.1 Flash-Lite

Google

50.5/100

Supported · Public rank #127

90% interval 24.4–76.6

Gemini 3.1 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 50.53, 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 3.1 Flash-Lite

    Gemini 3.1 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 3.1 Flash-Lite

    Gemini 3.1 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 3.1 Flash-Lite

    Gemini 3.1 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

    No shared weighted benchmark basis supports a winner.

    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.

Shared results
1
Gemini 3.1 Flash-Lite only
2
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.

Multimodal

Directional only
Gemini 3.1 Flash-Lite
73.2
Kimi K3
78.5
Weighted basis
1 vs 3 rows
Reading
Directional only

Agentic

Not comparable
Gemini 3.1 Flash-Lite
Not measured
Kimi K3
89.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Gemini 3.1 Flash-Lite
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 3.1 Flash-Lite
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Gemini 3.1 Flash-Lite
Not measured
Kimi K3
61.0
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Flash-Lite
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Flash-Lite
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Flash-Lite
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
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.

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 3.1 Flash-Lite
$0.001
Fits in one request
Kimi K3
$0.0105
Fits in one request

Gemini 3.1 Flash-Lite has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 3.1 Flash-Lite
$0.017
Fits in one request
Kimi K3
$0.195
Fits in one request

Gemini 3.1 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 3.1 Flash-Lite
$0.025
Fits in one request
Kimi K3
$0.27
Fits in one request

Gemini 3.1 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.

Context window

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

Gemini 3.1 Flash-Lite

Kimi K3

1.05M

Cached-input rate

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

Gemini 3.1 Flash-Lite

$0.025 per 1M cached input tokens

Google Gemini API pricing

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

Gemini 3.1 Flash-Lite

Not sourced

Kimi K3

Not sourced

Documented outputs

Gemini 3.1 Flash-Lite

Not sourced

Kimi K3

Not sourced

Provider availability

Gemini 3.1 Flash-Lite

Not sourced

Kimi K3

Not sourced

Reasoning profile

Gemini 3.1 Flash-Lite

Non-Reasoning

Kimi K3

Reasoning

Weight access

Gemini 3.1 Flash-Lite

Proprietary

Kimi K3

Pending

License

Gemini 3.1 Flash-Lite

Proprietary

Kimi K3

Pending

Release date

Gemini 3.1 Flash-Lite

2026-03-03

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 50.53, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.017 vs $0.195. Cache-heavy agent loop: $0.025 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 evidence42 rows

Agentic

  • Gert Labs

    Gemini 3.1 Flash-Lite38.46%
    Source
    Kimi K3

    Not directly comparable

  • Terminal-Bench 2.0

    Gemini 3.1 Flash-Lite
    Kimi K388.3%
    Source

    Not directly comparable

  • BrowseComp

    Gemini 3.1 Flash-Lite
    Kimi K391.2%
    Source

    Not directly comparable

  • DeepSearchQA

    Gemini 3.1 Flash-Lite
    Kimi K395.0%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Gemini 3.1 Flash-Lite
    Kimi K373.2%
    Source

    Not directly comparable

  • MCP Atlas

    Gemini 3.1 Flash-Lite
    Kimi K384.2%
    Source

    Not directly comparable

  • AutomationBench

    Gemini 3.1 Flash-Lite
    Kimi K330.8%
    Source

    Not directly comparable

  • JobBench

    Gemini 3.1 Flash-Lite
    Kimi K352.9%
    Source

    Not directly comparable

  • APEX-Agents

    Gemini 3.1 Flash-Lite
    Kimi K337.6%
    Source

    Not directly comparable

  • SpreadsheetBench 2

    Gemini 3.1 Flash-Lite
    Kimi K334.8%
    Source

    Not directly comparable

  • DECK-Bench

    Gemini 3.1 Flash-Lite
    Kimi K373.5%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Gemini 3.1 Flash-Lite0.00%
    Source
    Kimi K3

