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Model comparison

DeepSeek V4 Flash (Max) vs Pokee-Isaac 28B

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

DeepSeek V4 Flash (Max)

DeepSeek

—

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.

2 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

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Flash (Max)

    DeepSeek V4 Flash (Max) 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

    DeepSeek V4 Flash (Max)

    DeepSeek V4 Flash (Max) has the lower estimated token cost for this stated workload. Pokee-Isaac 28B has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

Show secondary and unsupported calls

  • Repository review cost

    50K fresh input + 3K output tokens

    DeepSeek V4 Flash (Max)

    DeepSeek V4 Flash (Max) 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
2
DeepSeek V4 Flash (Max) only
31
Pokee-Isaac 28B only
5
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
DeepSeek V4 Flash (Max)
63.8
Pokee-Isaac 28B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4 Flash (Max)
68.8
Pokee-Isaac 28B
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Flash (Max)
Not measured
Pokee-Isaac 28B
60.7
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4 Flash (Max)
55.3
Pokee-Isaac 28B
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Flash (Max)
94.8
Pokee-Isaac 28B
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Flash (Max)
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Flash (Max)
Not measured
Pokee-Isaac 28B
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Flash (Max)
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

DeepSeek V4 Flash (Max)
$0.00028
Fits in one request
Pokee-Isaac 28B
$0.00065
Fits in one request

DeepSeek V4 Flash (Max) has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Flash (Max)
$0.00784
Fits in one request
Pokee-Isaac 28B
$0.0105
Fits in one request

DeepSeek V4 Flash (Max) has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

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

DeepSeek V4 Flash (Max)
$0.00616
Fits in one request
Pokee-Isaac 28B
$0.043
Fits in one request
Cached input priced at the published list-input rate

DeepSeek V4 Flash (Max) has the lower modeled cost

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

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.

DeepSeek V4 Flash (Max)

$0.0028 per 1M cached input tokens

Pokee-Isaac 28B

Not published

Pokee-Isaac 28B model page

Reasoning profile

DeepSeek V4 Flash (Max)

Reasoning

Pokee-Isaac 28B

Reasoning

Weight access

DeepSeek V4 Flash (Max)

Proprietary

Pokee-Isaac 28B

Proprietary

License

DeepSeek V4 Flash (Max)

Proprietary

Pokee-Isaac 28B

Proprietary

Release date

DeepSeek V4 Flash (Max)

2026-07-31

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
Repository review: $0.00784 vs $0.0105. Cache-heavy agent loop: $0.00616 vs $0.043.
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 evidence38 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Flash (Max)56.9%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Flash (Max)73.2%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Flash (Max)45.1%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Flash (Max)69%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Flash (Max)47.8%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Flash (Max)82.7%
    Source
    Pokee-Isaac 28B65.1%
    Source

    DeepSeek V4 Flash (Max) leads this result

  • CyberGym

    DeepSeek V4 Flash (Max)76.7%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V4 Flash (Max)70.3%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Agents' Last Exam

    DeepSeek V4 Flash (Max)25.2%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • AutomationBench

    DeepSeek V4 Flash (Max)25.1%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • BFCL v4

    DeepSeek V4 Flash (Max)—
    Pokee-Isaac 28B70.9%
    Source

    Not directly comparable

  • τ³-bench results

    DeepSeek V4 Flash (Max)—
    Pokee-Isaac 28B66.2%
    Source

    Not directly comparable

  • MCP-Atlas claim coverage

    DeepSeek V4 Flash (Max)—
    Pokee-Isaac 28B74.6%
    Source

    Not directly comparable

  • PinchBench

    DeepSeek V4 Flash (Max)—
    Pokee-Isaac 28B95.7%
    Source

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Flash (Max)91.6%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Flash (Max)3052.0
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Flash (Max)79%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Flash (Max)52.6%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Flash (Max)73.3%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Flash (Max)56.9%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V4 Flash (Max)82.7%
    Source
    Pokee-Isaac 28B65.1%
    Source

    DeepSeek V4 Flash (Max) leads this result

  • NL2Repo

    DeepSeek V4 Flash (Max)54.2%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • deepSwe

    DeepSeek V4 Flash (Max)54.4%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • DSBench-FullStack

    DeepSeek V4 Flash (Max)68.7%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • DSBench-Hard

    DeepSeek V4 Flash (Max)59.6%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Flash (Max)78.7%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Flash (Max)60.5%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • MRCRv2

    DeepSeek V4 Flash (Max)—
    Pokee-Isaac 28B60.7%
    Source

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Flash (Max)86.2%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Flash (Max)34.1%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Flash (Max)78.9%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • GPQA

    DeepSeek V4 Flash (Max)88.1%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Flash (Max)88.1%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • HLE

    DeepSeek V4 Flash (Max)34.8%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Flash (Max)94.8%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Flash (Max)88.4%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Apex

    DeepSeek V4 Flash (Max)33.0%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Flash (Max)85.7%
    Source
    Pokee-Isaac 28B—

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Flash (Max) 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, DeepSeek V4 Flash (Max) 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, DeepSeek V4 Flash (Max) 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, DeepSeek V4 Flash (Max) or Pokee-Isaac 28B?

For the stated presets, chat costs $0.00028 on DeepSeek V4 Flash (Max) and $0.00065 on Pokee-Isaac 28B; repository review costs $0.00784 and $0.0105; the cache-heavy agent loop costs $0.00616 and $0.043. Pokee-Isaac 28B has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, DeepSeek V4 Flash (Max) or Pokee-Isaac 28B?

Pokee-Isaac 28B has the larger documented context window: 10M, compared with 1M.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 4, 2026

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