Llama-3.2-11B-Vision-Instruct vs Qwen-VL Plus vs Command R
Too close to call on our weighted score (Llama-3.2-11B-Vision-Instruct 58, Qwen-VL Plus 57, Command R 51). The right pick depends on what you value most.
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
Alibaba (Qwen)Qwen-VL Plus
57/100- ECI—
- Price$0.21 / $0.63
- Context131K
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
Too close to call
It is close. Our weighted score puts them within 1 points (Llama-3.2-11B-Vision-Instruct 58/100, Qwen-VL Plus 57/100, Command R 51/100), so choose by what matters most for your work: Command R on price and Qwen-VL Plus for long inputs. 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 Plus $0.315 per 1M tokens (3:1 blend)
- Longest contextQwen-VL PlusQwen-VL Plus 131,072 · Llama-3.2-11B-Vision-Instruct 128,000 · Command R 128,000 tokens
- Widest inputsLlama-3.2-11B-Vision-Instruct and Qwen-VL PlusLlama-3.2-11B-Vision-Instruct: Text, Images · Qwen-VL Plus: Text, Images · Command R: Text
- Self-hostingLlama-3.2-11B-Vision-Instruct and Command RPublishes downloadable weights
| Measure | Weight | Llama-3.2-11B-Vision-Instruct | Qwen-VL Plus | Command R |
|---|---|---|---|---|
| Price | 50% | 76 | 74 | 77 |
| Inputs & features | 30% | 50 | 50 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 58/100 | 57/100 | 51/100 |
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 | Llama-3.2-11B-Vision-InstructMeta | Qwen-VL PlusAlibaba (Qwen) | Command RCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.197 | $0.21 | $0.15 (best) |
| Output | $0.51 (best) | $0.63 | $0.60 |
| Cached input | — | — | — |
| Blended (3:1) | $0.275 | $0.315 | $0.263 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 2 providers | Official Alibaba API | Official Cohere API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 4,096 tokens | 8,192 tokens (best) | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | — | qwen-vl-plus | command-r-08-2024 |
| API providers | 2 | 2 | 5 (best) |
| Released | Sep 25, 2024 | Jan 25, 2024 | Aug 30, 2024 |
| Knowledge cutoff | Dec 2023 | Apr 2024 | Jun 1, 2024 |
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
- Qwen-VL Plus$3.36
- Command R$2.70
Which should you choose?
Which is better: Llama-3.2-11B-Vision-Instruct, Qwen-VL Plus or Command R?
It is close. Our weighted score puts them within 1 points (Llama-3.2-11B-Vision-Instruct 58/100, Qwen-VL Plus 57/100, Command R 51/100), so choose by what matters most for your work: Command R on price and Qwen-VL Plus for long inputs. 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, Qwen-VL Plus 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); Qwen-VL Plus costs $0.21 input / $0.63 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 $0.315 for Qwen-VL Plus (1.2× 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, Qwen-VL Plus 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, Qwen-VL Plus 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?
Qwen-VL Plus 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, Qwen-VL Plus 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; Qwen-VL Plus accepts text and images; Command R accepts text. Llama-3.2-11B-Vision-Instruct 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; Qwen-VL Plus 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; Qwen-VL Plus came out Jan 25, 2024. Knowledge cutoff: Llama-3.2-11B-Vision-Instruct Dec 2023, Qwen-VL Plus Apr 2024, 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.
