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Data

Ling 3.0 Flash vs Nemotron 3.5 Super VL 120B A12B

Updated October 15, 2026. Rank says Nemotron 3.5 Super VL 120B A12B is ahead. Price, access, and your workload can each overturn that. Public scores include evidence status and uncertainty.

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Decision reading

Nemotron 3.5 Super VL 120B A12B has the higher public point estimate, 49.51 versus 45.16. Their conditional score ranges overlap. These ranges do not establish rank confidence. 6 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

Model A
InclusionAI logo
Ling 3.0 Flash

InclusionAI

45.16/100

Estimated · Public rank #118

Conditional range 30.8–59.5

Model B
NVIDIA logo
Nemotron 3.5 Super VL 120B A12B

NVIDIA

49.51/100

Estimated · Public rank #97

Conditional range 35.2–63.9

Shared results
6
Ling 3.0 Flash only
18
Nemotron 3.5 Super VL 120B A12B only
13
Like-for-like categories
1 / 8
Estimated: Ling 3.0 Flash and Nemotron 3.5 Super VL 120B A12B. Conditional ranges do not establish rank confidence.How 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

    Nemotron 3.5 Super VL 120B A12B

    Nemotron 3.5 Super VL 120B A12B 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

    Nemotron 3.5 Super VL 120B A12B 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

    Nemotron 3.5 Super VL 120B A12B is 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.

31.7Ling 3.0 Flash—Nemotron 3.5 Super VL 120B A12B

Not comparable · BenchAlign v5.8

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.

2 categories rest 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.

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.

Bars run 0–100 on each benchmark’s normalized display scale

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign v5.8 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.

Instruction following

Like-for-like
Ling 3.0 Flash
75.2
#65/146
Nemotron 3.5 Super VL 120B A12B
66.6
#72/146
Basis
Provisional lane · 1 vs 1 weighted rows
Reading
Ling 3.0 Flash leads

Agentic

Directional only
Ling 3.0 Flash
33.6
Supported · #83/127
Nemotron 3.5 Super VL 120B A12B
44.2
Estimated · #61/127
Basis
BenchAlign v5.8 lane · 9 vs 4 public rows
Reading
Directional only

Knowledge

Directional only
Ling 3.0 Flash
43.6
Supported · #105/184
Nemotron 3.5 Super VL 120B A12B
51.8
Estimated · #73/184
Basis
BenchAlign v5.8 lane · 5 vs 3 public rows
Reading
Directional only

Coding

Not comparable
Ling 3.0 Flash
31.7
Supported · #89/150
Nemotron 3.5 Super VL 120B A12B
Not ranked
Basis
BenchAlign v5.8 lane · 6 vs 4 public rows
Reading
Not comparable

Reasoning

Not comparable
Ling 3.0 Flash
76.0
Unranked · 2 rankable rows
Nemotron 3.5 Super VL 120B A12B
73.0
Unranked · 2 rankable rows
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Ling 3.0 Flash
Not ranked
Nemotron 3.5 Super VL 120B A12B
62.0
Unranked · 5 rankable rows
Basis
Provisional lane · 0 vs 1 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Ling 3.0 Flash
Not ranked
Nemotron 3.5 Super VL 120B A12B
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Ling 3.0 Flash
81.1
Unranked · 3 rankable rows
Nemotron 3.5 Super VL 120B A12B
Not ranked
Basis
Provisional lane · 2 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 v5.8) 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.

Supported evidence per lane · 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

Ling 3.0 Flash
API rate not published
Fits in one request
Nemotron 3.5 Super VL 120B A12B
API rate not published
Fits in one request

Ling 3.0 Flash has no comparable published API token rate. Nemotron 3.5 Super VL 120B A12B has no comparable published API token rate.

Repository review

50K fresh input + 3K output tokens

Ling 3.0 Flash
API rate not published
Fits in one request
Nemotron 3.5 Super VL 120B A12B
API rate not published
Fits in one request

Ling 3.0 Flash has no comparable published API token rate. Nemotron 3.5 Super VL 120B A12B has no comparable published API token rate.

Cache-heavy agent loop

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

Ling 3.0 Flash
API rate not published
Fits in one request
Cached-input rate unavailable
Nemotron 3.5 Super VL 120B A12B
API rate not published
Fits in one request
Cached-input rate unavailable

Ling 3.0 Flash has no comparable published API token rate. Nemotron 3.5 Super VL 120B A12B 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.

API model ID

Ling 3.0 Flash

Not sourced

Nemotron 3.5 Super VL 120B A12B

Not sourced

Documented inputs

Ling 3.0 Flash

Not sourced

Nemotron 3.5 Super VL 120B A12B

text, image, video

NVIDIA BF16 model card

Provider availability

Ling 3.0 Flash

Not sourced

Nemotron 3.5 Super VL 120B A12B

Open Weights · Hugging Face, self-hosted inference

NVIDIA BF16 model card

Reasoning profile

Ling 3.0 Flash

Reasoning

Nemotron 3.5 Super VL 120B A12B

Reasoning

Weight access

Ling 3.0 Flash

Open Weight

Nemotron 3.5 Super VL 120B A12B

Open Weight

License

Ling 3.0 Flash

Open Weight

Nemotron 3.5 Super VL 120B A12B

Open Weight

Release date

Ling 3.0 Flash

2026-07-23

Nemotron 3.5 Super VL 120B A12B

2026-10-15

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
Nemotron 3.5 Super VL 120B A12B has the higher public point estimate, 49.51 versus 45.16. Their conditional score ranges overlap. These ranges do not establish rank confidence.
Workload cost
A complete comparable API-rate estimate is not available for both models.
Context tradeoff
Nemotron 3.5 Super VL 120B A12B has the larger documented window (1M).
Run the same representative tasks against both endpoints before changing production traffic.

