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BenchLM

Agents-A1-F16-GGUF vs dots3-note Preview

Updated September 28, 2026. Rank says dots3-note Preview is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

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 resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
InternScience logo
Agents-A1-F16-GGUF

InternScience

52.33/100

Estimated · Public rank #72

90% interval 42.5–62.2

Model B
dots3-note Preview

Dots Studio

62.64/100

Estimated · Public rank #39

90% interval 52.8–72.5

Shared results
0
Agents-A1-F16-GGUF only
0
dots3-note Preview only
31
Like-for-like categories
0 / 8
Estimated: Agents-A1-F16-GGUF and dots3-note PreviewHow the comparison works

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-F16-GGUF and dots3-note Preview are 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-F16-GGUF and dots3-note Preview are scored on Estimated evidence for agentic, so the reading is directional rather than like-for-like.

    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

Which one for a specific job

Choose a job from the LLM Selector's task catalog to see the category row it rests on, under the same basis rules as the table below. A directional row stays directional; choosing a job never creates a winner.

The same task catalog as the LLM Selector. Each job names the evidence surface it rests on; nothing here adds a new score.

—Agents-A1-F16-GGUF—dots3-note Preview

Not comparable · BenchAlign v5.7

The coding row is not comparable on the public lane: at least one model is not measured or not ranked there.

Coding scores combine specific tasks and setups. Match the editor, harness, and effort to your workflow.

Open the coding leaderboardShortlist models for this job
Same basis rules as the category table below

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

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.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.7 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

Directional only
Agents-A1-F16-GGUF
43.9
Estimated · #46/117
dots3-note Preview
56.4
Estimated · #28/117
Basis
BenchAlign v5.7 lane · 0 vs 9 public rows
Reading
Directional only

Coding

Not comparable
Agents-A1-F16-GGUF
Not ranked
dots3-note Preview
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 7 public rows
Reading
Not comparable

Reasoning

Not comparable
Agents-A1-F16-GGUF
Not ranked
dots3-note Preview
72.9
Unranked · 1 rankable row
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Agents-A1-F16-GGUF
Not ranked
dots3-note Preview
67.2
Unranked · 10 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Knowledge

Not comparable
Agents-A1-F16-GGUF
Not ranked
dots3-note Preview
Not ranked
Basis
BenchAlign v5.7 lane · 0 vs 1 public rows
Reading
Not comparable

Multilingual

Not comparable
Agents-A1-F16-GGUF
Not ranked
dots3-note Preview
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Agents-A1-F16-GGUF
Not ranked
dots3-note Preview
85.2
#37/124
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Math

Not comparable
Agents-A1-F16-GGUF
Not ranked
dots3-note Preview
Not ranked
Basis
Provisional lane · 0 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 v5.7) differ from the provisional-lane categories. Unranked scores sit on the provisional lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Bars run 0–100Methodology

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-F16-GGUF
API rate not published
Fits in one request
dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request

Agents-A1-F16-GGUF 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-F16-GGUF
API rate not published
Fits in one request
dots3-note Preview
Self-hosted; infrastructure cost varies
Fits in one request

Agents-A1-F16-GGUF 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-F16-GGUF
API rate not published
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-F16-GGUF has no comparable published API token rate. dots3-note Preview has no comparable published API token rate.

Cached input falls back to the list input rate only where a cached rate is unpublished

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.

Agents-A1-F16-GGUF

262K

dots3-note Preview

512K

dots3-note Preview model card

API model ID

Agents-A1-F16-GGUF

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.

Agents-A1-F16-GGUF

No comparable hosted API rate

dots3-note Preview

No comparable hosted API rate

dots3-note Preview model card

Documented inputs

Agents-A1-F16-GGUF

Not sourced

dots3-note Preview

Not sourced

Documented outputs

Agents-A1-F16-GGUF

Not sourced

dots3-note Preview

Not sourced

Provider availability

Agents-A1-F16-GGUF

Not sourced

dots3-note Preview

Not sourced

Reasoning profile

Agents-A1-F16-GGUF

Reasoning

dots3-note Preview

Reasoning

Weight access

Agents-A1-F16-GGUF

Open Weight

dots3-note Preview

Open Weight

License

Agents-A1-F16-GGUF

Open Weight

dots3-note Preview

Open Weight

Release date

Agents-A1-F16-GGUF

2026-07-02

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
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
dots3-note Preview has the larger documented window (512K).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Agents-A1-F16-GGUF or dots3-note Preview?

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, Agents-A1-F16-GGUF or dots3-note Preview?

