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Comparison · 2 models · Updated Nov 2, 2026

LightOnOCR-3 0.8B vs Muse Wonder Full

Muse Wonder Full comes out ahead, 72 to 30 on our weighted score.

  1. LightOn

    LightOnOCR-3 0.8B

    Released Oct 8, 2026New

    30/100
    • ECI—
    • Price—
    • Context262K
    Full specs Remove LightOnOCR-3 0.8B
  2. Our pick

    Meta

    Muse Wonder Full

    Released Oct 12, 2026New

    72/100
    • ECI—
    • Price—
    • Context1.05M
    Full specs Remove Muse Wonder Full

  3. Add a model

    Make it a three-way comparison.

01 — Verdict

Muse Wonder Full is our pick

Muse Wonder Full is the better all-round choice, scoring 72/100 against LightOnOCR-3 0.8B (30). It leads on inputs & features and context window. 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
  • Longest contextMuse Wonder FullMuse Wonder Full 1,048,576 · LightOnOCR-3 0.8B 262,144 tokens
  • Widest inputsMuse Wonder FullLightOnOCR-3 0.8B: Text, Images · Muse Wonder Full: Text, Images, Audio, Video
  • Self-hostingLightOnOCR-3 0.8BPublishes downloadable weights (apache-2.0)
How the score is built
MeasureWeightLightOnOCR-3 0.8BMuse Wonder Full
Inputs & features60%2580
Context window40%3761
Overall100%30/10072/100

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.

LightOnOCR-3 0.8B vs Muse Wonder Full specifications side by side
SpecificationLightOnOCR-3 0.8BLightOnMuse Wonder FullMeta
Capability
Capabilities Index (ECI)——
ECI rank——
Price per million tokens
Input——
Output——
Cached input——
Blended (3:1)——
Long-context rate——
Price source——
Limits
Context window262,144 tokens1,048,576 tokens (best)
Max output16,384 tokens—
Inputs and features
TextYesYes
ImagesYesYes
PDFsNoNo
AudioNoYes
VideoNoYes
ReasoningNoYes
Tool callingNoYes
Structured outputNoNo
Availability
WeightsOpenapache-2.0Proprietary
API model ID——
API providers1—
ReleasedOct 8, 2026Oct 12, 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.

  • LightOnOCR-3 0.8B—
  • Muse Wonder Full—
04 — Questions

Which should you choose?

Which is better: LightOnOCR-3 0.8B or Muse Wonder Full?

Muse Wonder Full is the better all-round choice, scoring 72/100 against LightOnOCR-3 0.8B (30). It leads on inputs & features and context window. 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, LightOnOCR-3 0.8B or Muse Wonder Full?

None of these models has a published per-token price.

Which scores higher on benchmarks?

There is no independent benchmark that covers both models yet. LightOnOCR-3 0.8B has not been scored yet and Muse Wonder Full has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for LightOnOCR-3 0.8B and Muse Wonder Full yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that LightOnOCR-3 0.8B does not support tool calling, which most coding agents need.

Which has the bigger context window?

Muse Wonder Full has the largest context window at 1,048,576 tokens, against 262,144 for LightOnOCR-3 0.8B. Maximum output per response: LightOnOCR-3 0.8B up to 16,384 tokens.

Which can read images, PDFs, audio or video?

LightOnOCR-3 0.8B accepts text and images; Muse Wonder Full accepts text, images, audio and video. Muse Wonder Full handles the widest range of inputs.

Are any of these open source?

LightOnOCR-3 0.8B publishes its weights (apache-2.0) and can be self-hosted; Muse Wonder Full is proprietary.

Which is newer?

Muse Wonder Full is the newest, released Oct 12, 2026. LightOnOCR-3 0.8B came out Oct 8, 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.

05 — Keep comparing

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