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Model comparison

DeepSeek V4 Pro (Max) vs Hy-MT1.5-1.8B-1.25bit

Updated August 3, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

DeepSeek V4 Pro (Max)

DeepSeek

58.8/100

Estimated · Public rank #60

90% interval 48.9–68.7

Hy-MT1.5-1.8B-1.25bit

Tencent Hunyuan

—

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.

  • Long documents

    Prompts that approach the documented context limit

    DeepSeek V4 Pro (Max)

    DeepSeek V4 Pro (Max) 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

    A complete comparable API-rate estimate is not available for both models.

    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
0
DeepSeek V4 Pro (Max) only
24
Hy-MT1.5-1.8B-1.25bit only
0
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 Pro (Max)
74.5
Hy-MT1.5-1.8B-1.25bit
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V4 Pro (Max)
70.9
Hy-MT1.5-1.8B-1.25bit
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V4 Pro (Max)
Not measured
Hy-MT1.5-1.8B-1.25bit
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V4 Pro (Max)
60.1
Hy-MT1.5-1.8B-1.25bit
Not measured
Weighted basis
4 vs 0 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V4 Pro (Max)
95.2
Hy-MT1.5-1.8B-1.25bit
Not measured
Weighted basis
1 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V4 Pro (Max)
Not measured
Hy-MT1.5-1.8B-1.25bit
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V4 Pro (Max)
Not measured
Hy-MT1.5-1.8B-1.25bit
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V4 Pro (Max)
Not measured
Hy-MT1.5-1.8B-1.25bit
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 Pro (Max)
$0.00087
Fits in one request
Hy-MT1.5-1.8B-1.25bit
API rate not published
Fits in one request

Hy-MT1.5-1.8B-1.25bit has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V4 Pro (Max)
$0.02436
Fits in one request
Hy-MT1.5-1.8B-1.25bit
API rate not published
Fits in one request

Hy-MT1.5-1.8B-1.25bit has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V4 Pro (Max)
$0.01812
Fits in one request
Hy-MT1.5-1.8B-1.25bit
API rate not published
Fits in one request
Cached-input rate unavailable

Hy-MT1.5-1.8B-1.25bit 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.

DeepSeek V4 Pro (Max)

1M

DeepSeek models and pricing

Hy-MT1.5-1.8B-1.25bit

262K

Cached-input rate

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

DeepSeek V4 Pro (Max)

$0.003625 per 1M cached input tokens

Hy-MT1.5-1.8B-1.25bit

No comparable hosted API rate

Reasoning profile

DeepSeek V4 Pro (Max)

Reasoning

Hy-MT1.5-1.8B-1.25bit

Non-Reasoning

Weight access

DeepSeek V4 Pro (Max)

Open Weight

Hy-MT1.5-1.8B-1.25bit

Open Weight

License

DeepSeek V4 Pro (Max)

Open Weight

Hy-MT1.5-1.8B-1.25bit

Open Weight

Release date

DeepSeek V4 Pro (Max)

2026-04-24

Hy-MT1.5-1.8B-1.25bit

2026-04-29

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
DeepSeek V4 Pro (Max) 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 evidence24 rows

Agentic

  • Terminal-Bench 2.0

    DeepSeek V4 Pro (Max)67.9%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • BrowseComp

    DeepSeek V4 Pro (Max)83.4%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • HLE w/ tools

    DeepSeek V4 Pro (Max)48.2%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • MCP Atlas

    DeepSeek V4 Pro (Max)73.6%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • Toolathlon

    DeepSeek V4 Pro (Max)51.8%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

Coding

  • LiveCodeBench Pass@1-COT

    DeepSeek V4 Pro (Max)93.5%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • Codeforces

    DeepSeek V4 Pro (Max)3206.0
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V4 Pro (Max)80.6%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V4 Pro (Max)55.4%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • SWE Multilingual

    DeepSeek V4 Pro (Max)76.2%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • Terminal-Bench 2.0

    DeepSeek V4 Pro (Max)67.9%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • Vibe Code Bench

    DeepSeek V4 Pro (Max)49.93%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

Reasoning

  • MRCR 1M

    DeepSeek V4 Pro (Max)83.5%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • CorpusQA 1M

    DeepSeek V4 Pro (Max)62.0%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

Knowledge

  • MMLU-Pro

    DeepSeek V4 Pro (Max)87.5%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • SimpleQA

    DeepSeek V4 Pro (Max)57.9%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • Chinese-SimpleQA

    DeepSeek V4 Pro (Max)84.4%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • GPQA

    DeepSeek V4 Pro (Max)90.1%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • GPQA-D

    DeepSeek V4 Pro (Max)90.1%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • HLE

    DeepSeek V4 Pro (Max)37.7%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

Math

  • HMMT Feb 2026

    DeepSeek V4 Pro (Max)95.2%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V4 Pro (Max)89.8%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • Apex

    DeepSeek V4 Pro (Max)38.3%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

  • Apex Shortlist

    DeepSeek V4 Pro (Max)90.2%
    Source
    Hy-MT1.5-1.8B-1.25bit—

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V4 Pro (Max) or Hy-MT1.5-1.8B-1.25bit?

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, DeepSeek V4 Pro (Max) or Hy-MT1.5-1.8B-1.25bit?

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 (Max) or Hy-MT1.5-1.8B-1.25bit?

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 (Max) or Hy-MT1.5-1.8B-1.25bit?

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 (Max) or Hy-MT1.5-1.8B-1.25bit?

DeepSeek V4 Pro (Max) has the larger documented context window: 1M, compared with 262K.

Related comparisons

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Last updated August 3, 2026

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