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BenchLM
Model A
Kimi K3

Moonshot AI

80.5/100

Supported · Public rank #5

90% interval 77.7–83.4

Kimi K3 vs A.X series

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

Model B
A.X series

SK Telecom

—

Evidence status unavailable

90% interval unavailable

Decision reading

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. Use the documented cost, context, and runtime rows instead.

0 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

Show secondary and unsupported calls

  • 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

  • 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. A.X series does not fit this workload in one request. A.X series has no comparable published API token rate.

    Confidence: listed-rates

  • Repository review cost

    50K fresh input + 3K output tokens

    Not enough matched evidence

    A complete comparable API-rate estimate is not available for both models.

    Confidence: listed-rates

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
0
Kimi K3 only
40
A.X series only
0
Like-for-like categories
0 / 8

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

Not comparable
Kimi K3
89.5
A.X series
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Kimi K3
Not measured
A.X series
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K3
Not measured
A.X series
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Kimi K3
61.0
A.X series
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Kimi K3
Not measured
A.X series
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K3
Not measured
A.X series
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K3
78.5
A.X series
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Kimi K3
Not measured
A.X series
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.

A shared-evidence shape is not available.

BenchLM does not draw a radar or infer missing axes when the matched evidence is too sparse.

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
A.X series
API rate not published
Fits in one request

A.X series has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Kimi K3
$0.195
Fits in one request
A.X series
API rate not published
Fits in one request

A.X series has no comparable published API token rate.

Cache-heavy agent loop

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

Kimi K3
$0.27
Fits in one request
A.X series
API rate not published
Does not fit in one request
Cached-input rate unavailable

A.X series does not fit this workload in one request. A.X series has no comparable published API token rate.

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

A.X series

64K

API model ID

Kimi K3

Not sourced

A.X series

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

A.X series

No comparable hosted API rate

Documented inputs

Kimi K3

Not sourced

A.X series

Not sourced

Documented outputs

Kimi K3

Not sourced

A.X series

Not sourced

Provider availability

Kimi K3

Not sourced

A.X series

Not sourced

Reasoning profile

Kimi K3

Reasoning

A.X series

Non-Reasoning

Weight access

Kimi K3

Pending

A.X series

Proprietary

License

Kimi K3

Pending

A.X series

Proprietary

Release date

Kimi K3

2026-07-16

A.X series

Not sourced

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
The public evidence has no benchmark result shared by both models, so it does not support a quality verdict.
Workload cost
A complete comparable API-rate estimate is not available for both models.
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 evidence40 rows

Agentic

  • Terminal-Bench 2.0

    Kimi K388.3%
    Source
    A.X series—

    Not directly comparable

  • BrowseComp

    Kimi K391.2%
    Source
    A.X series—

    Not directly comparable

  • DeepSearchQA

    Kimi K395.0%
    Source
    A.X series—

    Not directly comparable

  • Toolathlon-Verified

    Kimi K373.2%
    Source
    A.X series—

    Not directly comparable

  • MCP Atlas

    Kimi K384.2%
    Source
    A.X series—

    Not directly comparable

  • AutomationBench

    Kimi K330.8%
    Source
    A.X series—

    Not directly comparable

  • JobBench

    Kimi K352.9%
    Source
    A.X series—

    Not directly comparable

  • APEX-Agents

    Kimi K337.6%
    Source
    A.X series—

    Not directly comparable

  • SpreadsheetBench 2

    Kimi K334.8%
    Source
    A.X series—

    Not directly comparable

  • DECK-Bench

    Kimi K373.5%
    Source
    A.X series—

    Not directly comparable

Coding

  • deepSwe

    Kimi K367.5%
    Source
    A.X series—

    Not directly comparable

  • cursorBench32

    Kimi K360.8%
    Source
    A.X series—

    Not directly comparable

  • FrontierSWE

    Kimi K381.2%
    Source
    A.X series—

    Not directly comparable

  • ProgramBench

    Kimi K377.8%
    Source
    A.X series—

    Not directly comparable

  • Kimi Code Bench v2

    Kimi K372.9%
    Source
    A.X series—

    Not directly comparable

  • sweMarathon

    Kimi K342%
    Source
    A.X series—

    Not directly comparable

  • PostTrain Bench

    Kimi K336.6%
    Source
    A.X series—

    Not directly comparable

  • MLS-Bench Lite

    Kimi K348.3%
    Source
    A.X series—

    Not directly comparable

  • VulcanBench v3

    Kimi K373.9%
    Source
    A.X series—

    Not directly comparable

  • APEX-SWE

    Kimi K348.0%
    Source
    A.X series—

    Not directly comparable

  • EEBench

    Kimi K338.3%
    Source
    A.X series—

    Not directly comparable

  • InferenceEval

    Kimi K339.8%
    Source
    A.X series—

    Not directly comparable

  • KernelBench Internal

    Kimi K368.5%
    Source
    A.X series—

    Not directly comparable

Knowledge

  • GPQA

    Kimi K393.5%
    Source
    A.X series—

    Not directly comparable

  • GPQA-D

    Kimi K393.5%
    Source
    A.X series—

    Not directly comparable

  • HLE

    Kimi K356%
    Source
    A.X series—

    Not directly comparable

  • HLE w/o tools

    Kimi K343.5%
    Source
    A.X series—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Kimi K363.3%
    Source
    A.X series—

    Not directly comparable

  • MMMU-Pro

    Kimi K381.6%
    Source
    A.X series—

    Not directly comparable

  • MMMU-Pro w/ Python

    Kimi K383.4%
    Source
    A.X series—

    Not directly comparable

  • CharXiv w/o tools

    Kimi K384.8%
    Source
    A.X series—

    Not directly comparable

  • CharXiv

    Kimi K391.3%
    Source
    A.X series—

    Not directly comparable

  • MathVision

    Kimi K394.3%
    Source
    A.X series—

    Not directly comparable

  • MathVision w/ Python

    Kimi K397.8%
    Source
    A.X series—

    Not directly comparable

  • BabyVision w/ Python

    Kimi K385.7%
    Source
    A.X series—

    Not directly comparable

  • ZeroBench

    Kimi K323.0%
    Source
    A.X series—

    Not directly comparable

  • ZeroBench w/ Python

    Kimi K341.0%
    Source
    A.X series—

    Not directly comparable

  • WorldVQA ForceAnswer

    Kimi K351.0%
    Source
    A.X series—

    Not directly comparable

  • OmniDocBench

    Kimi K391.1%
    Source
    A.X series—

    Not directly comparable

  • PerceptionBench

    Kimi K358.5%
    Source
    A.X series—

    Not directly comparable

Frequently asked questions

Which is better, Kimi K3 or A.X series?

The public evidence has no benchmark result shared by both models, so it does not support a quality verdict. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Kimi K3 or A.X series?

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, Kimi K3 or A.X series?

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, Kimi K3 or A.X series?

Both models do not have comparable published API token rates, so this page does not name a universal price winner.

Which has the larger context window, Kimi K3 or A.X series?

Kimi K3 has the larger documented context window: 1.05M, compared with 64K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 19, 2026

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