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Comparison · 3 models · Updated Nov 2, 2026

Granite 4.2 8B vs Nemotron 3.5 Super VL 120B A12B vs Ling 3.0 Flash VL

Ling 3.0 Flash VL comes out ahead, 76 to 71 and 64 on our weighted score, though Granite 4.2 8B is 8% cheaper per token.

  1. IBM

    Granite 4.2 8B

    Released Aug 25, 2026

    64/100
    • ECI—
    • Price$0.06 / $0.15
    • Context131K
  2. NVIDIA

    Nemotron 3.5 Super VL 120B A12B

    Released Oct 15, 2026New

    71/100
    • ECI—
    • Price$0.15 / $0.50
    • Context1.05M
  3. Our pick

    inclusionAI

    Ling 3.0 Flash VL

    Released Sep 10, 2026New

    76/100
    • ECI—
    • Price$0.06 / $0.18
    • Context262K
01 — Verdict

Ling 3.0 Flash VL is our pick

Ling 3.0 Flash VL is the better all-round choice, scoring 76/100 against Nemotron 3.5 Super VL 120B A12B (71) and Granite 4.2 8B (64). Granite 4.2 8B wins on price. Nemotron 3.5 Super VL 120B A12B wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

  • CapabilityNot enough dataNo independent benchmark covers every model here yet
  • Lowest priceGranite 4.2 8BGranite 4.2 8B $0.083 · Ling 3.0 Flash VL $0.09 · Nemotron 3.5 Super VL 120B A12B $0.237 per 1M tokens (3:1 blend)
  • Longest contextNemotron 3.5 Super VL 120B A12BNemotron 3.5 Super VL 120B A12B 1,048,576 · Ling 3.0 Flash VL 262,144 · Granite 4.2 8B 131,072 tokens
  • Widest inputsNemotron 3.5 Super VL 120B A12B and Ling 3.0 Flash VLGranite 4.2 8B: Text · Nemotron 3.5 Super VL 120B A12B: Text, Images, Video · Ling 3.0 Flash VL: Text, Images, Video
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightGranite 4.2 8BNemotron 3.5 Super VL 120B A12BLing 3.0 Flash VL
Price50%977695
Inputs & features30%357070
Context window20%256137
Overall100%64/10071/10076/100

Left out because at least one model lacks the data: capability. The remaining weights were rescaled.

02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Granite 4.2 8B vs Nemotron 3.5 Super VL 120B A12B vs Ling 3.0 Flash VL specifications side by side
SpecificationGranite 4.2 8BIBMNemotron 3.5 Super VL 120B A12BNVIDIALing 3.0 Flash VLinclusionAI
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.06 (best)$0.15$0.06 (best)
Output$0.15 (best)$0.50$0.18
Cached input———
Blended (3:1)$0.083 (best)$0.237$0.09
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 7 providersMedian of 11 providersMedian of 7 providers
Limits
Context window131,072 tokens1,048,576 tokens (best)262,144 tokens
Max output117,964 tokens (best)8,192 tokens32,768 tokens
Inputs and features
TextYesYesYes
ImagesNoYesYes
PDFsNoNoNo
AudioNoNoNo
VideoNoYesYes
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpenMIT
API model ID—nvidia/nemotron-3.5-super-vl-120b-a12b—
API providers713 (best)7
ReleasedAug 25, 2026Oct 15, 2026Sep 10, 2026
Knowledge cutoff———
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Granite 4.2 8B$0.90
  • Nemotron 3.5 Super VL 120B A12B$2.50
  • Ling 3.0 Flash VL$0.96
04 — Questions

Which should you choose?

Which is better: Granite 4.2 8B, Nemotron 3.5 Super VL 120B A12B or Ling 3.0 Flash VL?

Ling 3.0 Flash VL is the better all-round choice, scoring 76/100 against Nemotron 3.5 Super VL 120B A12B (71) and Granite 4.2 8B (64). Granite 4.2 8B wins on price. Nemotron 3.5 Super VL 120B A12B wins on context window. The score weighs price 50%, inputs & features 30%, context window 20%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Which is cheaper, Granite 4.2 8B, Nemotron 3.5 Super VL 120B A12B or Ling 3.0 Flash VL?

Granite 4.2 8B is cheaper at $0.06 input / $0.15 output per million tokens (median across 7 API providers). Ling 3.0 Flash VL costs $0.06 input / $0.18 output per million tokens (median across 7 API providers); Nemotron 3.5 Super VL 120B A12B costs $0.15 input / $0.50 output per million tokens (median across 11 API providers; free on Nvidia). At a typical mix of three input tokens to one output token, that is $0.083 per million tokens for Granite 4.2 8B versus $0.09 for Ling 3.0 Flash VL (1.1× as much) and $0.237 for Nemotron 3.5 Super VL 120B A12B (2.9× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Granite 4.2 8B has not been scored yet, Nemotron 3.5 Super VL 120B A12B has not been scored yet and Ling 3.0 Flash VL has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Granite 4.2 8B, Nemotron 3.5 Super VL 120B A12B and Ling 3.0 Flash VL yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. All three support tool calling for agent workflows.

Which has the bigger context window?

Nemotron 3.5 Super VL 120B A12B has the largest context window at 1,048,576 tokens, against 262,144 for Ling 3.0 Flash VL and 131,072 for Granite 4.2 8B. Maximum output per response: Granite 4.2 8B up to 117,964, Nemotron 3.5 Super VL 120B A12B up to 8,192, Ling 3.0 Flash VL up to 32,768 tokens.

Which can read images, PDFs, audio or video?

Granite 4.2 8B accepts text; Nemotron 3.5 Super VL 120B A12B accepts text, images and video; Ling 3.0 Flash VL accepts text, images and video. Nemotron 3.5 Super VL 120B A12B handles the widest range of inputs.

Are any of these open source?

Yes, all three publish their weights (MIT), so you can self-host them.

Which is newer?

Nemotron 3.5 Super VL 120B A12B is the newest, released Oct 15, 2026. Ling 3.0 Flash VL came out Sep 10, 2026; Granite 4.2 8B came out Aug 25, 2026.

How do you decide the winner?

Each model gets a 0–100 score on capability (50%, independent benchmark results); price (25%, blended price per million tokens (3 input : 1 output), log scale); inputs & features (15%, image, PDF, audio and video input, tool calling, structured output and reasoning); context window (10%, maximum tokens per request, log scale). Dimensions missing for any model are dropped and the remaining weights rescaled, so every model is judged on the same evidence. Specs and prices come from public model listings and the labs’ own API pages; capability scores come from independent benchmark runs. Data updated Nov 2, 2026.