ZMIME
Comparison · 3 models · Updated Oct 4, 2026

Aya Expanse 32B vs Llama 3.1 Nemotron Ultra 253B vs Qwen-VL OCR

Llama 3.1 Nemotron Ultra 253B comes out ahead, 65 to 36 and 33 on our weighted score, and it is the cheaper option too.

  1. Cohere

    Aya Expanse 32B

    Released Oct 24, 2024

    33/100
    • ECI—
    • Price$0.50 / $1.50
    • Context128K
  2. Our pick

    NVIDIA

    Llama 3.1 Nemotron Ultra 253B

    Released Apr 7, 2025

    65/100
    • ECI—
    • PriceFree / Free
    • Context128K
  3. Alibaba (Qwen)

    Qwen-VL OCR

    Released Oct 28, 2024

    36/100
    • ECI—
    • Price$0.72 / $0.72
    • Context34K
01 — Verdict

Llama 3.1 Nemotron Ultra 253B is our pick

Llama 3.1 Nemotron Ultra 253B is the better all-round choice, scoring 65/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price and inputs & features. 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 priceLlama 3.1 Nemotron Ultra 253BLlama 3.1 Nemotron Ultra 253B Free · Qwen-VL OCR $0.72 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
  • Longest contextAya Expanse 32B and Llama 3.1 Nemotron Ultra 253BAya Expanse 32B 128,000 · Llama 3.1 Nemotron Ultra 253B 128,000 · Qwen-VL OCR 34,096 tokens
  • Widest inputsQwen-VL OCRAya Expanse 32B: Text · Llama 3.1 Nemotron Ultra 253B: Text · Qwen-VL OCR: Text, Images
  • Self-hostingAya Expanse 32B and Llama 3.1 Nemotron Ultra 253BPublishes downloadable weights (CC-BY-NC-4.0)
How the score is built
MeasureWeightAya Expanse 32BLlama 3.1 Nemotron Ultra 253BQwen-VL OCR
Price50%5610057
Inputs & features30%03525
Context window20%24241
Overall100%33/10065/10036/100

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.

Aya Expanse 32B vs Llama 3.1 Nemotron Ultra 253B vs Qwen-VL OCR specifications side by side
SpecificationAya Expanse 32BCohereLlama 3.1 Nemotron Ultra 253BNVIDIAQwen-VL OCRAlibaba (Qwen)
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.50Free (best)$0.72
Output$1.50Free (best)$0.72
Cached input———
Blended (3:1)$0.75Free (best)$0.72
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 1 providersOfficial Nvidia APIOfficial Alibaba API
Limits
Context window128,000 tokens (best)128,000 tokens (best)34,096 tokens
Max output4,000 tokens8,192 tokens (best)4,096 tokens
Inputs and features
TextYesYesYes
ImagesNoNoYes
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoYesNo
Tool callingNoYesNo
Structured outputNoNoNo
Availability
WeightsOpenCC-BY-NC-4.0OpenProprietary
API model IDc4ai-aya-expanse-32bnvidia/llama-3.1-nemotron-ultra-253b-v1qwen-vl-ocr
API providers2 (best)11
ReleasedOct 24, 2024Apr 7, 2025Oct 28, 2024
Knowledge cutoff——Apr 2024
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.

  • Aya Expanse 32B$8.00
  • Llama 3.1 Nemotron Ultra 253BFree
  • Qwen-VL OCR$8.64
04 — Questions

Which should you choose?

Which is better: Aya Expanse 32B, Llama 3.1 Nemotron Ultra 253B or Qwen-VL OCR?

Llama 3.1 Nemotron Ultra 253B is the better all-round choice, scoring 65/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price and inputs & features. 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, Aya Expanse 32B, Llama 3.1 Nemotron Ultra 253B or Qwen-VL OCR?

Llama 3.1 Nemotron Ultra 253B is cheaper at Free input / Free output per million tokens (official Nvidia API price). Qwen-VL OCR costs $0.72 input / $0.72 output per million tokens (official Alibaba API price); Aya Expanse 32B costs $0.50 input / $1.50 output per million tokens (median across 1 API provider). Llama 3.1 Nemotron Ultra 253B is listed as free.

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Aya Expanse 32B has not been scored yet, Llama 3.1 Nemotron Ultra 253B has not been scored yet and Qwen-VL OCR has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Aya Expanse 32B, Llama 3.1 Nemotron Ultra 253B and Qwen-VL OCR yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Expanse 32B and Qwen-VL OCR does not support tool calling, which most coding agents need.

Which has the bigger context window?

Aya Expanse 32B and Llama 3.1 Nemotron Ultra 253B have the largest context windows (128,000 and 128,000 tokens), against 34,096 for Qwen-VL OCR. Maximum output per response: Aya Expanse 32B up to 4,000, Llama 3.1 Nemotron Ultra 253B up to 8,192, Qwen-VL OCR up to 4,096 tokens.

Which can read images, PDFs, audio or video?

Aya Expanse 32B accepts text; Llama 3.1 Nemotron Ultra 253B accepts text; Qwen-VL OCR accepts text and images. Qwen-VL OCR handles the widest range of inputs.

Are any of these open source?

Aya Expanse 32B and Llama 3.1 Nemotron Ultra 253B publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen-VL OCR is proprietary.

Which is newer?

Llama 3.1 Nemotron Ultra 253B is the newest, released Apr 7, 2025. Qwen-VL OCR came out Oct 28, 2024; Aya Expanse 32B came out Oct 24, 2024. Knowledge cutoff: Qwen-VL OCR Apr 2024.

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 Oct 4, 2026.