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Model comparison

DeepSeek V4 Pro vs Muse Glimmer 30B

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

DeepSeek V4 Pro

DeepSeek

60.0/100

Supported · Public rank #52

90% interval 41.7–78.3

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.

3 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

    DeepSeek V4 Pro

    DeepSeek V4 Pro 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

    The category averages use different weighted benchmark sets, so they are directional rather than like-for-like.

    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. Muse Glimmer 30B 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
3
DeepSeek V4 Pro only
20
Muse Glimmer 30B only
11
Like-for-like categories
0 / 8

1 category uses different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Coding

Directional only
DeepSeek V4 Pro
65.3
Muse Glimmer 30B
57.8
Weighted basis
2 vs 3 rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V4 Pro
59.1
Muse Glimmer 30B
65.9
Weighted basis
1 vs 1 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Pro
Not measured
Muse Glimmer 30B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4 Pro
41.3
Muse Glimmer 30B
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro
31.7
Muse Glimmer 30B
94.7
Weighted basis
1 vs 1 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro
Not measured
Muse Glimmer 30B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro
Not measured
Muse Glimmer 30B
75.7
Weighted basis
0 vs 2 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro
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.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

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

DeepSeek V4 Pro
$0.00087
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

DeepSeek V4 Pro
$0.02436
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

DeepSeek V4 Pro
$0.01812
Fits in one request
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. 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.

Muse Glimmer 30B

131K

Cached-input rate

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

DeepSeek V4 Pro

$0.003625 per 1M cached input tokens

Muse Glimmer 30B

No comparable hosted API rate

Reasoning profile

DeepSeek V4 Pro

Non-Reasoning

Muse Glimmer 30B

Reasoning

Weight access

DeepSeek V4 Pro

Open Weight

Muse Glimmer 30B

Open Weight

License

DeepSeek V4 Pro

Open Weight

Muse Glimmer 30B

Open Weight

Release date

DeepSeek V4 Pro

2026-04-24

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
DeepSeek V4 Pro has the larger documented window (1M).

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 evidence34 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro59.1%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro69.4%
    Source
    Muse Glimmer 30B75.5%
    Source

    Muse Glimmer 30B leads this result

  • Toolathlon

    DeepSeek V4 Pro46.3%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Claw-Eval

    DeepSeek V4 Pro59.8%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Gert Labs

    DeepSeek V4 Pro50.28%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • ResearchClawBench

    DeepSeek V4 Pro17.1%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • DeepSearchQA

    DeepSeek V4 Pro—
    Muse Glimmer 30B74.6%
    Source

    Not directly comparable

  • skillsBench

    DeepSeek V4 Pro—
    Muse Glimmer 30B44.3%
    Source

    Not directly comparable

  • OSWorld-Verified

    DeepSeek V4 Pro—
    Muse Glimmer 30B65.9%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro56.8%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro73.6%
    Source
    Muse Glimmer 30B76%
    Source

    Muse Glimmer 30B leads this result

  • SWE-bench Pro

    DeepSeek V4 Pro52.1%
    Source
    Muse Glimmer 30B51.2%
    Source

    DeepSeek V4 Pro leads this result

  • SWE Multilingual

    DeepSeek V4 Pro69.8%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro59.1%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Pro—
    Muse Glimmer 30B51.7%
    Source

    Not directly comparable

  • SciCode

    DeepSeek V4 Pro—
    Muse Glimmer 30B43.6%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro44.7%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro35.6%
    Source
    Muse Glimmer 30B—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro82.9%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro45%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro75.8%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro72.9%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Pro72.9%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • HLE

    DeepSeek V4 Pro7.7%
    Source
    Muse Glimmer 30B—

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro31.7%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro35.3%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro0.4%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro9.2%
    Source
    Muse Glimmer 30B—

    Not directly comparable

  • AIME26

    DeepSeek V4 Pro—
    Muse Glimmer 30B94.7%
    Source

    Not directly comparable

Multimodal

  • CharXiv

    DeepSeek V4 Pro—
    Muse Glimmer 30B78.8%
    Source

    Not directly comparable

  • ScreenSpot Pro

    DeepSeek V4 Pro—
    Muse Glimmer 30B75.4%
    Source

    Not directly comparable

  • OmniDocBench 1.5

    DeepSeek V4 Pro—
    Muse Glimmer 30B75.8%
    Source

    Not directly comparable

  • MMMU-Pro

    DeepSeek V4 Pro—
    Muse Glimmer 30B74%
    Source

    Not directly comparable

Instruction following

  • IFBench

    DeepSeek V4 Pro—
    Muse Glimmer 30B77%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro 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, DeepSeek V4 Pro or Muse Glimmer 30B?

The current coding averages use different weighted benchmark sets, so BenchLM does not name a winner from them. Read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, DeepSeek V4 Pro 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, DeepSeek V4 Pro 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, DeepSeek V4 Pro or Muse Glimmer 30B?

DeepSeek V4 Pro has the larger documented context window: 1M, compared with 131K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 10, 2026

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