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Model comparison

Kimi K2.7 Code vs Muse Glimmer 30B

Updated August 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Kimi K2.7 Code

Moonshot AI

54.1/100

Estimated · Public rank #95

90% interval 42.2–65.9

Muse Glimmer 30B

Meta

—

Evidence status unavailable

90% interval unavailable

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 K2.7 Code

    Kimi K2.7 Code 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. Muse Glimmer 30B does not fit this workload in one request. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate. Muse Glimmer 30B has no comparable published API token rate.

    Confidence: rate-fallback

  • 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
1
Kimi K2.7 Code only
6
Muse Glimmer 30B only
13
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 K2.7 Code
Not measured
Muse Glimmer 30B
65.9
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
Kimi K2.7 Code
Not measured
Muse Glimmer 30B
57.8
Weighted basis
0 vs 3 rows
Reading
Not comparable

Reasoning

Not comparable
Kimi K2.7 Code
Not measured
Muse Glimmer 30B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
Kimi K2.7 Code
Not measured
Muse Glimmer 30B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
Kimi K2.7 Code
Not measured
Muse Glimmer 30B
94.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
Kimi K2.7 Code
Not measured
Muse Glimmer 30B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
Kimi K2.7 Code
Not measured
Muse Glimmer 30B
75.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
Kimi K2.7 Code
Not measured
Muse Glimmer 30B
77.0
Weighted basis
0 vs 1 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 K2.7 Code
$0.00295
Fits in one request
Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Fits in one request

Muse Glimmer 30B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Kimi K2.7 Code
$0.0595
Fits in one request
Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Fits in one request

Muse Glimmer 30B has no comparable published API token rate.

Cache-heavy agent loop

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

Kimi K2.7 Code
$0.249
Fits in one request
Cached input priced at the published list-input rate
Muse Glimmer 30B
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

Muse Glimmer 30B does not fit this workload in one request. Kimi K2.7 Code has no published cached-input rate, so cached tokens use its listed input rate. Muse Glimmer 30B 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 K2.7 Code

256K

Muse Glimmer 30B

131K

API model ID

Kimi K2.7 Code

Not sourced

Muse Glimmer 30B

Not sourced

Cached-input rate

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

Kimi K2.7 Code

Not published

Muse Glimmer 30B

No comparable hosted API rate

Documented inputs

Kimi K2.7 Code

Not sourced

Muse Glimmer 30B

Not sourced

Documented outputs

Kimi K2.7 Code

Not sourced

Muse Glimmer 30B

Not sourced

Provider availability

Kimi K2.7 Code

Not sourced

Muse Glimmer 30B

Not sourced

Reasoning profile

Kimi K2.7 Code

Reasoning

Muse Glimmer 30B

Reasoning

Weight access

Kimi K2.7 Code

Open Weight

Muse Glimmer 30B

Open Weight

License

Kimi K2.7 Code

Open Weight

Muse Glimmer 30B

Open Weight

Release date

Kimi K2.7 Code

2026-06-12

Muse Glimmer 30B

2026-08-10

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
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Kimi K2.7 Code has the larger documented window (256K).

Run the same representative tasks against both endpoints before changing production traffic.

Self-host vs API cost

Estimates at 50,000 req/day · 1000 tokens/req average.

Kimi K2.7 Code
API / mo$3,713
Self-host / mo$18,221
Break-even326M/day
Muse Glimmer 30B
API / mo$0
Self-host / moNot listed
Break-even—
Proprietary model — self-hosting not applicable.
Model the full break-even

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence20 rows

Agentic

  • Kimi Claw 24/7

    Kimi K2.7 Code46.9%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • MCP Atlas

    Kimi K2.7 Code76%
    Source
    Muse Glimmer 30B75.5%
    Source

    Kimi K2.7 Code leads this result

  • MCP Mark Verified

    Kimi K2.7 Code81.1%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • DeepSearchQA

    Kimi K2.7 Code—
    Muse Glimmer 30B74.6%
    Source

    Not directly comparable

  • skillsBench

    Kimi K2.7 Code—
    Muse Glimmer 30B44.3%
    Source

    Not directly comparable

  • OSWorld-Verified

    Kimi K2.7 Code—
    Muse Glimmer 30B65.9%
    Source

    Not directly comparable

Coding

  • Kimi Code Bench v2

    Kimi K2.7 Code62.0%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • ProgramBench

    Kimi K2.7 Code53.6%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • MLS-Bench Lite

    Kimi K2.7 Code35.1%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • cursorBench32

    Kimi K2.7 Code49.7%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • SWE-bench Pro

    Kimi K2.7 Code—
    Muse Glimmer 30B51.2%
    Source

    Not directly comparable

  • SWE-bench Verified

    Kimi K2.7 Code—
    Muse Glimmer 30B76%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Kimi K2.7 Code—
    Muse Glimmer 30B51.7%
    Source

    Not directly comparable

  • SciCode

    Kimi K2.7 Code—
    Muse Glimmer 30B43.6%
    Source

    Not directly comparable

Math

  • AIME26

    Kimi K2.7 Code—
    Muse Glimmer 30B94.7%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    Kimi K2.7 Code—
    Muse Glimmer 30B78.8%
    Source

    Not directly comparable

  • ScreenSpot Pro

    Kimi K2.7 Code—
    Muse Glimmer 30B75.4%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    Kimi K2.7 Code—
    Muse Glimmer 30B75.8%
    Source

    Not directly comparable

  • MMMU-Pro

    Kimi K2.7 Code—
    Muse Glimmer 30B74%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Kimi K2.7 Code—
    Muse Glimmer 30B77%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Kimi K2.7 Code or Muse Glimmer 30B?

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, Kimi K2.7 Code or Muse Glimmer 30B?

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 K2.7 Code or Muse Glimmer 30B?

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 K2.7 Code or Muse Glimmer 30B?

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 K2.7 Code or Muse Glimmer 30B?

Kimi K2.7 Code has the larger documented context window: 256K, compared with 131K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

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