Agents-A1-F16-GGUF and dots3-note Preview are not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Agents-A1-F16-GGUF or dots3-note Preview?

dots3-note Preview scores higher for agentic tasks on the public lane, 56.4 to 43.9. Agents-A1-F16-GGUF and dots3-note Preview are scored on Estimated evidence for agentic tasks, so the reading is directional rather than like-for-like. BenchLM does not name a winner for agentic tasks from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which costs less, Agents-A1-F16-GGUF 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-F16-GGUF or dots3-note Preview?

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

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence31 rows

Agentic

  • Claw-Eval

    Agents-A1-F16-GGUF—
    dots3-note Preview73.4%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Agents-A1-F16-GGUF—
    dots3-note Preview75.1%
    Source

    Not directly comparable

  • Toolathlon-Verified

    Agents-A1-F16-GGUF—
    dots3-note Preview55.6%
    Source

    Not directly comparable

  • skillsBench

    Agents-A1-F16-GGUF—
    dots3-note Preview52.8%
    Source

    Not directly comparable

  • APEX-Agents

    Agents-A1-F16-GGUF—
    dots3-note Preview30.8%
    Source

    Not directly comparable

  • BrowseComp

    Agents-A1-F16-GGUF—
    dots3-note Preview83.3%
    Source

    Not directly comparable

  • HLE w/ tools

    Agents-A1-F16-GGUF—
    dots3-note Preview52.6%
    Source

    Not directly comparable

  • DeepSearchQA

    Agents-A1-F16-GGUF—
    dots3-note Preview92.1%
    Source

    Not directly comparable

  • WideResearch

    Agents-A1-F16-GGUF—
    dots3-note Preview78.9%
    Source

    Not directly comparable

Coding

  • Codeforces

    Agents-A1-F16-GGUF—
    dots3-note Preview3056.0
    Source

    Not directly comparable

  • LiveCodeBench v6

    Agents-A1-F16-GGUF—
    dots3-note Preview91.5%
    Source

    Not directly comparable

  • Terminal-Bench 2.1

    Agents-A1-F16-GGUF—
    dots3-note Preview75.1%
    Source

    Not directly comparable

  • SWE-bench Verified

    Agents-A1-F16-GGUF—
    dots3-note Preview78.4%
    Source

    Not directly comparable

  • SWE Multilingual

    Agents-A1-F16-GGUF—
    dots3-note Preview75.7%
    Source

    Not directly comparable

  • SWE-bench Pro

    Agents-A1-F16-GGUF—
    dots3-note Preview61%
    Source

    Not directly comparable

  • NL2Repo

    Agents-A1-F16-GGUF—
    dots3-note Preview49.8%
    Source

    Not directly comparable

Reasoning

  • ARC-AGI-2

    Agents-A1-F16-GGUF—
    dots3-note Preview81.4%
    Source

    Not directly comparable

Multimodal

  • SimpleVQA

    Agents-A1-F16-GGUF—
    dots3-note Preview72.5%
    Source

    Not directly comparable

  • MMMU-Pro

    Agents-A1-F16-GGUF—
    dots3-note Preview79.1%
    Source

    Not directly comparable

  • MathVision

    Agents-A1-F16-GGUF—
    dots3-note Preview87.7%
    Source

    Not directly comparable

  • ZeroBench

    Agents-A1-F16-GGUF—
    dots3-note Preview19.0%
    Source

    Not directly comparable

  • CharXiv w/o tools

    Agents-A1-F16-GGUF—
    dots3-note Preview83.1%
    Source

    Not directly comparable

  • GDP.pdf (no tools)

    Agents-A1-F16-GGUF—
    dots3-note Preview60.7%
    Source

    Not directly comparable

  • PerceptionBench

    Agents-A1-F16-GGUF—
    dots3-note Preview53.4%
    Source

    Not directly comparable

  • BabyVision

    Agents-A1-F16-GGUF—
    dots3-note Preview50.0%
    Source

    Not directly comparable

  • MMVU

    Agents-A1-F16-GGUF—
    dots3-note Preview79.9%
    Source

    Not directly comparable

  • VideoMMMU

    Agents-A1-F16-GGUF—
    dots3-note Preview86.8%
    Source

    Not directly comparable

Knowledge

  • HLE

    Agents-A1-F16-GGUF—
    dots3-note Preview52.6%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Agents-A1-F16-GGUF—
    dots3-note Preview80.4%
    Source

    Not directly comparable

  • IFEval

    Agents-A1-F16-GGUF—
    dots3-note Preview93.9%
    Source

    Not directly comparable

Math

  • IMOAnswerBench

    Agents-A1-F16-GGUF—
    dots3-note Preview90.9%
    Source

    Not directly comparable

31 public results · 0 shared

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Last updated September 28, 2026