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Kimi K3 vs o3-mini

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.

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Decision reading

Kimi K3 has the higher public score, 72.14 versus 40.85, and the 90% score intervals do not overlap. 1 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
Moonshot AI logo

Moonshot AI

72.14/100

Supported · Public rank #15

90% interval 68.8–75.5

Model B
OpenAI logo

OpenAI

40.85/100

Supported · Public rank #126

90% interval 28.9–52.9

Shared results
1
Kimi K3 only
47
o3-mini only
4
Like-for-like categories
1 / 8
Supported: Kimi K3 and o3-miniHow 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

    o3-mini

    o3-mini 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

    o3-mini

    o3-mini 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

    O3-mini is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited
  • Agentic work

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

    Not enough matched evidence

    O3-mini is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited
  • Cache-heavy agent loop cost

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

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

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.

61.4Kimi K3—o3-mini

Not comparable · BenchAlign v5.8

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

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.

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.

Knowledge

Like-for-like
Kimi K3
67.9
Supported · #18/171
o3-mini
35.6
Supported · #115/171
Basis
BenchAlign v5.8 lane · 6 vs 2 public rows
Reading
Kimi K3 leads

Agentic

Not comparable
Kimi K3
68.1
Supported · #8/119
o3-mini
Not ranked
Basis
BenchAlign v5.8 lane · 12 vs 0 public rows
Reading
Not comparable

Coding

Not comparable
Kimi K3
61.4
Supported · #18/144
o3-mini
Not ranked
Basis
BenchAlign v5.8 lane · 14 vs 1 public rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K3
65.8
#18/27
o3-mini
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K3
89.4
#1/49
o3-mini
Not ranked
Basis
Provisional lane · 3 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K3
Not ranked
o3-mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Kimi K3
Not ranked
o3-mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Kimi K3
Not ranked
o3-mini
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

Kimi K3
$0.0105
Fits in one request
o3-mini
$0.0033
Fits in one request

o3-mini has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Kimi K3
$0.195
Fits in one request
o3-mini
$0.0682
Fits in one request

o3-mini has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Kimi K3
$0.27
Fits in one request
o3-mini
$0.286
Does not fit in one request
Cached input priced at the published list-input rate

o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.

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.

Kimi K3

1.05M

o3-mini

200K

API model ID

Kimi K3

Not sourced

o3-mini

Not sourced

Cached-input rate

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

Kimi K3

$0.3 per 1M cached input tokens

o3-mini

Not published

Documented inputs

Kimi K3

Not sourced

o3-mini

Not sourced

Documented outputs

Kimi K3

Not sourced

o3-mini

Not sourced

Provider availability

Kimi K3

Not sourced

o3-mini

Not sourced

Reasoning profile

Kimi K3

Reasoning

o3-mini

Reasoning

Weight access

Kimi K3

Pending

o3-mini

Proprietary

License

Kimi K3

Pending

o3-mini

Proprietary

Release date

Kimi K3

2026-07-16

o3-mini

2025-01-31

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, 72.14 versus 40.85, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.195 vs $0.0682. Cache-heavy agent loop: $0.27 vs $0.286.
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, Kimi K3 or o3-mini?

Kimi K3 has the higher public score, 72.14 versus 40.85, 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, Kimi K3 or o3-mini?

O3-mini is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Kimi K3 or o3-mini?

O3-mini is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Kimi K3 or o3-mini?

For the stated presets, chat costs $0.0105 on Kimi K3 and $0.0033 on o3-mini; repository review costs $0.195 and $0.0682; the cache-heavy agent loop costs $0.27 and $0.286. o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Kimi K3 or o3-mini?

Kimi K3 has the larger documented context window: 1.05M, compared with 200K.

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

  • Terminal-Bench 2.1

    Kimi K388.3%
    Source
    o3-mini—

    Not directly comparable

  • BrowseComp

    Kimi K391.2%
    Source
    o3-mini—

    Not directly comparable

  • DeepSearchQA

    Kimi K395.0%
    Source
    o3-mini—

    Not directly comparable

  • Toolathlon-Verified

    Kimi K373.2%
    Source
    o3-mini—

    Not directly comparable

  • MCP Atlas

    Kimi K384.2%
    Source
    o3-mini—

    Not directly comparable

  • AutomationBench

    Kimi K330.8%
    Source
    o3-mini—

    Not directly comparable

  • JobBench

    Kimi K352.9%
    Source
    o3-mini—

    Not directly comparable

  • APEX-Agents

    Kimi K337.6%
    Source
    o3-mini—

    Not directly comparable

  • SpreadsheetBench 2

    Kimi K334.8%
    Source
    o3-mini—

    Not directly comparable

  • DECK-Bench

    Kimi K373.5%
    Source
    o3-mini—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Kimi K380.9%
    Source
    o3-mini—

