Command R vs Llama-3.2-11B-Vision-Instruct vs Llama 4 Scout 17B Instruct
Llama 4 Scout 17B Instruct comes out ahead, 71 to 58 and 51 on our weighted score, though Command R is 23% cheaper per token.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
- Our pickMeta
Llama 4 Scout 17B Instruct
71/100- ECI129.7
- Price$0.225 / $0.69
- Context10M
Llama 4 Scout 17B Instruct is our pick
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 71/100 against Llama-3.2-11B-Vision-Instruct (58) and Command R (51). It leads on context window. 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 · Llama 4 Scout 17B Instruct $0.341 per 1M tokens (3:1 blend)
- Longest contextLlama 4 Scout 17B InstructLlama 4 Scout 17B Instruct 10,000,000 · Command R 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 tokens
- Widest inputsLlama-3.2-11B-Vision-Instruct and Llama 4 Scout 17B InstructCommand R: Text · Llama-3.2-11B-Vision-Instruct: Text, Images · Llama 4 Scout 17B Instruct: Text, Images
- Self-hostingAll open weightsEvery model here can be downloaded and run on your own hardware
| Measure | Weight | Command R | Llama-3.2-11B-Vision-Instruct | Llama 4 Scout 17B Instruct |
|---|---|---|---|---|
| Price | 50% | 77 | 76 | 72 |
| Inputs & features | 30% | 25 | 50 | 50 |
| Context window | 20% | 24 | 24 | 100 |
| Overall | 100% | 51/100 | 58/100 | 71/100 |
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 | Llama-3.2-11B-Vision-InstructMeta | Llama 4 Scout 17B InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | 129.7 |
| ECI rank | — | — | #126 of 148 |
| GPQA DiamondGraduate-level science questions | — | — | 51.8% |
| OTIS Mock AIME 2024–2025Competition mathematics | — | — | 7.8% |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.197 | $0.225 |
| Output | $0.60 | $0.51 (best) | $0.69 |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 (best) | $0.275 | $0.341 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 2 providers | Median of 4 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 10,000,000 tokens (best) |
| Max output | 4,000 tokens | 4,096 tokens | 16,384 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 | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | command-r-08-2024 | — | — |
| API providers | 5 (best) | 2 | 4 |
| Released | Aug 30, 2024 | Sep 25, 2024 | Apr 5, 2025 |
| Knowledge cutoff | Jun 1, 2024 | Dec 2023 | Aug 2024 |
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
- Llama-3.2-11B-Vision-Instruct$2.99
- Llama 4 Scout 17B Instruct$3.63
Which should you choose?
Which is better: Command R, Llama-3.2-11B-Vision-Instruct or Llama 4 Scout 17B Instruct?
Llama 4 Scout 17B Instruct is the better all-round choice, scoring 71/100 against Llama-3.2-11B-Vision-Instruct (58) and Command R (51). It leads on context window. 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, Llama-3.2-11B-Vision-Instruct or Llama 4 Scout 17B 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); Llama 4 Scout 17B Instruct costs $0.225 input / $0.69 output per million tokens (median across 4 API providers). 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 $0.341 for Llama 4 Scout 17B Instruct (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, Llama-3.2-11B-Vision-Instruct has not been scored yet and Llama 4 Scout 17B Instruct has an ECI of 129.7.
Which is better for coding?
There are no published SWE-bench Verified results for Command R, Llama-3.2-11B-Vision-Instruct and Llama 4 Scout 17B 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?
Llama 4 Scout 17B Instruct has the largest context window at 10,000,000 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, Llama-3.2-11B-Vision-Instruct up to 4,096, Llama 4 Scout 17B Instruct up to 16,384 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Llama-3.2-11B-Vision-Instruct accepts text and images; Llama 4 Scout 17B 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?
Llama 4 Scout 17B Instruct is the newest, released Apr 5, 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, Llama-3.2-11B-Vision-Instruct Dec 2023, Llama 4 Scout 17B Instruct Aug 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.
