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Comparison · 3 models · Updated Oct 4, 2026

Qwen2.5-Coder-32B-Instruct vs Qwen-VL OCR vs Aya Expanse 32B

Qwen2.5-Coder-32B-Instruct comes out ahead, 45 to 36 and 33 on our weighted score, and it is the cheaper option too.

  1. Our pick

    Alibaba (Qwen)

    Qwen2.5-Coder-32B-Instruct

    Released Nov 12, 2024

    45/100
    • ECI—
    • Price$0.43 / $0.60
    • Context131K
    Full specs Remove Qwen2.5-Coder-32B-Instruct

  2. Alibaba (Qwen)

    Qwen-VL OCR

    Released Oct 28, 2024

    36/100
    • ECI—
    • Price$0.72 / $0.72
    • Context34K
    Full specs Remove Qwen-VL OCR

  3. Cohere

    Aya Expanse 32B

    Released Oct 24, 2024

    33/100
    • ECI—
    • Price$0.50 / $1.50
    • Context128K
    Full specs Remove Aya Expanse 32B
01 — Verdict

Qwen2.5-Coder-32B-Instruct is our pick

Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 45/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price. 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 priceQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct $0.473 · Qwen-VL OCR $0.72 · Aya Expanse 32B $0.75 per 1M tokens (3:1 blend)
  • Longest contextQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct 131,072 · Aya Expanse 32B 128,000 · Qwen-VL OCR 34,096 tokens
  • Widest inputsQwen-VL OCRQwen2.5-Coder-32B-Instruct: Text · Qwen-VL OCR: Text, Images · Aya Expanse 32B: Text
  • Self-hostingQwen2.5-Coder-32B-Instruct and Aya Expanse 32BPublishes downloadable weights (CC-BY-NC-4.0)
How the score is built
MeasureWeightQwen2.5-Coder-32B-InstructQwen-VL OCRAya Expanse 32B
Price50%655756
Inputs & features30%25250
Context window20%24124
Overall100%45/10036/10033/100

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.

Qwen2.5-Coder-32B-Instruct vs Qwen-VL OCR vs Aya Expanse 32B specifications side by side
SpecificationQwen2.5-Coder-32B-InstructAlibaba (Qwen)Qwen-VL OCRAlibaba (Qwen)Aya Expanse 32BCohere
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.43 (best)$0.72$0.50
Output$0.60 (best)$0.72$1.50
Cached input———
Blended (3:1)$0.473 (best)$0.72$0.75
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 4 providersOfficial Alibaba APIMedian of 1 providers
Limits
Context window131,072 tokens (best)34,096 tokens128,000 tokens
Max output8,192 tokens (best)4,096 tokens4,000 tokens
Inputs and features
TextYesYesYes
ImagesNoYesNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesNoNo
Structured outputNoNoNo
Availability
WeightsOpenProprietaryOpenCC-BY-NC-4.0
API model ID—qwen-vl-ocrc4ai-aya-expanse-32b
API providers4 (best)12
ReleasedNov 12, 2024Oct 28, 2024Oct 24, 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.

  • Qwen2.5-Coder-32B-Instruct$5.50
  • Qwen-VL OCR$8.64
  • Aya Expanse 32B$8.00
04 — Questions

Which should you choose?

Which is better: Qwen2.5-Coder-32B-Instruct, Qwen-VL OCR or Aya Expanse 32B?

Qwen2.5-Coder-32B-Instruct is the better all-round choice, scoring 45/100 against Qwen-VL OCR (36) and Aya Expanse 32B (33). It leads on price. 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, Qwen2.5-Coder-32B-Instruct, Qwen-VL OCR or Aya Expanse 32B?

Qwen2.5-Coder-32B-Instruct is cheaper at $0.43 input / $0.60 output per million tokens (median across 4 API providers). 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). At a typical mix of three input tokens to one output token, that is $0.473 per million tokens for Qwen2.5-Coder-32B-Instruct versus $0.72 for Qwen-VL OCR (1.5× as much) and $0.75 for Aya Expanse 32B (1.6× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Qwen2.5-Coder-32B-Instruct has not been scored yet, Qwen-VL OCR has not been scored yet and Aya Expanse 32B has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Qwen2.5-Coder-32B-Instruct, Qwen-VL OCR and Aya Expanse 32B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen-VL OCR and Aya Expanse 32B does not support tool calling, which most coding agents need.

Which has the bigger context window?

Qwen2.5-Coder-32B-Instruct has the largest context window at 131,072 tokens, against 128,000 for Aya Expanse 32B and 34,096 for Qwen-VL OCR. Maximum output per response: Qwen2.5-Coder-32B-Instruct up to 8,192, Qwen-VL OCR up to 4,096, Aya Expanse 32B up to 4,000 tokens.

Which can read images, PDFs, audio or video?

Qwen2.5-Coder-32B-Instruct accepts text; Qwen-VL OCR accepts text and images; Aya Expanse 32B accepts text. Qwen-VL OCR handles the widest range of inputs.

Are any of these open source?

Qwen2.5-Coder-32B-Instruct and Aya Expanse 32B publishes its weights (CC-BY-NC-4.0) and can be self-hosted; Qwen-VL OCR is proprietary.

Which is newer?

Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. 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.

05 — Keep comparing

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