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BenchLM
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Model A
Agents-A1-4B

InternScience

—

Evidence status unavailable

90% interval unavailable

Agents-A1-4B vs dots3-note Preview

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

Model B
dots3-note Preview

Dots Studio

64.41/100

Estimated · Public rank #42

90% interval 54.5–74.3

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.

4 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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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

    Agents-A1-4B is not ranked on the public lane for coding, so no winner is named for coding.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    Agents-A1-4B is not ranked on the public lane for agentic, so no winner is named for agentic.

    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
4
Agents-A1-4B only
7
dots3-note Preview only
27
Like-for-like categories
0 / 8

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Agentic

Not comparable
Agents-A1-4B
Not ranked
dots3-note Preview
59.3
Estimated · #25/152
Basis
BenchAlign lane · 5 vs 9 public rows
Reading
Not comparable

Coding

Not comparable
Agents-A1-4B
Not ranked
dots3-note Preview
57.2
Estimated · #34/151
Basis
BenchAlign lane · 2 vs 7 public rows
Reading
Not comparable

Reasoning

Not comparable
Agents-A1-4B
Not ranked
dots3-note Preview
68.3
Unranked · 1 rankable row
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Agents-A1-4B
Not ranked
dots3-note Preview
57.9
Estimated · #42/183
Basis
BenchAlign lane · 1 vs 1 public rows
Reading
Not comparable

Math

Not comparable
Agents-A1-4B
Not ranked
dots3-note Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Agents-A1-4B
Not ranked
dots3-note Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Agents-A1-4B
Not ranked
dots3-note Preview
66.1
Unranked · 10 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Agents-A1-4B
Not ranked
dots3-note Preview
86.3
#38/123
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

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

Agents-A1-4B
Self-hosted; infrastructure cost varies
Fits in one request
dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request

Agents-A1-4B has no comparable published API token rate. dots3-note Preview has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Agents-A1-4B
Self-hosted; infrastructure cost varies
Fits in one request
dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request

Agents-A1-4B has no comparable published API token rate. dots3-note Preview has no comparable published API token rate.

Cache-heavy agent loop

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

Agents-A1-4B
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable

Agents-A1-4B has no comparable published API token rate. 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.

Documented inputs

Agents-A1-4B

Not sourced

dots3-note Preview

Not sourced

Documented outputs

Agents-A1-4B

Not sourced

dots3-note Preview

Not sourced

Provider availability

Agents-A1-4B

Not sourced

dots3-note Preview

Not sourced

Reasoning profile

Agents-A1-4B

Reasoning

dots3-note Preview

Reasoning

Weight access

Agents-A1-4B

Open Weight

dots3-note Preview

Open Weight

License

Agents-A1-4B

Open Weight

dots3-note Preview

Open Weight

Release date

Agents-A1-4B

2026-07-13

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 evidence38 rows

Agentic

  • BrowseComp

    Agents-A1-4B66.8%
    Source
    dots3-note Preview83.3%
    Source

    dots3-note Preview leads this result

  • GAIA

    Agents-A1-4B95.1%
    Source
    dots3-note Preview—

    Not directly comparable

  • MLE-Bench Lite

    Agents-A1-4B22.7%
    Source
    dots3-note Preview—

    Not directly comparable

  • τ²-bench results

    Agents-A1-4B78.2%
    Source
    dots3-note Preview—

    Not directly comparable

  • VITA-Bench

    Agents-A1-4B40.3%
    Source
    dots3-note Preview—

    Not directly comparable

  • Claw-Eval

    Agents-A1-4B—
    dots3-note Preview73.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Agents-A1-4B—
    dots3-note Preview75.1%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Agents-A1-4B—
    dots3-note Preview55.6%
    Source

