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Comparison · 3 models · Updated Oct 4, 2026

Llama-3.2-11B-Vision-Instruct vs Qwen2.5-Coder-32B-Instruct vs Command R

Llama-3.2-11B-Vision-Instruct comes out ahead, 58 to 51 and 45 on our weighted score.

  1. Our pick

    Meta

    Llama-3.2-11B-Vision-Instruct

    Released Sep 25, 2024

    58/100
    • ECI—
    • Price$0.197 / $0.51
    • Context128K
    Full specs Remove Llama-3.2-11B-Vision-Instruct

  2. 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

  3. Cohere

    Command R

    Released Aug 30, 2024

    51/100
    • ECI—
    • Price$0.15 / $0.60
    • Context128K
    Full specs Remove Command R
01 — Verdict

Llama-3.2-11B-Vision-Instruct is our pick

Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Command R (51) and Qwen2.5-Coder-32B-Instruct (45). It leads on 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 priceCommand RCommand R $0.263 · Llama-3.2-11B-Vision-Instruct $0.275 · Qwen2.5-Coder-32B-Instruct $0.473 per 1M tokens (3:1 blend)
  • Longest contextQwen2.5-Coder-32B-InstructQwen2.5-Coder-32B-Instruct 131,072 · Llama-3.2-11B-Vision-Instruct 128,000 · Command R 128,000 tokens
  • Widest inputsLlama-3.2-11B-Vision-InstructLlama-3.2-11B-Vision-Instruct: Text, Images · Qwen2.5-Coder-32B-Instruct: Text · Command R: Text
  • Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
How the score is built
MeasureWeightLlama-3.2-11B-Vision-InstructQwen2.5-Coder-32B-InstructCommand R
Price50%766577
Inputs & features30%502525
Context window20%242424
Overall100%58/10045/10051/100

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.

Llama-3.2-11B-Vision-Instruct vs Qwen2.5-Coder-32B-Instruct vs Command R specifications side by side
SpecificationLlama-3.2-11B-Vision-InstructMetaQwen2.5-Coder-32B-InstructAlibaba (Qwen)Command RCohere
Capability
Capabilities Index (ECI)———
ECI rank———
Price per million tokens
Input$0.197$0.43$0.15 (best)
Output$0.51 (best)$0.60$0.60
Cached input———
Blended (3:1)$0.275$0.473$0.263 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 2 providersMedian of 4 providersOfficial Cohere API
Limits
Context window128,000 tokens131,072 tokens (best)128,000 tokens
Max output4,096 tokens8,192 tokens (best)4,000 tokens
Inputs and features
TextYesYesYes
ImagesYesNoNo
PDFsNoNoNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputNoNoNo
Availability
WeightsOpenOpenOpen
API model ID——command-r-08-2024
API providers245 (best)
ReleasedSep 25, 2024Nov 12, 2024Aug 30, 2024
Knowledge cutoffDec 2023—Jun 1, 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.

  • Llama-3.2-11B-Vision-Instruct$2.99
  • Qwen2.5-Coder-32B-Instruct$5.50
  • Command R$2.70
04 — Questions

Which should you choose?

Which is better: Llama-3.2-11B-Vision-Instruct, Qwen2.5-Coder-32B-Instruct or Command R?

Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Command R (51) and Qwen2.5-Coder-32B-Instruct (45). It leads on 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, Llama-3.2-11B-Vision-Instruct, Qwen2.5-Coder-32B-Instruct or Command R?

Command R is cheaper at $0.15 input / $0.60 output per million tokens (official Cohere API price). Llama-3.2-11B-Vision-Instruct costs $0.197 input / $0.51 output per million tokens (median across 2 API providers); Qwen2.5-Coder-32B-Instruct costs $0.43 input / $0.60 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for Command R versus $0.275 for Llama-3.2-11B-Vision-Instruct (1× as much) and $0.473 for Qwen2.5-Coder-32B-Instruct (1.8× as much).

Which scores higher on benchmarks?

There is no independent benchmark that covers all three models yet. Llama-3.2-11B-Vision-Instruct has not been scored yet, Qwen2.5-Coder-32B-Instruct has not been scored yet and Command R has not been scored yet.

Which is better for coding?

There are no published SWE-bench Verified results for Llama-3.2-11B-Vision-Instruct, Qwen2.5-Coder-32B-Instruct and Command R 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?

Qwen2.5-Coder-32B-Instruct has the largest context window at 131,072 tokens, against 128,000 for Llama-3.2-11B-Vision-Instruct and 128,000 for Command R. Maximum output per response: Llama-3.2-11B-Vision-Instruct up to 4,096, Qwen2.5-Coder-32B-Instruct up to 8,192, Command R up to 4,000 tokens.

Which can read images, PDFs, audio or video?

Llama-3.2-11B-Vision-Instruct accepts text and images; Qwen2.5-Coder-32B-Instruct accepts text; Command R accepts text. Llama-3.2-11B-Vision-Instruct handles the widest range of inputs.

Are any of these open source?

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

Which is newer?

Qwen2.5-Coder-32B-Instruct is the newest, released Nov 12, 2024. Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024; Command R came out Aug 30, 2024. Knowledge cutoff: Llama-3.2-11B-Vision-Instruct Dec 2023, Command R Jun 1, 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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