Command R vs Command R7B vs Llama-3.2-11B-Vision-Instruct
Command R7B comes out ahead, 62 to 58 and 51 on our weighted score, and it is the cheaper option too.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
- Our pickCohere
Command R7B
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
Command R7B is our pick
Command R7B is the better all-round choice, scoring 62/100 against Llama-3.2-11B-Vision-Instruct (58) and Command R (51). It leads on price. Llama-3.2-11B-Vision-Instruct wins 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 R7BCommand R7B $0.066 · Command R $0.263 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextAbout the sameCommand R 128,000 · Command R7B 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructCommand R: Text · Command R7B: Text · 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 | Command R7B | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 77 | 100 | 76 |
| Inputs & features | 30% | 25 | 25 | 50 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 51/100 | 62/100 | 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 | Command R7BCohere | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | $0.037 (best) | $0.197 |
| Output | $0.60 | $0.15 (best) | $0.51 |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 | $0.066 (best) | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Official Cohere API | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 128,000 tokens |
| Max output | 4,000 tokens | 4,000 tokens | 4,096 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | Yes |
| 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 | Open | Open |
| API model ID | command-r-08-2024 | command-r7b-12-2024 | — |
| API providers | 5 (best) | 5 (best) | 2 |
| Released | Aug 30, 2024 | Dec 2, 2024 | Sep 25, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Jun 1, 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
- Command R7B$0.675
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Command R, Command R7B or Llama-3.2-11B-Vision-Instruct?
Command R7B is the better all-round choice, scoring 62/100 against Llama-3.2-11B-Vision-Instruct (58) and Command R (51). It leads on price. Llama-3.2-11B-Vision-Instruct wins 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, Command R7B or Llama-3.2-11B-Vision-Instruct?
Command R7B is cheaper at $0.037 input / $0.15 output per million tokens (official Cohere 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 $0.066 per million tokens for Command R7B versus $0.263 for Command R (4× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (4.2× 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, Command R7B 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, Command R7B 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?
Command R, Command R7B and Llama-3.2-11B-Vision-Instruct share the same 128,000-token context window. Maximum output per response: Command R up to 4,000, Command R7B up to 4,000, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Command R7B accepts text; Llama-3.2-11B-Vision-Instruct accepts text and images. 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?
Command R7B is the newest, released Dec 2, 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, Command R7B Jun 1, 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.
