Skip to main content
BenchLM
Data

Gemini 3.1 Flash-Lite vs Kimi K3

Updated October 2, 2026. Rank says Kimi K3 is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

Share or export
Share on XLinkedInSocial cardCSVAPI/MCP

Decision reading

Kimi K3 has the higher public point estimate, 72.14 versus 49.51. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Google logo

Google

49.51/100

Estimated · Public rank #92

Conditional range 39.8–59.2

Model B
Moonshot AI logo

Moonshot AI

72.14/100

Supported · Public rank #15

90% interval 68.8–75.5

Shared results
6
Gemini 3.1 Flash-Lite only
4
Kimi K3 only
42
Like-for-like categories
0 / 8
Estimated: Gemini 3.1 Flash-Lite · Supported: Kimi K3. Conditional ranges do not establish rank confidence.How the comparison works

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

    Gemini 3.1 Flash-Lite is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited
  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Gemini 3.1 Flash-Lite is scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    Confidence: limited

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

26.3Gemini 3.1 Flash-Lite61.4Kimi K3

Directional only · BenchAlign v5.8

Kimi K3 has the higher coding point estimate. Conditional score ranges do not establish rank confidence.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Same basis rules as the category table below

What is actually comparable

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

3 categories rest on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Directional only
Gemini 3.1 Flash-Lite
27.8
Estimated · #88/119
Kimi K3
68.1
Supported · #8/119
Basis
BenchAlign v5.8 lane · 2 vs 12 public rows
Reading
Directional only

Coding

Directional only
Gemini 3.1 Flash-Lite
26.3
Estimated · #103/144
Kimi K3
61.4
Supported · #18/144
Basis
BenchAlign v5.8 lane · 3 vs 14 public rows
Reading
Directional only

Knowledge

Directional only
Gemini 3.1 Flash-Lite
47.0
Estimated · #79/171
Kimi K3
67.9
Supported · #18/171
Basis
BenchAlign v5.8 lane · 2 vs 6 public rows
Reading
Directional only

Reasoning

Not comparable
Gemini 3.1 Flash-Lite
Not ranked
Kimi K3
65.8
#18/27
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 3.1 Flash-Lite
38.5
Unranked · 1 rankable row
Kimi K3
89.4
#1/49
Basis
Provisional lane · 1 vs 3 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 3.1 Flash-Lite
Not ranked
Kimi K3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 3.1 Flash-Lite
Not ranked
Kimi K3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 3.1 Flash-Lite
Not ranked
Kimi K3
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign v5.8) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Supported evidence per lane · bars run 0–100Methodology

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.

Cached input falls back to the list input rate only where a cached rate is unpublished

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 point estimate, 72.14 versus 49.51. Their conditional score ranges do not overlap. These ranges do not establish rank confidence.
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.

Questions

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

Kimi K3 has the higher public point estimate, 72.14 versus 49.51. Their conditional score ranges do not overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

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

Kimi K3 scores higher for coding on the public lane, 61.4 to 26.3. Gemini 3.1 Flash-Lite is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

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

Kimi K3 scores higher for agentic tasks on the public lane, 68.1 to 27.8. Gemini 3.1 Flash-Lite is scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

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.

Benchmark evidence

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

Browse raw public benchmark evidence52 rows

Agentic

  • Gert Labs

    Gemini 3.1 Flash-Lite38.46%
    Source
    Kimi K3—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Gemini 3.1 Flash-Lite34.1%
    Source
    Kimi K380.9%
    Source

    Kimi K3 leads this result

  • Terminal-Bench 2.1

    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

  • ApprenticeBench

    Gemini 3.1 Flash-Lite—
    Kimi K318%
    Source

    Not directly comparable

Coding

  • Vibe Code Bench

    Gemini 3.1 Flash-Lite0.00%
    Source
    Kimi K3—

    Not directly comparable

  • LiveCodeBench (Vals)

    Gemini 3.1 Flash-Lite80.1%
    Source
    Kimi K387.2%
    Source

    Kimi K3 leads this result

  • SWE-bench (Vals)

    Gemini 3.1 Flash-Lite62.8%
    Source
    Kimi K393.4%
    Source

    Kimi K3 leads this result

  • DeepSWE

    Gemini 3.1 Flash-Lite—
    Kimi K367.5%
    Source

    Not directly comparable

  • CursorBench 3.2

    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 K373.7%
    Source

    Not directly comparable

  • OpenHarmony Bench

    Gemini 3.1 Flash-Lite—
    Kimi K357.3%
    Source

    Not directly comparable

  • FrontierSWE v2

    Gemini 3.1 Flash-Lite—
    Kimi K325.9%
    Source

    Not directly comparable

  • PostTrainBench v1.1

    Gemini 3.1 Flash-Lite—
    Kimi K332.0%
    Source

    Not directly comparable

Reasoning

  • JevBench 1.4

    Gemini 3.1 Flash-Lite14.26
    Source
    Kimi K3—

    Not directly comparable

  • JevBench 1.5

    Gemini 3.1 Flash-Lite19.58
    Source
    Kimi K3—

    Not directly comparable

  • ARC-AGI-1

    Gemini 3.1 Flash-Lite—
    Kimi K394.50%
    Source

    Not directly comparable

  • ARC-AGI-2

    Gemini 3.1 Flash-Lite—
    Kimi K360.4%
    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

Knowledge

  • GPQA Diamond (Vals)

    Gemini 3.1 Flash-Lite81.1%
    Source
    Kimi K392.9%
    Source

    Kimi K3 leads this result

  • MMLU-Pro (Vals)

    Gemini 3.1 Flash-Lite86.2%
    Source
    Kimi K388.0%
    Source

    Kimi K3 leads this result

  • 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

Instruction following

  • Gray Swan IPI (15 attempts)

    Gemini 3.1 Flash-Lite—
    Kimi K352.7%
    Source

    Not directly comparable

52 public results · 6 shared

Watch Gemini 3.1 Flash-Lite vs Kimi K3

One weekly email when material rank, price, or benchmark evidence changes make this matchup worth revisiting.

Read a sample issue

Join 2,000+ readers.

Last updated October 2, 2026