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Model comparison

DeepSeek V4 Pro (High) vs Interfaze Beta

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

DeepSeek V4 Pro (High)

DeepSeek

55.5/100

Estimated · Public rank #87

90% interval 44.0–67.0

Interfaze Beta

Interfaze

—

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.

  • Chat turn cost

    1K fresh input + 500 output tokens

    DeepSeek V4 Pro (High)

    DeepSeek V4 Pro (High) 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 Pro (High)

    DeepSeek V4 Pro (High) has the lower estimated token cost for this stated workload. Interfaze Beta 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 Pro (High)

    DeepSeek V4 Pro (High) 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

  • Long documents

    Prompts that approach the documented context limit

    No clear pick

    The documented context windows are equal.

    Confidence: documented

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 Pro (High) only
21
Interfaze Beta only
7
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.

Knowledge

Directional only
DeepSeek V4 Pro (High)
57.0
Interfaze Beta
89.9
Weighted basis
4 vs 1 rows
Reading
Directional only

Agentic

Not comparable
DeepSeek V4 Pro (High)
70.6
Interfaze Beta
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4 Pro (High)
69.8
Interfaze Beta
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Pro (High)
Not measured
Interfaze Beta
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro (High)
94.0
Interfaze Beta
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro (High)
Not measured
Interfaze Beta
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro (High)
Not measured
Interfaze Beta
71.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro (High)
Not measured
Interfaze Beta
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.

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 (High)
$0.00087
Fits in one request
Interfaze Beta
$0.00325
Fits in one request

DeepSeek V4 Pro (High) has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro (High)
$0.02436
Fits in one request
Interfaze Beta
$0.0855
Fits in one request

DeepSeek V4 Pro (High) 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 Pro (High)
$0.01812
Fits in one request
Interfaze Beta
$0.365
Fits in one request
Cached input priced at the published list-input rate

DeepSeek V4 Pro (High) has the lower modeled cost

Interfaze Beta 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.

Context window

Maximum documented context; output-token limits may be lower.

DeepSeek V4 Pro (High)

1M

DeepSeek models and pricing

Interfaze Beta

1M

Cached-input rate

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

DeepSeek V4 Pro (High)

$0.003625 per 1M cached input tokens

Interfaze Beta

Not published

Reasoning profile

DeepSeek V4 Pro (High)

Reasoning

Interfaze Beta

Reasoning

Weight access

DeepSeek V4 Pro (High)

Open Weight

Interfaze Beta

Proprietary

License

DeepSeek V4 Pro (High)

Open Weight

Interfaze Beta

Proprietary

Release date

DeepSeek V4 Pro (High)

2026-04-24

Interfaze Beta

2026-05-11

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: $0.02436 vs $0.0855. Cache-heavy agent loop: $0.01812 vs $0.365.
Context tradeoff
Both models list 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 evidence30 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro (High)63.3%
    Source
    Interfaze Beta—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro (High)80.4%
    Source
    Interfaze Beta—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Pro (High)44.7%
    Source
    Interfaze Beta—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro (High)74.2%
    Source
    Interfaze Beta—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Pro (High)49%
    Source
    Interfaze Beta—

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro (High)89.8%
    Source
    Interfaze Beta—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro (High)2919.0
    Source
    Interfaze Beta—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro (High)79.4%
    Source
    Interfaze Beta—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro (High)54.4%
    Source
    Interfaze Beta—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Pro (High)74.1%
    Source
    Interfaze Beta—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro (High)63.3%
    Source
    Interfaze Beta—

    Not directly comparable

  • Spider 2.0-Lite

    DeepSeek V4 Pro (High)—
    Interfaze Beta52.9%
    Source

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro (High)83.3%
    Source
    Interfaze Beta—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro (High)56.5%
    Source
    Interfaze Beta—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro (High)87.1%
    Source
    Interfaze Beta—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro (High)46.2%
    Source
    Interfaze Beta—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro (High)77.7%
    Source
    Interfaze Beta—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro (High)89.1%
    Source
    Interfaze Beta89.9%
    Source

    Interfaze Beta leads this result

  • GPQA-D

    DeepSeek V4 Pro (High)89.1%
    Source
    Interfaze Beta89.9%
    Source

    Interfaze Beta leads this result

  • HLE

    DeepSeek V4 Pro (High)34.5%
    Source
    Interfaze Beta—

    Not directly comparable

  • MMMLU

    DeepSeek V4 Pro (High)—
    Interfaze Beta90.9%
    Source

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro (High)94.0%
    Source
    Interfaze Beta—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro (High)88.0%
    Source
    Interfaze Beta—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro (High)27.4%
    Source
    Interfaze Beta—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro (High)85.5%
    Source
    Interfaze Beta—

    Not directly comparable

Multimodal

  • OCRBench V2

    DeepSeek V4 Pro (High)—
    Interfaze Beta70.7%
    Source

    Not directly comparable

  • olmOCR

    DeepSeek V4 Pro (High)—
    Interfaze Beta85.7%
    Source

    Not directly comparable

  • RefCOCO (avg)

    DeepSeek V4 Pro (High)—
    Interfaze Beta82.1%
    Source

    Not directly comparable

  • MMMU-Pro

    DeepSeek V4 Pro (High)—
    Interfaze Beta71.1%
    Source

    Not directly comparable

Instruction following

  • SOB Value Acc

    DeepSeek V4 Pro (High)—
    Interfaze Beta79.5%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro (High) or Interfaze Beta?

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 (High) or Interfaze Beta?

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 Pro (High) or Interfaze Beta?

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 (High) or Interfaze Beta?

For the stated presets, chat costs $0.00087 on DeepSeek V4 Pro (High) and $0.00325 on Interfaze Beta; repository review costs $0.02436 and $0.0855; the cache-heavy agent loop costs $0.01812 and $0.365. Interfaze Beta has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, DeepSeek V4 Pro (High) or Interfaze Beta?

Both models list the same context window, 1M.

Related comparisons

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Last updated August 10, 2026

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