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Comparison · 3 models · Updated Oct 4, 2026

Llama-3.2-11B-Vision-Instruct vs GPT-4o mini vs Command R

GPT-4o mini comes out ahead, 64 to 58 and 51 on our weighted score.

  1. 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. Our pick

    OpenAI

    GPT-4o mini

    Released Jul 18, 2024

    64/100
    • ECI126.6
    • Price$0.15 / $0.60
    • Context128K
    Full specs Remove GPT-4o mini

  3. Cohere

    Command R

    Released Aug 30, 2024

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

GPT-4o mini is our pick

GPT-4o mini is the better all-round choice, scoring 64/100 against Llama-3.2-11B-Vision-Instruct (58) and Command R (51). 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 priceGPT-4o mini and Command RGPT-4o mini $0.263 · Command R $0.263 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
  • Longest contextAbout the sameLlama-3.2-11B-Vision-Instruct 128,000 · GPT-4o mini 128,000 · Command R 128,000 tokens
  • Widest inputsGPT-4o miniLlama-3.2-11B-Vision-Instruct: Text, Images · GPT-4o mini: Text, Images, PDFs · Command R: Text
  • Self-hostingLlama-3.2-11B-Vision-Instruct and Command RPublishes downloadable weights
How the score is built
MeasureWeightLlama-3.2-11B-Vision-InstructGPT-4o miniCommand R
Price50%767777
Inputs & features30%507025
Context window20%242424
Overall100%58/10064/10051/100

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 GPT-4o mini vs Command R specifications side by side
SpecificationLlama-3.2-11B-Vision-InstructMetaGPT-4o miniOpenAICommand RCohere
Capability
Capabilities Index (ECI)—126.6—
ECI rank—#135 of 148—
GPQA DiamondGraduate-level science questions—37.7%—
FrontierMath Tiers 1–3Research-level mathematics—0.7%—
OTIS Mock AIME 2024–2025Competition mathematics—6.9%—
SimpleQA VerifiedShort factual questions—8.3%—
Price per million tokens
Input$0.197$0.15 (best)$0.15 (best)
Output$0.51 (best)$0.60$0.60
Cached input—$0.075—
Blended (3:1)$0.275$0.263 (best)$0.263 (best)
Long-context rateSame rateSame rateSame rate
Price sourceMedian of 2 providersOfficial OpenAI APIOfficial Cohere API
Limits
Context window128,000 tokens128,000 tokens128,000 tokens
Max output4,096 tokens16,384 tokens (best)4,000 tokens
Inputs and features
TextYesYesYes
ImagesYesYesNo
PDFsNoYesNo
AudioNoNoNo
VideoNoNoNo
ReasoningNoNoNo
Tool callingYesYesYes
Structured outputNoYesNo
Availability
WeightsOpenProprietaryOpen
API model ID—gpt-4o-minicommand-r-08-2024
API providers221 (best)5
ReleasedSep 25, 2024Jul 18, 2024Aug 30, 2024
Knowledge cutoffDec 2023Sep 2023Jun 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
  • GPT-4o mini$2.70
  • Command R$2.70
04 — Questions

Which should you choose?

Which is better: Llama-3.2-11B-Vision-Instruct, GPT-4o mini or Command R?

GPT-4o mini is the better all-round choice, scoring 64/100 against Llama-3.2-11B-Vision-Instruct (58) and Command R (51). 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, GPT-4o mini or Command R?

GPT-4o mini is cheaper at $0.15 input / $0.60 output per million tokens (official OpenAI API price). Command R costs $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). At a typical mix of three input tokens to one output token, that is $0.263 per million tokens for GPT-4o mini versus $0.263 for Command R (1× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (1× 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, GPT-4o mini has an ECI of 126.6 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, GPT-4o mini 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?

Llama-3.2-11B-Vision-Instruct, GPT-4o mini and Command R share the same 128,000-token context window. Maximum output per response: Llama-3.2-11B-Vision-Instruct up to 4,096, GPT-4o mini up to 16,384, 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; GPT-4o mini accepts text, images and PDFs; Command R accepts text. GPT-4o mini handles the widest range of inputs.

Are any of these open source?

Llama-3.2-11B-Vision-Instruct and Command R publishes its weights and can be self-hosted; GPT-4o mini is proprietary.

Which is newer?

Llama-3.2-11B-Vision-Instruct is the newest, released Sep 25, 2024. Command R came out Aug 30, 2024; GPT-4o mini came out Jul 18, 2024. Knowledge cutoff: Llama-3.2-11B-Vision-Instruct Dec 2023, GPT-4o mini Sep 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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