    Not directly comparable

  • deepSwe

    Gemini 3.1 Flash-Lite
    Kimi K367.5%
    Source

    Not directly comparable

  • cursorBench32

    Gemini 3.1 Flash-Lite
    Kimi K360.8%
    Source

    Not directly comparable

  • FrontierSWE

    Gemini 3.1 Flash-Lite
    Kimi K381.2%
    Source

    Not directly comparable

  • ProgramBench

    Gemini 3.1 Flash-Lite
    Kimi K377.8%
    Source

    Not directly comparable

  • Kimi Code Bench v2

    Gemini 3.1 Flash-Lite
    Kimi K372.9%
    Source

    Not directly comparable

  • sweMarathon

    Gemini 3.1 Flash-Lite
    Kimi K342%
    Source

    Not directly comparable

  • PostTrain Bench

    Gemini 3.1 Flash-Lite
    Kimi K336.6%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Gemini 3.1 Flash-Lite
    Kimi K348.3%
    Source

    Not directly comparable

  • VulcanBench v3

    Gemini 3.1 Flash-Lite
    Kimi K373.9%
    Source

    Not directly comparable

  • APEX-SWE

    Gemini 3.1 Flash-Lite
    Kimi K348.0%
    Source

    Not directly comparable

  • EEBench

    Gemini 3.1 Flash-Lite
    Kimi K338.3%
    Source

    Not directly comparable

  • InferenceEval

    Gemini 3.1 Flash-Lite
    Kimi K339.8%
    Source

    Not directly comparable

  • KernelBench Internal

    Gemini 3.1 Flash-Lite
    Kimi K368.5%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Gemini 3.1 Flash-Lite
    Kimi K393.5%
    Source

    Not directly comparable

  • GPQA-D

    Gemini 3.1 Flash-Lite
    Kimi K393.5%
    Source

    Not directly comparable

  • HLE

    Gemini 3.1 Flash-Lite
    Kimi K356%
    Source

    Not directly comparable

  • HLE w/o tools

    Gemini 3.1 Flash-Lite
    Kimi K343.5%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Gemini 3.1 Flash-Lite73.2%
    Source
    Kimi K391.3%
    Source

    Kimi K3 leads this result

  • OfficeQA Pro

    Gemini 3.1 Flash-Lite
    Kimi K363.3%
    Source

    Not directly comparable

  • MMMU-Pro

    Gemini 3.1 Flash-Lite
    Kimi K381.6%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Gemini 3.1 Flash-Lite
    Kimi K383.4%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Gemini 3.1 Flash-Lite
    Kimi K384.8%
    Source

    Not directly comparable

  • MathVision

    Gemini 3.1 Flash-Lite
    Kimi K394.3%
    Source

    Not directly comparable

  • MathVision w/ Python

    Gemini 3.1 Flash-Lite
    Kimi K397.8%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Gemini 3.1 Flash-Lite
    Kimi K385.7%
    Source

    Not directly comparable

  • ZeroBench

    Gemini 3.1 Flash-Lite
    Kimi K323.0%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Gemini 3.1 Flash-Lite
    Kimi K341.0%
    Source

    Not directly comparable

  • WorldVQA ForceAnswer

    Gemini 3.1 Flash-Lite
    Kimi K351.0%
    Source

    Not directly comparable

  • OmniDocBench

    Gemini 3.1 Flash-Lite
    Kimi K391.1%
    Source

    Not directly comparable

  • PerceptionBench

    Gemini 3.1 Flash-Lite
    Kimi K358.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 3.1 Flash-Lite or Kimi K3?

Kimi K3 has the higher public score, 80.53 versus 50.53, 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 3.1 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 3.1 Flash-Lite or Kimi K3?

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

Which costs less, Gemini 3.1 Flash-Lite or Kimi K3?

For the stated presets, chat costs $0.001 on Gemini 3.1 Flash-Lite and $0.0105 on Kimi K3; repository review costs $0.017 and $0.195; the cache-heavy agent loop costs $0.025 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, Gemini 3.1 Flash-Lite or Kimi K3?

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

Related comparisons

Last updated August 19, 2026

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