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Model comparison

LFM2.5-230M vs Pokee-Isaac 28B

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

LFM2.5-230M

LiquidAI

—

Evidence status unavailable

90% interval unavailable

Pokee-Isaac 28B

Pokee AI

—

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

    Pokee-Isaac 28B

    Pokee-Isaac 28B 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. LFM2.5-230M does not fit this workload in one request. Pokee-Isaac 28B has no published cached-input rate, so cached tokens use its listed input rate. LFM2.5-230M has no comparable published API token rate.

    Confidence: rate-fallback

  • Repository review cost

    50K fresh input + 3K 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. LFM2.5-230M does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate.

    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
LFM2.5-230M only
5
Pokee-Isaac 28B only
6
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
LFM2.5-230M
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Coding

Not comparable
LFM2.5-230M
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
LFM2.5-230M
Not measured
Pokee-Isaac 28B
60.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
LFM2.5-230M
21.2
Pokee-Isaac 28B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Math

Not comparable
LFM2.5-230M
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
LFM2.5-230M
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
LFM2.5-230M
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
LFM2.5-230M
50.1
Pokee-Isaac 28B
Not measured
Weighted basis
2 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

LFM2.5-230M
Self-hosted; infrastructure cost varies
Fits in one request
Pokee-Isaac 28B
$0.00065
Fits in one request

LFM2.5-230M has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

LFM2.5-230M
Self-hosted; infrastructure cost varies
Does not fit in one request
Pokee-Isaac 28B
$0.0105
Fits in one request

LFM2.5-230M does not fit this workload in one request. LFM2.5-230M has no comparable published API token rate.

Cache-heavy agent loop

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

LFM2.5-230M
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable
Pokee-Isaac 28B
$0.043
Fits in one request
Cached input priced at the published list-input rate

LFM2.5-230M does not fit this workload in one request. Pokee-Isaac 28B has no published cached-input rate, so cached tokens use its listed input rate. LFM2.5-230M 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.

LFM2.5-230M

32K

Pokee-Isaac 28B

10M

Pokee-Isaac 28B model page

Cached-input rate

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

LFM2.5-230M

No comparable hosted API rate

Pokee-Isaac 28B

Not published

Pokee-Isaac 28B model page

Documented inputs

LFM2.5-230M

Not sourced

Pokee-Isaac 28B

Not sourced

Documented outputs

LFM2.5-230M

Not sourced

Pokee-Isaac 28B

Not sourced

Provider availability

LFM2.5-230M

Not sourced

Pokee-Isaac 28B

Not sourced

Reasoning profile

LFM2.5-230M

Non-Reasoning

Pokee-Isaac 28B

Reasoning

Weight access

LFM2.5-230M

Open Weight

Pokee-Isaac 28B

Proprietary

License

LFM2.5-230M

Open Weight

Pokee-Isaac 28B

Proprietary

Release date

LFM2.5-230M

2026-06-25

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
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
Pokee-Isaac 28B has the larger documented window (10M).

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 evidence12 rows

Agentic

  • BFCL v4

    LFM2.5-230M21.0%
    Source
    Pokee-Isaac 28B70.9%
    Source

    Pokee-Isaac 28B leads this result

  • Terminal-Bench 2.1

    LFM2.5-230M—
    Pokee-Isaac 28B65.1%
    Source

    Not directly comparable

  • τ³-bench results

    LFM2.5-230M—
    Pokee-Isaac 28B66.2%
    Source

    Not directly comparable

  • MCP-Atlas claim coverage

    LFM2.5-230M—
    Pokee-Isaac 28B74.6%
    Source

    Not directly comparable

  • PinchBench

    LFM2.5-230M—
    Pokee-Isaac 28B95.7%
    Source

    Not directly comparable

Coding

  • Terminal-Bench 2.1

    LFM2.5-230M—
    Pokee-Isaac 28B65.1%
    Source

    Not directly comparable

Reasoning

  • MRCRv2

    LFM2.5-230M—
    Pokee-Isaac 28B60.7%
    Source

    Not directly comparable

Knowledge

  • GPQA

    LFM2.5-230M25.4%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • GPQA-D

    LFM2.5-230M25.4%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • MMLU-Pro

    LFM2.5-230M20.3%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

Instruction following

  • IFEval

    LFM2.5-230M71.7%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • IFBench

    LFM2.5-230M38.4%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

Frequently asked questions

Which is better, LFM2.5-230M or Pokee-Isaac 28B?

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, LFM2.5-230M 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, LFM2.5-230M 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, LFM2.5-230M 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, LFM2.5-230M or Pokee-Isaac 28B?

Pokee-Isaac 28B has the larger documented context window: 10M, compared with 32K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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