Questions

Which is better, Ling 3.0 Flash or Nemotron 3.5 Super VL 120B A12B?

Nemotron 3.5 Super VL 120B A12B has the higher public point estimate, 49.51 versus 45.16. Their conditional score ranges overlap. These ranges do not establish rank confidence. The page therefore keeps the decision tied to the specific documented workload.

Which is better for coding, Ling 3.0 Flash or Nemotron 3.5 Super VL 120B A12B?

Nemotron 3.5 Super VL 120B A12B is not ranked on the public lane for coding, so no winner is named for coding.

Which is better for agentic tasks, Ling 3.0 Flash or Nemotron 3.5 Super VL 120B A12B?

Nemotron 3.5 Super VL 120B A12B scores higher for agentic tasks on the public lane, 44.2 to 33.6. Nemotron 3.5 Super VL 120B A12B is 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, Ling 3.0 Flash or Nemotron 3.5 Super VL 120B A12B?

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, Ling 3.0 Flash or Nemotron 3.5 Super VL 120B A12B?

Nemotron 3.5 Super VL 120B A12B has the larger documented context window: 1M, 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 evidence37 rows

Agentic

  • MCP Atlas

    Ling 3.0 Flash65.5%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • skillsBench

    Ling 3.0 Flash44.8%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • BFCL v4

    Ling 3.0 Flash73.0%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • WideResearch

    Ling 3.0 Flash73.6%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • BrowseComp

    Ling 3.0 Flash72.2%
    Source
    Nemotron 3.5 Super VL 120B A12B78%
    Source

    Nemotron 3.5 Super VL 120B A12B leads this result

  • DRACO

    Ling 3.0 Flash70.4%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • Terminal-Bench 2.1 (Vals)

    Ling 3.0 Flash50.2%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • Hermes Index

    Ling 3.0 Flash21.6%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • Hermes Bench

    Ling 3.0 Flash53.8%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • Terminal-Bench 2.1

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B66.7%
    Source

    Not directly comparable

  • τ²-bench Airline

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B76.5%
    Source

    Not directly comparable

  • τ³-bench results

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B18.9%
    Source

    Not directly comparable

Coding

  • SWE-bench Pro

    Ling 3.0 Flash56.6%
    Source
    Nemotron 3.5 Super VL 120B A12B51.8%
    Source

    Ling 3.0 Flash leads this result

  • SWE Multilingual

    Ling 3.0 Flash72.4%
    Source
    Nemotron 3.5 Super VL 120B A12B67.7%
    Source

    Ling 3.0 Flash leads this result

  • LiveCodeBench v5

    Ling 3.0 Flash82.8%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • SciCode

    Ling 3.0 Flash41.2%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • LiveCodeBench (Vals)

    Ling 3.0 Flash84.0%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • SWE-bench (Vals)

    Ling 3.0 Flash65.2%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • LiveCodeBench v6

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B88.2%
    Source

    Not directly comparable

  • SWE-bench Verified

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B71.1%
    Source

    Not directly comparable

Reasoning

  • CritPt

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B10.9%
    Source

    Not directly comparable

Multimodal

  • BabyVision

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B29.6%
    Source

    Not directly comparable

  • MMLongBench-Doc

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B62.8%
    Source

    Not directly comparable

  • V*

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B85.6%
    Source

    Not directly comparable

  • MathVision

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B78.2%
    Source

    Not directly comparable

  • CharXiv

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B80.8%
    Source

    Not directly comparable

Knowledge

  • GPQA

    Ling 3.0 Flash85.0%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • GPQA-D

    Ling 3.0 Flash85.0%
    Source
    Nemotron 3.5 Super VL 120B A12B89.5%
    Source

    Nemotron 3.5 Super VL 120B A12B leads this result

  • HLE

    Ling 3.0 Flash22.7%
    Source
    Nemotron 3.5 Super VL 120B A12B29.2%
    Source

    Nemotron 3.5 Super VL 120B A12B leads this result

  • GPQA Diamond (Vals)

    Ling 3.0 Flash84.8%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • MMLU-Pro (Vals)

    Ling 3.0 Flash82.0%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • HLE w/o tools

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B31.8%
    Source

    Not directly comparable

Instruction following

  • IFBench

    Ling 3.0 Flash74.5%
    Source
    Nemotron 3.5 Super VL 120B A12B70.9%
    Source

    Ling 3.0 Flash leads this result

Math

  • AIME26

    Ling 3.0 Flash93.2%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • HMMT Feb 2026

    Ling 3.0 Flash87.0%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • IMOAnswerBench

    Ling 3.0 Flash83.7%
    Source
    Nemotron 3.5 Super VL 120B A12B—

    Not directly comparable

  • Apex Shortlist

    Ling 3.0 Flash—
    Nemotron 3.5 Super VL 120B A12B88.4%
    Source

    Not directly comparable

37 public results · 6 shared

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Last updated October 15, 2026