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Model comparison

GLM-4-Voice vs Pokee-Isaac 28B

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

GLM-4-Voice

Zhipu AI

—

Evidence status unavailable

90% interval unavailable

Pokee-Isaac 28B

Pokee AI

—

Evidence status unavailable

90% interval unavailable

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: 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
0
GLM-4-Voice only
0
Pokee-Isaac 28B only
7
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
GLM-4-Voice
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
GLM-4-Voice
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
GLM-4-Voice
Not measured
Pokee-Isaac 28B
60.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
GLM-4-Voice
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
GLM-4-Voice
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
GLM-4-Voice
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
GLM-4-Voice
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
GLM-4-Voice
Not measured
Pokee-Isaac 28B
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

GLM-4-Voice
API rate not published
Fit state unavailable
Pokee-Isaac 28B
$0.00065
Fits in one request

GLM-4-Voice has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

GLM-4-Voice
API rate not published
Fit state unavailable
Pokee-Isaac 28B
$0.0105
Fits in one request

GLM-4-Voice has no comparable published API token rate.

Cache-heavy agent loop

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

GLM-4-Voice
API rate not published
Fit state unavailable
Cached-input rate unavailable
Pokee-Isaac 28B
$0.043
Fits in one request
Cached input priced at the published list-input rate

Pokee-Isaac 28B has no published cached-input rate, so cached tokens use its listed input rate. GLM-4-Voice 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.

GLM-4-Voice

N/A

Pokee-Isaac 28B

10M

Pokee-Isaac 28B model page

Documented inputs

GLM-4-Voice

Not sourced

Pokee-Isaac 28B

Not sourced

Documented outputs

GLM-4-Voice

Not sourced

Pokee-Isaac 28B

Not sourced

Provider availability

GLM-4-Voice

Not sourced

Pokee-Isaac 28B

Not sourced

Reasoning profile

GLM-4-Voice

Non-Reasoning

Pokee-Isaac 28B

Reasoning

Weight access

GLM-4-Voice

Open Weight

Pokee-Isaac 28B

Proprietary

License

GLM-4-Voice

Open Weight

Pokee-Isaac 28B

Proprietary

Release date

GLM-4-Voice

2024-12-03

Pokee-Isaac 28B

2026-08-03

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 evidence7 rows

Agentic

  • Terminal-Bench 2.1

    GLM-4-Voice—
    Pokee-Isaac 28B65.1%
    Source

    Not directly comparable

  • BFCL v4

    GLM-4-Voice—
    Pokee-Isaac 28B70.9%
    Source

    Not directly comparable

  • τ³-bench results

    GLM-4-Voice—
    Pokee-Isaac 28B66.2%
    Source

    Not directly comparable

  • MCP-Atlas claim coverage

    GLM-4-Voice—
    Pokee-Isaac 28B74.6%
    Source

    Not directly comparable

  • PinchBench

    GLM-4-Voice—
    Pokee-Isaac 28B95.7%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    GLM-4-Voice—
    Pokee-Isaac 28B65.1%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    GLM-4-Voice—
    Pokee-Isaac 28B60.7%
    Source

    Not directly comparable

Frequently asked questions

Which is better, GLM-4-Voice or Pokee-Isaac 28B?

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, GLM-4-Voice or Pokee-Isaac 28B?

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, GLM-4-Voice or Pokee-Isaac 28B?

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, GLM-4-Voice or Pokee-Isaac 28B?

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, GLM-4-Voice or Pokee-Isaac 28B?

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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