    Not directly comparable

  • ApprenticeBench

    Kimi K318%
    Source
    o3-mini—

    Not directly comparable

Coding

  • DeepSWE

    Kimi K367.5%
    Source
    o3-mini—

    Not directly comparable

  • CursorBench 3.2

    Kimi K360.8%
    Source
    o3-mini—

    Not directly comparable

  • FrontierSWE

    Kimi K381.2%
    Source
    o3-mini—

    Not directly comparable

  • ProgramBench

    Kimi K377.8%
    Source
    o3-mini—

    Not directly comparable

  • Kimi Code Bench v2

    Kimi K372.9%
    Source
    o3-mini—

    Not directly comparable

  • sweMarathon

    Kimi K342%
    Source
    o3-mini—

    Not directly comparable

  • PostTrain Bench

    Kimi K336.6%
    Source
    o3-mini—

    Not directly comparable

  • MLS-Bench Lite

    Kimi K348.3%
    Source
    o3-mini—

    Not directly comparable

  • VulcanBench v3

    Kimi K373.7%
    Source
    o3-mini—

    Not directly comparable

  • OpenHarmony Bench

    Kimi K357.3%
    Source
    o3-mini—

    Not directly comparable

  • FrontierSWE v2

    Kimi K325.9%
    Source
    o3-mini—

    Not directly comparable

  • LiveCodeBench (Vals)

    Kimi K387.2%
    Source
    o3-mini—

    Not directly comparable

  • SWE-bench (Vals)

    Kimi K393.4%
    Source
    o3-mini—

    Not directly comparable

  • PostTrainBench v1.1

    Kimi K332.0%
    Source
    o3-mini—

    Not directly comparable

  • SWE-bench Verified

    Kimi K3—
    o3-mini49.3%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-1

    Kimi K394.50%
    Source
    o3-mini—

    Not directly comparable

  • ARC-AGI-2

    Kimi K360.4%
    Source
    o3-mini—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Kimi K363.3%
    Source
    o3-mini—

    Not directly comparable

  • MMMU-Pro

    Kimi K381.6%
    Source
    o3-mini—

    Not directly comparable

  • MMMU-Pro w/ Python

    Kimi K383.4%
    Source
    o3-mini—

    Not directly comparable

  • CharXiv w/o tools

    Kimi K384.8%
    Source
    o3-mini—

    Not directly comparable

  • CharXiv

    Kimi K391.3%
    Source
    o3-mini—

    Not directly comparable

  • MathVision

    Kimi K394.3%
    Source
    o3-mini—

    Not directly comparable

  • MathVision w/ Python

    Kimi K397.8%
    Source
    o3-mini—

    Not directly comparable

  • BabyVision w/ Python

    Kimi K385.7%
    Source
    o3-mini—

    Not directly comparable

  • ZeroBench

    Kimi K323.0%
    Source
    o3-mini—

    Not directly comparable

  • ZeroBench w/ Python

    Kimi K341.0%
    Source
    o3-mini—

    Not directly comparable

  • WorldVQA ForceAnswer

    Kimi K351.0%
    Source
    o3-mini—

    Not directly comparable

  • OmniDocBench

    Kimi K391.1%
    Source
    o3-mini—

    Not directly comparable

  • PerceptionBench

    Kimi K358.5%
    Source
    o3-mini—

    Not directly comparable

Knowledge

  • GPQA

    Kimi K393.5%
    Source
    o3-mini77.2%
    Source

    Kimi K3 leads this result

  • GPQA-D

    Kimi K393.5%
    Source
    o3-mini—

    Not directly comparable

  • HLE

    Kimi K356%
    Source
    o3-mini—

    Not directly comparable

  • HLE w/o tools

    Kimi K343.5%
    Source
    o3-mini—

    Not directly comparable

  • GPQA Diamond (Vals)

    Kimi K392.9%
    Source
    o3-mini—

    Not directly comparable

  • MMLU-Pro (Vals)

    Kimi K388.0%
    Source
    o3-mini—

    Not directly comparable

  • MMLU

    Kimi K3—
    o3-mini86.9%
    Source

    Not directly comparable

Instruction following

  • Gray Swan IPI (15 attempts)

    Kimi K352.7%
    Source
    o3-mini—

    Not directly comparable

  • IFEval

    Kimi K3—
    o3-mini93.9%
    Source

    Not directly comparable

Math

  • AIME 2024

    Kimi K3—
    o3-mini87.3%
    Source

    Not directly comparable

52 public results · 1 shared

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Last updated October 2, 2026