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BenchLM
Model A
dots3-note Preview

Dots Studio

64.41/100

Estimated · Public rank #42

90% interval 54.5–74.3

dots3-note Preview vs LLaDA2.2-flash

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

InclusionAI logo
Model B
LLaDA2.2-flash

InclusionAI

—

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.

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

    LLaDA2.2-flash 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

    LLaDA2.2-flash 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

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. LLaDA2.2-flash does not fit this workload in one request. dots3-note Preview has no comparable published API token rate. LLaDA2.2-flash 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
4
dots3-note Preview only
27
LLaDA2.2-flash only
4
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
dots3-note Preview
59.3
Estimated · #25/152
LLaDA2.2-flash
Not ranked
Basis
BenchAlign lane · 9 vs 5 public rows
Reading
Not comparable

Coding

Not comparable
dots3-note Preview
57.2
Estimated · #34/151
LLaDA2.2-flash
Not ranked
Basis
BenchAlign lane · 7 vs 3 public rows
Reading
Not comparable

Reasoning

Not comparable
dots3-note Preview
68.3
Unranked · 1 rankable row
LLaDA2.2-flash
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Knowledge

Not comparable
dots3-note Preview
57.9
Estimated · #42/183
LLaDA2.2-flash
Not ranked
Basis
BenchAlign lane · 1 vs 0 public rows
Reading
Not comparable

Math

Not comparable
dots3-note Preview
Not ranked
LLaDA2.2-flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
dots3-note Preview
Not ranked
LLaDA2.2-flash
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
dots3-note Preview
66.1
Unranked · 10 rankable rows
LLaDA2.2-flash
Not ranked
Basis
Provisional lane · 1 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
dots3-note Preview
86.3
#38/123
LLaDA2.2-flash
Not ranked
Basis
Provisional lane · 1 vs 0 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

dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
LLaDA2.2-flash
Self-hosted; infrastructure cost varies
Fits in one request

dots3-note Preview has no comparable published API token rate. LLaDA2.2-flash has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
LLaDA2.2-flash
Self-hosted; infrastructure cost varies
Fits in one request

dots3-note Preview has no comparable published API token rate. LLaDA2.2-flash has no comparable published API token rate.

Cache-heavy agent loop

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

dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request
Cached-input rate unavailable
LLaDA2.2-flash
Self-hosted; infrastructure cost varies
Does not fit in one request
Cached-input rate unavailable

LLaDA2.2-flash does not fit this workload in one request. dots3-note Preview has no comparable published API token rate. LLaDA2.2-flash 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

dots3-note Preview

Not sourced

LLaDA2.2-flash

Not sourced

Documented outputs

dots3-note Preview

Not sourced

LLaDA2.2-flash

Not sourced

Provider availability

dots3-note Preview

Not sourced

LLaDA2.2-flash

Not sourced

Reasoning profile

dots3-note Preview

Reasoning

LLaDA2.2-flash

Reasoning

Weight access

dots3-note Preview

Open Weight

LLaDA2.2-flash

Open Weight

License

dots3-note Preview

Open Weight

LLaDA2.2-flash

Open Weight

Release date

dots3-note Preview

2026-08-14

LLaDA2.2-flash

2026-07-16

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 evidence35 rows

Agentic

  • Claw-Eval

    dots3-note Preview73.4%
    Source
    LLaDA2.2-flash64.2%
    Source

    dots3-note Preview leads this result

  • Terminal-Bench 2.1

    dots3-note Preview75.1%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • Toolathlon-Verified

    dots3-note Preview55.6%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • skillsBench

    dots3-note Preview52.8%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • APEX-Agents

    dots3-note Preview30.8%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • BrowseComp

    dots3-note Preview83.3%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • HLE w/ tools

    dots3-note Preview52.6%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • DeepSearchQA

    dots3-note Preview92.1%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • WideResearch

    dots3-note Preview78.9%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • τ²-bench results

    dots3-note Preview—
    LLaDA2.2-flash80.3%
    Source

    Not directly comparable

  • PinchBench

    dots3-note Preview—
    LLaDA2.2-flash81.7%
    Source

    Not directly comparable

  • MCP Atlas

    dots3-note Preview—
    LLaDA2.2-flash46.2%
    Source

    Not directly comparable

  • BFCL v4

    dots3-note Preview—
    LLaDA2.2-flash60.8%
    Source

    Not directly comparable

Coding

  • Codeforces

    dots3-note Preview3056.0
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • LiveCodeBench v6

    dots3-note Preview91.5%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • Terminal-Bench 2.1

    dots3-note Preview75.1%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • SWE-bench Verified

    dots3-note Preview78.4%
    Source
    LLaDA2.2-flash49.3%
    Source

    dots3-note Preview leads this result

  • SWE Multilingual

    dots3-note Preview75.7%
    Source
    LLaDA2.2-flash25%
    Source

    dots3-note Preview leads this result

  • SWE-bench Pro

    dots3-note Preview61%
    Source
    LLaDA2.2-flash30.1%
    Source

    dots3-note Preview leads this result

  • NL2Repo

    dots3-note Preview49.8%
    Source
    LLaDA2.2-flash—

    Not directly comparable

Reasoning

  • ARC-AGI-2

    dots3-note Preview81.4%
    Source
    LLaDA2.2-flash—

    Not directly comparable

Knowledge

  • HLE

    dots3-note Preview52.6%
    Source
    LLaDA2.2-flash—

    Not directly comparable

Math

  • IMOAnswerBench

    dots3-note Preview90.9%
    Source
    LLaDA2.2-flash—

    Not directly comparable

Multimodal

  • SimpleVQA

    dots3-note Preview72.5%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • MMMU-Pro

    dots3-note Preview79.1%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • MathVision

    dots3-note Preview87.7%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • ZeroBench

    dots3-note Preview19.0%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • CharXiv w/o tools

    dots3-note Preview83.1%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • GDP.pdf (no tools)

    dots3-note Preview60.7%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • PerceptionBench

    dots3-note Preview53.4%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • BabyVision

    dots3-note Preview50.0%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • MMVU

    dots3-note Preview79.9%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • VideoMMMU

    dots3-note Preview86.8%
    Source
    LLaDA2.2-flash—

    Not directly comparable

Instruction following

  • IFBench

    dots3-note Preview80.4%
    Source
    LLaDA2.2-flash—

    Not directly comparable

  • IFEval

    dots3-note Preview93.9%
    Source
    LLaDA2.2-flash—

    Not directly comparable

Frequently asked questions

Which is better, dots3-note Preview or LLaDA2.2-flash?

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, dots3-note Preview or LLaDA2.2-flash?

LLaDA2.2-flash is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, dots3-note Preview or LLaDA2.2-flash?

LLaDA2.2-flash is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, dots3-note Preview or LLaDA2.2-flash?

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, dots3-note Preview or LLaDA2.2-flash?

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 September 10, 2026

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