    Not directly comparable

  • skillsBench

    Agents-A1-4B—
    dots3-note Preview52.8%
    Source

    Not directly comparable

  • APEX-Agents

    Agents-A1-4B—
    dots3-note Preview30.8%
    Source

    Not directly comparable

  • HLE w/ tools

    Agents-A1-4B—
    dots3-note Preview52.6%
    Source

    Not directly comparable

  • DeepSearchQA

    Agents-A1-4B—
    dots3-note Preview92.1%
    Source

    Not directly comparable

  • WideResearch

    Agents-A1-4B—
    dots3-note Preview78.9%
    Source

    Not directly comparable

Coding

  • SciCode

    Agents-A1-4B29.6%
    Source
    dots3-note Preview—

    Not directly comparable

  • LiveCodeBench v6

    Agents-A1-4B59.6%
    Source
    dots3-note Preview91.5%
    Source

    dots3-note Preview leads this result

  • Codeforces

    Agents-A1-4B—
    dots3-note Preview3056.0
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Agents-A1-4B—
    dots3-note Preview75.1%
    Source

    Not directly comparable

  • SWE-bench Verified

    Agents-A1-4B—
    dots3-note Preview78.4%
    Source

    Not directly comparable

  • SWE Multilingual

    Agents-A1-4B—
    dots3-note Preview75.7%
    Source

    Not directly comparable

  • SWE-bench Pro

    Agents-A1-4B—
    dots3-note Preview61%
    Source

    Not directly comparable

  • NL2Repo

    Agents-A1-4B—
    dots3-note Preview49.8%
    Source

    Not directly comparable

Reasoning

  • LongBench v2

    Agents-A1-4B52.1%
    Source
    dots3-note Preview—

    Not directly comparable

  • ARC-AGI-2

    Agents-A1-4B—
    dots3-note Preview81.4%
    Source

    Not directly comparable

Knowledge

  • FrontierScience Research

    Agents-A1-4B33.3%
    Source
    dots3-note Preview—

    Not directly comparable

  • HLE

    Agents-A1-4B—
    dots3-note Preview52.6%
    Source

    Not directly comparable

Math

  • IMOAnswerBench

    Agents-A1-4B—
    dots3-note Preview90.9%
    Source

    Not directly comparable

Multimodal

  • SimpleVQA

    Agents-A1-4B—
    dots3-note Preview72.5%
    Source

    Not directly comparable

  • MMMU-Pro

    Agents-A1-4B—
    dots3-note Preview79.1%
    Source

    Not directly comparable

  • MathVision

    Agents-A1-4B—
    dots3-note Preview87.7%
    Source

    Not directly comparable

  • ZeroBench

    Agents-A1-4B—
    dots3-note Preview19.0%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Agents-A1-4B—
    dots3-note Preview83.1%
    Source

    Not directly comparable

  • GDP.pdf (no tools)

    Agents-A1-4B—
    dots3-note Preview60.7%
    Source

    Not directly comparable

  • PerceptionBench

    Agents-A1-4B—
    dots3-note Preview53.4%
    Source

    Not directly comparable

  • BabyVision

    Agents-A1-4B—
    dots3-note Preview50.0%
    Source

    Not directly comparable

  • MMVU

    Agents-A1-4B—
    dots3-note Preview79.9%
    Source

    Not directly comparable

  • VideoMMMU

    Agents-A1-4B—
    dots3-note Preview86.8%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Agents-A1-4B69.1%
    Source
    dots3-note Preview80.4%
    Source

    dots3-note Preview leads this result

  • IFEval

    Agents-A1-4B94.8%
    Source
    dots3-note Preview93.9%
    Source

    Agents-A1-4B leads this result

Frequently asked questions

Which is better, Agents-A1-4B 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, Agents-A1-4B or dots3-note Preview?

Agents-A1-4B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Agents-A1-4B or dots3-note Preview?

Agents-A1-4B is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Agents-A1-4B 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, Agents-A1-4B or dots3-note Preview?

dots3-note Preview has the larger documented context window: 512K, compared with 262K.

Related comparisons

Compare API pricing Read the methodology Open the model selector

Last updated September 10, 2026

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