Command R vs Qwen-VL OCR vs Llama-3.2-11B-Vision-Instruct
Llama-3.2-11B-Vision-Instruct comes out ahead, 58 to 51 and 36 on our weighted score.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
Alibaba (Qwen)Qwen-VL OCR
36/100- ECI—
- Price$0.72 / $0.72
- Context34K
- Our pick
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
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 Qwen-VL OCR (36). 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 · Qwen-VL OCR $0.72 per 1M tokens (3:1 blend)
- Longest contextCommand R and Llama-3.2-11B-Vision-InstructCommand R 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 · Qwen-VL OCR 34,096 tokens
- Widest inputsQwen-VL OCR and Llama-3.2-11B-Vision-InstructCommand R: Text · Qwen-VL OCR: Text, Images · Llama-3.2-11B-Vision-Instruct: Text, Images
- Self-hostingCommand R and Llama-3.2-11B-Vision-InstructPublishes downloadable weights
| Measure | Weight | Command R | Qwen-VL OCR | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 77 | 57 | 76 |
| Inputs & features | 30% | 25 | 25 | 50 |
| Context window | 20% | 24 | 1 | 24 |
| Overall | 100% | 51/100 | 36/100 | 58/100 |
Left out because at least one model lacks the data: capability. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | Command RCohere | Qwen-VL OCRAlibaba (Qwen) | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.72 | $0.197 |
| Output | $0.60 | $0.72 | $0.51 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 (best) | $0.72 | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Official Alibaba API | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens (best) | 34,096 tokens | 128,000 tokens (best) |
| Max output | 4,000 tokens | 4,096 tokens (best) | 4,096 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | command-r-08-2024 | qwen-vl-ocr | — |
| API providers | 5 (best) | 1 | 2 |
| Released | Aug 30, 2024 | Oct 28, 2024 | Sep 25, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Apr 2024 | Dec 2023 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Command R$2.70
- Qwen-VL OCR$8.64
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Command R, Qwen-VL OCR or Llama-3.2-11B-Vision-Instruct?
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Command R (51) and Qwen-VL OCR (36). 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, Command R, Qwen-VL OCR or Llama-3.2-11B-Vision-Instruct?
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); Qwen-VL OCR costs $0.72 input / $0.72 output per million tokens (official Alibaba API price). 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 $0.72 for Qwen-VL OCR (2.7× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command R has not been scored yet, Qwen-VL OCR has not been scored yet and Llama-3.2-11B-Vision-Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command R, Qwen-VL OCR and Llama-3.2-11B-Vision-Instruct yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen-VL OCR does not support tool calling, which most coding agents need.
Which has the bigger context window?
Command R and Llama-3.2-11B-Vision-Instruct have the largest context windows (128,000 and 128,000 tokens), against 34,096 for Qwen-VL OCR. Maximum output per response: Command R up to 4,000, Qwen-VL OCR up to 4,096, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Qwen-VL OCR accepts text and images; Llama-3.2-11B-Vision-Instruct accepts text and images. Qwen-VL OCR handles the widest range of inputs.
Are any of these open source?
Command R and Llama-3.2-11B-Vision-Instruct publishes its weights and can be self-hosted; Qwen-VL OCR is proprietary.
Which is newer?
Qwen-VL OCR is the newest, released Oct 28, 2024. Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024; Command R came out Aug 30, 2024. Knowledge cutoff: Command R Jun 1, 2024, Qwen-VL OCR Apr 2024, Llama-3.2-11B-Vision-Instruct Dec 2023.
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.
