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BenchLM
Model A
DeepSeek V3.2

DeepSeek

54.7/100

Supported · Public rank #98

90% interval 37.6–71.8

DeepSeek V3.2 vs dots3-note Preview

Updated August 14, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

Model B
dots3-note Preview

Dots Studio

—

Evidence status unavailable

90% interval unavailable

Decision reading

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

    dots3-note Preview

    dots3-note Preview 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. DeepSeek V3.2 does not fit this workload in one request. dots3-note Preview has no comparable published API token rate.

    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
1
DeepSeek V3.2 only
6
dots3-note Preview only
30
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 V3.2
Not measured
dots3-note Preview
83.3
Weighted basis
0 vs 1 rows
Reading
Not comparable

Coding

Not comparable
DeepSeek V3.2
60.9
dots3-note Preview
71.7
Weighted basis
1 vs 2 rows
Reading
Not comparable

Reasoning

Not comparable
DeepSeek V3.2
Not measured
dots3-note Preview
81.4
Weighted basis
0 vs 1 rows
Reading
Not comparable

Knowledge

Not comparable
DeepSeek V3.2
Not measured
dots3-note Preview
52.6
Weighted basis
0 vs 1 rows
Reading
Not comparable

Math

Not comparable
DeepSeek V3.2
17.1
dots3-note Preview
Not measured
Weighted basis
2 vs 0 rows
Reading
Not comparable

Multilingual

Not comparable
DeepSeek V3.2
Not measured
dots3-note Preview
Not measured
Weighted basis
0 vs 0 rows
Reading
Not comparable

Multimodal

Not comparable
DeepSeek V3.2
Not measured
dots3-note Preview
79.1
Weighted basis
0 vs 1 rows
Reading
Not comparable

Instruction following

Not comparable
DeepSeek V3.2
Not measured
dots3-note Preview
85.1
Weighted basis
0 vs 2 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 V3.2
$0.00049
Fits in one request
dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request

dots3-note Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

DeepSeek V3.2
$0.01526
Fits in one request
dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request

dots3-note Preview has no comparable published API token rate.

Cache-heavy agent loop

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

DeepSeek V3.2
$0.0154
Does not fit in one request
dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

DeepSeek V3.2 does not fit this workload in one request. dots3-note Preview 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.

API model ID

DeepSeek V3.2

Not sourced

dots3-note Preview

Not sourced

Cached-input rate

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

DeepSeek V3.2

$0.028 per 1M cached input tokens

dots3-note Preview

No comparable hosted API rate

dots3-note Preview model card

Documented inputs

DeepSeek V3.2

Not sourced

dots3-note Preview

Not sourced

Documented outputs

DeepSeek V3.2

Not sourced

dots3-note Preview

Not sourced

Provider availability

DeepSeek V3.2

Not sourced

dots3-note Preview

Not sourced

Reasoning profile

DeepSeek V3.2

Non-Reasoning

dots3-note Preview

Reasoning

Weight access

DeepSeek V3.2

Open Weight

dots3-note Preview

Open Weight

License

DeepSeek V3.2

Open Weight

dots3-note Preview

Open Weight

Release date

DeepSeek V3.2

2025-12-01

dots3-note Preview

2026-08-14

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
dots3-note Preview has the larger documented window (512K).

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 evidence37 rows

Agentic

  • Claw-Eval

    DeepSeek V3.240.2%
    Source
    dots3-note Preview73.4%
    Source

    dots3-note Preview leads this result

  • VITA-Bench

    DeepSeek V3.218.5%
    Source
    dots3-note Preview—

    Not directly comparable

  • Gert Labs

    DeepSeek V3.229.57%
    Source
    dots3-note Preview—

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V3.2—
    dots3-note Preview75.1%
    Source

