ZMIME
Comparison · 3 models · Updated Nov 2, 2026

Ling 3.1 Flash vs Muse Wonder Full vs Nemotron 3.5 Super 120B A12B

Muse Wonder Full comes out ahead, 72 to 45 and 36 on our weighted score.

  1. inclusionAI

    Ling 3.1 Flash

    Released Sep 29, 2026New

    36/100
    • ECI—
    • Price$0.30 / $0.90
    • Context262K
  2. Our pick

    Meta

    Muse Wonder Full

    Released Oct 12, 2026New

    72/100
    • ECI—
    • Price—
    • Context1.05M
  3. NVIDIA

    Nemotron 3.5 Super 120B A12B

    Released Oct 15, 2026New

    45/100
    • ECI—
    • Price$0.20 / $0.50
    • Context1.05M
01 — Verdict

Muse Wonder Full is our pick

Muse Wonder Full is the better all-round choice, scoring 72/100 against Nemotron 3.5 Super 120B A12B (45) and Ling 3.1 Flash (36). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. 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 priceNemotron 3.5 Super 120B A12BNemotron 3.5 Super 120B A12B $0.275 · Ling 3.1 Flash $0.45 per 1M tokens (3:1 blend) · Muse Wonder Full unpriced
  • Longest contextMuse Wonder Full and Nemotron 3.5 Super 120B A12BMuse Wonder Full 1,048,576 · Nemotron 3.5 Super 120B A12B 1,048,576 · Ling 3.1 Flash 262,144 tokens
  • Widest inputsMuse Wonder FullLing 3.1 Flash: Text · Muse Wonder Full: Text, Images, Audio, Video · Nemotron 3.5 Super 120B A12B: Text
  • Self-hostingLing 3.1 Flash and Nemotron 3.5 Super 120B A12BPublishes downloadable weights
How the score is built
MeasureWeightLing 3.1 FlashMuse Wonder FullNemotron 3.5 Super 120B A12B
Inputs & features60%358035
Context window40%376161
Overall100%36/10072/10045/100

Left out because at least one model lacks the data: capability and price. 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.

Ling 3.1 Flash vs Muse Wonder Full vs Nemotron 3.5 Super 120B A12B specifications side by side
SpecificationLing 3.1 FlashinclusionAIMuse Wonder FullMetaNemotron 3.5 Super 120B A12BNVIDIA
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.30—$0.20 (best)
Output$0.90—$0.50 (best)
Cached input———
Blended (3:1)$0.45—$0.275 (best)
Long-context rateSame rate—Same rate
Price sourceMedian of 5 providers—Median of 1 providers
Limits
Context window262,144 tokens1,048,576 tokens (best)1,048,576 tokens (best)
Max output32,768 tokens—131,072 tokens (best)
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoYesNo
VideoNoYesNo
ReasoningYesYesYes
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenProprietaryOpen
API model ID———
API providers7 (best)—1
ReleasedSep 29, 2026Oct 12, 2026Oct 15, 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.

  • Ling 3.1 Flash$4.80
  • Muse Wonder Full—
  • Nemotron 3.5 Super 120B A12B$3.00
04 — Questions

Which should you choose?

Which is better: Ling 3.1 Flash, Muse Wonder Full or Nemotron 3.5 Super 120B A12B?

Muse Wonder Full is the better all-round choice, scoring 72/100 against Nemotron 3.5 Super 120B A12B (45) and Ling 3.1 Flash (36). It leads on inputs & features. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.

Which is cheaper, Ling 3.1 Flash, Muse Wonder Full or Nemotron 3.5 Super 120B A12B?

Nemotron 3.5 Super 120B A12B is cheaper at $0.20 input / $0.50 output per million tokens (median across 1 API provider). Ling 3.1 Flash costs $0.30 input / $0.90 output per million tokens (median across 5 API providers). At a typical mix of three input tokens to one output token, that is $0.275 per million tokens for Nemotron 3.5 Super 120B A12B versus $0.45 for Ling 3.1 Flash (1.6× as much). Muse Wonder Full has no published per-token price.

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Ling 3.1 Flash has not been scored yet, Muse Wonder Full has not been scored yet and Nemotron 3.5 Super 120B A12B has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Ling 3.1 Flash, Muse Wonder Full and Nemotron 3.5 Super 120B A12B 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?

Muse Wonder Full and Nemotron 3.5 Super 120B A12B have the largest context windows (1,048,576 and 1,048,576 tokens), against 262,144 for Ling 3.1 Flash. Maximum output per response: Ling 3.1 Flash up to 32,768, Nemotron 3.5 Super 120B A12B up to 131,072 tokens.

Which can read images, PDFs, audio or video?

Ling 3.1 Flash accepts text; Muse Wonder Full accepts text, images, audio and video; Nemotron 3.5 Super 120B A12B accepts text. Muse Wonder Full handles the widest range of inputs.

Are any of these open source?

Ling 3.1 Flash and Nemotron 3.5 Super 120B A12B publishes its weights and can be self-hosted; Muse Wonder Full is proprietary.

Which is newer?

Nemotron 3.5 Super 120B A12B is the newest, released Oct 15, 2026. Muse Wonder Full came out Oct 12, 2026; Ling 3.1 Flash came out Sep 29, 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.