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Model comparison

Kimi K3 vs Muse Spark 1.2

Updated August 5, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Kimi K3

Moonshot AI

79.9/100

Supported · Public rank #5

90% interval 77.0–82.8

Muse Spark 1.2

Meta

60.3/100

Estimated · Public rank #49

90% interval 48.7–71.8

Kimi K3 has the higher public score, 79.9 versus 60.25, and the 90% score intervals do not overlap.

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

    Muse Spark 1.2

    Muse Spark 1.2 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

    Muse Spark 1.2

    Muse Spark 1.2 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

    Muse Spark 1.2

    Muse Spark 1.2 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
Kimi K3 only
34
Muse Spark 1.2 only
2
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
Muse Spark 1.2
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
Kimi K3
Not measured
Muse Spark 1.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K3
Not measured
Muse Spark 1.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Kimi K3
61.0
Muse Spark 1.2
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
Kimi K3
Not measured
Muse Spark 1.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K3
Not measured
Muse Spark 1.2
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K3
78.5
Muse Spark 1.2
Not measured
Weighted basis
3 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
Kimi K3
Not measured
Muse Spark 1.2
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
Muse Spark 1.2
$0.00338
Fits in one request

Muse Spark 1.2 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
Muse Spark 1.2
$0.07525
Fits in one request

Muse Spark 1.2 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
Muse Spark 1.2
$0.0975
Fits in one request

Muse Spark 1.2 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.

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

Muse Spark 1.2

$0.15 per 1M cached input tokens

Meta: Muse Spark 1.2 model page

Documented inputs

Kimi K3

Not sourced

Muse Spark 1.2

Not sourced

Documented outputs

Kimi K3

Not sourced

Muse Spark 1.2

Not sourced

Provider availability

Kimi K3

Not sourced

Muse Spark 1.2

Not sourced

Reasoning profile

Kimi K3

Reasoning

Muse Spark 1.2

Reasoning

Weight access

Kimi K3

Pending

Muse Spark 1.2

Proprietary

License

Kimi K3

Pending

Muse Spark 1.2

Proprietary

Release date

Kimi K3

2026-07-16

Muse Spark 1.2

2026-08-05

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, 79.9 versus 60.25, and the 90% score intervals do not overlap.
Workload cost
Repository review: $0.195 vs $0.07525. Cache-heavy agent loop: $0.27 vs $0.0975.
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 evidence37 rows

Agentic

  • Terminal-Bench 2.0

    Kimi K388.3%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • BrowseComp

    Kimi K391.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • DeepSearchQA

    Kimi K395.0%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Toolathlon-Verified

    Kimi K373.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MCP Atlas

    Kimi K384.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • AutomationBench

    Kimi K330.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • JobBench

    Kimi K352.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • APEX-Agents

    Kimi K337.6%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • SpreadsheetBench 2

    Kimi K334.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • DECK-Bench

    Kimi K373.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.1

    Kimi K3—
    Muse Spark 1.282.9%
    Source

    Not directly comparable

Coding

  • deepSwe

    Kimi K367.5%
    Source
    Muse Spark 1.259.3%
    Source

    Kimi K3 leads this result

  • FrontierSWE

    Kimi K381.2%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ProgramBench

    Kimi K377.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Kimi Code Bench v2

    Kimi K372.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • sweMarathon

    Kimi K342%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • PostTrain Bench

    Kimi K336.6%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MLS-Bench Lite

    Kimi K348.3%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • VulcanBench v3

    Kimi K373.9%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • Terminal-Bench 2.1

    Kimi K3—
    Muse Spark 1.282.9%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Kimi K393.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • GPQA-D

    Kimi K393.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HLE

    Kimi K356%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • HLE w/o tools

    Kimi K343.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

Multimodal

  • OfficeQA Pro

    Kimi K363.3%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MMMU-Pro

    Kimi K381.6%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MMMU-Pro w/ Python

    Kimi K383.4%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • CharXiv w/o tools

    Kimi K384.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • CharXiv

    Kimi K391.3%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MathVision

    Kimi K394.3%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • MathVision w/ Python

    Kimi K397.8%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • BabyVision w/ Python

    Kimi K385.7%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ZeroBench

    Kimi K323.0%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • ZeroBench w/ Python

    Kimi K341.0%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • WorldVQA ForceAnswer

    Kimi K351.0%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • OmniDocBench

    Kimi K391.1%
    Source
    Muse Spark 1.2—

    Not directly comparable

  • PerceptionBench

    Kimi K358.5%
    Source
    Muse Spark 1.2—

    Not directly comparable

Frequently asked questions

Which is better, Kimi K3 or Muse Spark 1.2?

Kimi K3 has the higher public score, 79.9 versus 60.25, 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 Muse Spark 1.2?

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 Muse Spark 1.2?

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 Muse Spark 1.2?

For the stated presets, chat costs $0.0105 on Kimi K3 and $0.00337 on Muse Spark 1.2; repository review costs $0.195 and $0.07525; the cache-heavy agent loop costs $0.27 and $0.0975. Costs use the listed standard API rates.

Which has the larger context window, Kimi K3 or Muse Spark 1.2?

Kimi K3 has the larger documented context window: 1.05M, compared with 1M.

Related comparisons

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Last updated August 5, 2026

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