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BenchLM
Model A
Grok Build 0.1

xAI

—

Evidence status unavailable

90% interval unavailable

Grok Build 0.1 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

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.

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

    Grok Build 0.1

    Grok Build 0.1 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

    Grok Build 0.1

    Grok Build 0.1 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

    Grok Build 0.1

    Grok Build 0.1 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
Grok Build 0.1 only
1
Kimi K3 only
39
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
Grok Build 0.1
Not measured
Kimi K3
89.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

Not comparable
Grok Build 0.1
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Grok Build 0.1
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Grok Build 0.1
Not measured
Kimi K3
61.0
Weighted basis
0 vs 2 rows
Reading
Not comparable

Math

Not comparable
Grok Build 0.1
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Grok Build 0.1
Not measured
Kimi K3
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Grok Build 0.1
Not measured
Kimi K3
78.5
Weighted basis
0 vs 3 rows
Reading
Not comparable

Instruction following

Not comparable
Grok Build 0.1
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.

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

Grok Build 0.1
$0.002
Fits in one request
Kimi K3
$0.0105
Fits in one request

Grok Build 0.1 has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Grok Build 0.1
$0.056
Fits in one request
Kimi K3
$0.195
Fits in one request

Grok Build 0.1 has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

Grok Build 0.1
$0.08
Fits in one request
Kimi K3
$0.27
Fits in one request

Grok Build 0.1 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.

Grok Build 0.1

256K

Kimi K3

1.05M

API model ID

Grok Build 0.1

Not sourced

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.

Grok Build 0.1

$0.2 per 1M cached input tokens

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

Grok Build 0.1

Not sourced

Kimi K3

Not sourced

Documented outputs

Grok Build 0.1

Not sourced

Kimi K3

Not sourced

Provider availability

Grok Build 0.1

Not sourced

Kimi K3

Not sourced

Reasoning profile

Grok Build 0.1

Non-Reasoning

Kimi K3

Reasoning

Weight access

Grok Build 0.1

Proprietary

Kimi K3

Pending

License

Grok Build 0.1

Proprietary

Kimi K3

Pending

Release date

Grok Build 0.1

2026-05-20

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
At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner.
Workload cost
Repository review: $0.056 vs $0.195. Cache-heavy agent loop: $0.08 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 evidence41 rows

Agentic

  • Gert Labs

    Grok Build 0.149.15%
    Source
    Kimi K3—

    Not directly comparable

  • Terminal-Bench 2.0

    Grok Build 0.1—
    Kimi K388.3%
    Source

    Not directly comparable

  • BrowseComp

    Grok Build 0.1—
    Kimi K391.2%
    Source

    Not directly comparable

  • DeepSearchQA

    Grok Build 0.1—
    Kimi K395.0%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Grok Build 0.1—
    Kimi K373.2%
    Source

    Not directly comparable

  • MCP Atlas

    Grok Build 0.1—
    Kimi K384.2%
    Source

    Not directly comparable

  • AutomationBench

    Grok Build 0.1—
    Kimi K330.8%
    Source

    Not directly comparable

  • JobBench

    Grok Build 0.1—
    Kimi K352.9%
    Source

    Not directly comparable

  • APEX-Agents

    Grok Build 0.1—
    Kimi K337.6%
    Source

    Not directly comparable

  • SpreadsheetBench 2

    Grok Build 0.1—
    Kimi K334.8%
    Source

    Not directly comparable

  • DECK-Bench

    Grok Build 0.1—
    Kimi K373.5%
    Source

    Not directly comparable

Coding

  • EEBench

    Shared source
    Grok Build 0.118.0%
    Kimi K338.3%

    Kimi K3 leads this result

  • deepSwe

    Grok Build 0.1—
    Kimi K367.5%
    Source

    Not directly comparable

  • cursorBench32

    Grok Build 0.1—
    Kimi K360.8%
    Source

    Not directly comparable

  • FrontierSWE

    Grok Build 0.1—
    Kimi K381.2%
    Source

    Not directly comparable

  • ProgramBench

    Grok Build 0.1—
    Kimi K377.8%
    Source

    Not directly comparable

  • Kimi Code Bench v2

    Grok Build 0.1—
    Kimi K372.9%
    Source

    Not directly comparable

  • sweMarathon

    Grok Build 0.1—
    Kimi K342%
    Source

    Not directly comparable

  • PostTrain Bench

    Grok Build 0.1—
    Kimi K336.6%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Grok Build 0.1—
    Kimi K348.3%
    Source

    Not directly comparable

  • VulcanBench v3

    Grok Build 0.1—
    Kimi K373.9%
    Source

    Not directly comparable

  • APEX-SWE

    Grok Build 0.1—
    Kimi K348.0%
    Source

    Not directly comparable

  • InferenceEval

    Grok Build 0.1—
    Kimi K339.8%
    Source

    Not directly comparable

  • KernelBench Internal

    Grok Build 0.1—
    Kimi K368.5%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Grok Build 0.1—
    Kimi K393.5%
    Source

    Not directly comparable

  • GPQA-D

    Grok Build 0.1—
    Kimi K393.5%
    Source

    Not directly comparable

  • HLE

    Grok Build 0.1—
    Kimi K356%
    Source

    Not directly comparable

  • HLE w/o tools

    Grok Build 0.1—
    Kimi K343.5%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Grok Build 0.1—
    Kimi K363.3%
    Source

    Not directly comparable

  • MMMU-Pro

    Grok Build 0.1—
    Kimi K381.6%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Grok Build 0.1—
    Kimi K383.4%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Grok Build 0.1—
    Kimi K384.8%
    Source

    Not directly comparable

  • CharXiv

    Grok Build 0.1—
    Kimi K391.3%
    Source

    Not directly comparable

  • MathVision

    Grok Build 0.1—
    Kimi K394.3%
    Source

    Not directly comparable

  • MathVision w/ Python

    Grok Build 0.1—
    Kimi K397.8%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Grok Build 0.1—
    Kimi K385.7%
    Source

    Not directly comparable

  • ZeroBench

    Grok Build 0.1—
    Kimi K323.0%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Grok Build 0.1—
    Kimi K341.0%
    Source

    Not directly comparable

  • WorldVQA ForceAnswer

    Grok Build 0.1—
    Kimi K351.0%
    Source

    Not directly comparable

  • OmniDocBench

    Grok Build 0.1—
    Kimi K391.1%
    Source

    Not directly comparable

  • PerceptionBench

    Grok Build 0.1—
    Kimi K358.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Grok Build 0.1 or Kimi K3?

At least one model is not scored in the current public ranking lane, so the page does not name an overall quality winner. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Grok Build 0.1 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, Grok Build 0.1 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, Grok Build 0.1 or Kimi K3?

For the stated presets, chat costs $0.002 on Grok Build 0.1 and $0.0105 on Kimi K3; repository review costs $0.056 and $0.195; the cache-heavy agent loop costs $0.08 and $0.27. Costs use the listed standard API rates.

Which has the larger context window, Grok Build 0.1 or Kimi K3?

Kimi K3 has the larger documented context window: 1.05M, compared with 256K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 19, 2026

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