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Model comparison

Grok TTS vs Kimi K3

Updated August 4, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Grok TTS

xAI

—

Evidence status unavailable

90% interval unavailable

Kimi K3

Moonshot AI

79.9/100

Supported · Public rank #5

90% interval 77.0–82.8

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.

No workload recommendation clears the current evidence threshold.

Use the matched evidence, workload costs, and sourced specifications below instead of treating a point score as a universal answer.

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

  • Long documents

    Prompts that approach the documented context limit

    Not enough matched evidence

    A complete context comparison is not sourced.

    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

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

    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
Grok TTS only
0
Kimi K3 only
35
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 TTS
Not measured
Kimi K3
89.5
Weighted basis
0 vs 2 rows
Reading
Not comparable

Coding

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

Reasoning

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

Knowledge

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

Math

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

Multilingual

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

Multimodal

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

Instruction following

Not comparable
Grok TTS
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 TTS
API rate not published
Fit state unavailable
Kimi K3
$0.0105
Fits in one request

Grok TTS has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Grok TTS
API rate not published
Fit state unavailable
Kimi K3
$0.195
Fits in one request

Grok TTS has no comparable published API token rate.

Cache-heavy agent loop

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

Grok TTS
API rate not published
Fit state unavailable
Cached-input rate unavailable
Kimi K3
$0.27
Fits in one request

Grok TTS 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.

Grok TTS

N/A

Kimi K3

1.05M

API model ID

Grok TTS

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 TTS

No comparable hosted API rate

Kimi K3

$0.3 per 1M cached input tokens

Documented inputs

Grok TTS

Not sourced

Kimi K3

Not sourced

Documented outputs

Grok TTS

Not sourced

Kimi K3

Not sourced

Provider availability

Grok TTS

Not sourced

Kimi K3

Not sourced

Reasoning profile

Grok TTS

Non-Reasoning

Kimi K3

Reasoning

Weight access

Grok TTS

Proprietary

Kimi K3

Pending

License

Grok TTS

Proprietary

Kimi K3

Pending

Release date

Grok TTS

2026-04-17

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
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
A complete documented context comparison is not available.

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 evidence35 rows

Agentic

  • Terminal-Bench 2.0

    Grok TTS—
    Kimi K388.3%
    Source

    Not directly comparable

  • BrowseComp

    Grok TTS—
    Kimi K391.2%
    Source

    Not directly comparable

  • DeepSearchQA

    Grok TTS—
    Kimi K395.0%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Grok TTS—
    Kimi K373.2%
    Source

    Not directly comparable

  • MCP Atlas

    Grok TTS—
    Kimi K384.2%
    Source

    Not directly comparable

  • AutomationBench

    Grok TTS—
    Kimi K330.8%
    Source

    Not directly comparable

  • JobBench

    Grok TTS—
    Kimi K352.9%
    Source

    Not directly comparable

  • APEX-Agents

    Grok TTS—
    Kimi K337.6%
    Source

    Not directly comparable

  • SpreadsheetBench 2

    Grok TTS—
    Kimi K334.8%
    Source

    Not directly comparable

  • DECK-Bench

    Grok TTS—
    Kimi K373.5%
    Source

    Not directly comparable

Coding

  • deepSwe

    Grok TTS—
    Kimi K367.5%
    Source

    Not directly comparable

  • FrontierSWE

    Grok TTS—
    Kimi K381.2%
    Source

    Not directly comparable

  • ProgramBench

    Grok TTS—
    Kimi K377.8%
    Source

    Not directly comparable

  • Kimi Code Bench v2

    Grok TTS—
    Kimi K372.9%
    Source

    Not directly comparable

  • sweMarathon

    Grok TTS—
    Kimi K342%
    Source

    Not directly comparable

  • PostTrain Bench

    Grok TTS—
    Kimi K336.6%
    Source

    Not directly comparable

  • MLS-Bench Lite

    Grok TTS—
    Kimi K348.3%
    Source

    Not directly comparable

  • VulcanBench v3

    Grok TTS—
    Kimi K373.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Grok TTS—
    Kimi K393.5%
    Source

    Not directly comparable

  • GPQA-D

    Grok TTS—
    Kimi K393.5%
    Source

    Not directly comparable

  • HLE

    Grok TTS—
    Kimi K356%
    Source

    Not directly comparable

  • HLE w/o tools

    Grok TTS—
    Kimi K343.5%
    Source

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Grok TTS—
    Kimi K363.3%
    Source

    Not directly comparable

  • MMMU-Pro

    Grok TTS—
    Kimi K381.6%
    Source

    Not directly comparable

  • MMMU-Pro w/ Python

    Grok TTS—
    Kimi K383.4%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Grok TTS—
    Kimi K384.8%
    Source

    Not directly comparable

  • CharXiv

    Grok TTS—
    Kimi K391.3%
    Source

    Not directly comparable

  • MathVision

    Grok TTS—
    Kimi K394.3%
    Source

    Not directly comparable

  • MathVision w/ Python

    Grok TTS—
    Kimi K397.8%
    Source

    Not directly comparable

  • BabyVision w/ Python

    Grok TTS—
    Kimi K385.7%
    Source

    Not directly comparable

  • ZeroBench

    Grok TTS—
    Kimi K323.0%
    Source

    Not directly comparable

  • ZeroBench w/ Python

    Grok TTS—
    Kimi K341.0%
    Source

    Not directly comparable

  • WorldVQA ForceAnswer

    Grok TTS—
    Kimi K351.0%
    Source

    Not directly comparable

  • OmniDocBench

    Grok TTS—
    Kimi K391.1%
    Source

    Not directly comparable

  • PerceptionBench

    Grok TTS—
    Kimi K358.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Grok TTS or Kimi K3?

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, Grok TTS 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 TTS 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 TTS or Kimi K3?

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, Grok TTS or Kimi K3?

A complete documented context-window comparison is not available.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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