Command R vs Qwen3-VL 30B-A3B vs Llama-3.2-11B-Vision-Instruct
Too close to call on our weighted score (Qwen3-VL 30B-A3B 59, Llama-3.2-11B-Vision-Instruct 58, Command R 51). The right pick depends on what you value most.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
Alibaba (Qwen)Qwen3-VL 30B-A3B
59/100- ECI—
- Price$0.20 / $0.80
- Context131K
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (Qwen3-VL 30B-A3B 59/100, Llama-3.2-11B-Vision-Instruct 58/100, Command R 51/100), so choose by what matters most for your work: Command R on price and Qwen3-VL 30B-A3B 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 · Qwen3-VL 30B-A3B $0.35 per 1M tokens (3:1 blend)
- Longest contextQwen3-VL 30B-A3BQwen3-VL 30B-A3B 131,072 · Command R 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 tokens
- Widest inputsQwen3-VL 30B-A3B and Llama-3.2-11B-Vision-InstructCommand R: Text · Qwen3-VL 30B-A3B: Text, Images · Llama-3.2-11B-Vision-Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Command R | Qwen3-VL 30B-A3B | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 77 | 72 | 76 |
| Inputs & features | 30% | 25 | 60 | 50 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 51/100 | 59/100 | 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 | Qwen3-VL 30B-A3BAlibaba (Qwen) | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.20 | $0.197 |
| Output | $0.60 | $0.80 | $0.51 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 (best) | $0.35 | $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 | 131,072 tokens (best) | 128,000 tokens |
| Max output | 4,000 tokens | 32,768 tokens (best) | 4,096 tokens |
| 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 | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | command-r-08-2024 | qwen3-vl-30b-a3b | — |
| API providers | 5 (best) | 1 | 2 |
| Released | Aug 30, 2024 | Apr 2025 | Sep 25, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Apr 2025 | 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
- Qwen3-VL 30B-A3B$3.60
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Command R, Qwen3-VL 30B-A3B or Llama-3.2-11B-Vision-Instruct?
It is close. Our weighted score puts them within a point (Qwen3-VL 30B-A3B 59/100, Llama-3.2-11B-Vision-Instruct 58/100, Command R 51/100), so choose by what matters most for your work: Command R on price and Qwen3-VL 30B-A3B 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, Command R, Qwen3-VL 30B-A3B 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); Qwen3-VL 30B-A3B costs $0.20 input / $0.80 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 $0.35 for Qwen3-VL 30B-A3B (1.3× 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, Qwen3-VL 30B-A3B 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, Qwen3-VL 30B-A3B 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. All three support tool calling for agent workflows.
Which has the bigger context window?
Qwen3-VL 30B-A3B has the largest context window at 131,072 tokens, against 128,000 for Command R and 128,000 for Llama-3.2-11B-Vision-Instruct. Maximum output per response: Command R up to 4,000, Qwen3-VL 30B-A3B up to 32,768, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Qwen3-VL 30B-A3B accepts text and images; Llama-3.2-11B-Vision-Instruct accepts text and images. Qwen3-VL 30B-A3B 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?
Qwen3-VL 30B-A3B is the newest, released Apr 2025. 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, Qwen3-VL 30B-A3B Apr 2025, 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.