    Not directly comparable

  • Toolathlon-Verified

    DeepSeek V3.2—
    dots3-note Preview55.6%
    Source

    Not directly comparable

  • skillsBench

    DeepSeek V3.2—
    dots3-note Preview52.8%
    Source

    Not directly comparable

  • APEX-Agents

    DeepSeek V3.2—
    dots3-note Preview30.8%
    Source

    Not directly comparable

  • BrowseComp

    DeepSeek V3.2—
    dots3-note Preview83.3%
    Source

    Not directly comparable

  • HLE w/ tools

    DeepSeek V3.2—
    dots3-note Preview52.6%
    Source

    Not directly comparable

  • DeepSearchQA

    DeepSeek V3.2—
    dots3-note Preview92.1%
    Source

    Not directly comparable

  • WideResearch

    DeepSeek V3.2—
    dots3-note Preview78.9%
    Source

    Not directly comparable

Coding

  • SWE-Rebench

    DeepSeek V3.260.9%
    Source
    dots3-note Preview—

    Not directly comparable

  • React Native Evals

    DeepSeek V3.271.5%
    Source
    dots3-note Preview—

    Not directly comparable

  • Codeforces

    DeepSeek V3.2—
    dots3-note Preview3056.0
    Source

    Not directly comparable

  • LiveCodeBench v6

    DeepSeek V3.2—
    dots3-note Preview91.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    DeepSeek V3.2—
    dots3-note Preview75.1%
    Source

    Not directly comparable

  • SWE-bench Verified

    DeepSeek V3.2—
    dots3-note Preview78.4%
    Source

    Not directly comparable

  • SWE Multilingual

    DeepSeek V3.2—
    dots3-note Preview75.7%
    Source

    Not directly comparable

  • SWE-bench Pro

    DeepSeek V3.2—
    dots3-note Preview61%
    Source

    Not directly comparable

  • NL2Repo

    DeepSeek V3.2—
    dots3-note Preview49.8%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    DeepSeek V3.2—
    dots3-note Preview81.4%
    Source

    Not directly comparable

Knowledge

  • HLE

    DeepSeek V3.2—
    dots3-note Preview52.6%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    DeepSeek V3.222.100%
    Source
    dots3-note Preview—

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    DeepSeek V3.22.100%
    Source
    dots3-note Preview—

    Not directly comparable

  • IMOAnswerBench

    DeepSeek V3.2—
    dots3-note Preview90.9%
    Source

    Not directly comparable

Multimodal

  • SimpleVQA

    DeepSeek V3.2—
    dots3-note Preview72.5%
    Source

    Not directly comparable

  • MMMU-Pro

    DeepSeek V3.2—
    dots3-note Preview79.1%
    Source

    Not directly comparable

  • MathVision

    DeepSeek V3.2—
    dots3-note Preview87.7%
    Source

    Not directly comparable

  • ZeroBench

    DeepSeek V3.2—
    dots3-note Preview19.0%
    Source

    Not directly comparable

  • CharXiv w/o tools

    DeepSeek V3.2—
    dots3-note Preview83.1%
    Source

    Not directly comparable

  • GDP.pdf (no tools)

    DeepSeek V3.2—
    dots3-note Preview60.7%
    Source

    Not directly comparable

  • PerceptionBench

    DeepSeek V3.2—
    dots3-note Preview53.4%
    Source

    Not directly comparable

  • BabyVision

    DeepSeek V3.2—
    dots3-note Preview50.0%
    Source

    Not directly comparable

  • MMVU

    DeepSeek V3.2—
    dots3-note Preview79.9%
    Source

    Not directly comparable

  • VideoMMMU

    DeepSeek V3.2—
    dots3-note Preview86.8%
    Source

    Not directly comparable

Instruction following

  • IFBench

    DeepSeek V3.2—
    dots3-note Preview80.4%
    Source

    Not directly comparable

  • IFEval

    DeepSeek V3.2—
    dots3-note Preview93.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, DeepSeek V3.2 or dots3-note Preview?

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 V3.2 or dots3-note Preview?

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 V3.2 or dots3-note Preview?

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 V3.2 or dots3-note Preview?

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 V3.2 or dots3-note Preview?

dots3-note Preview has the larger documented context window: 512K, compared with 128K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated August 14, 2026

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