Command R vs Llama-3.2-11B-Vision-Instruct vs Saba
Llama-3.2-11B-Vision-Instruct comes out ahead, 64 to 52 and 46 on our weighted score, and it is the cheaper option too.
CohereCommand R
52/100- ECI—
- Price$0.15 / $0.60
- Context128K
- Our pick
MetaLlama-3.2-11B-Vision-Instruct
64/100- ECI—
- Price$0.055 / $0.345
- Context128K
Mistral AISaba
46/100- ECI—
- Price$0.20 / $0.60
- Context33K
Llama-3.2-11B-Vision-Instruct is our pick
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 64/100 against Command R (52) and Saba (46). It leads on price and 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 priceLlama-3.2-11B-Vision-InstructLlama-3.2-11B-Vision-Instruct $0.128 · Command R $0.263 · Saba $0.30 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 · Saba 32,768 tokens
- Widest inputsLlama-3.2-11B-Vision-Instruct and SabaCommand R: Text · Llama-3.2-11B-Vision-Instruct: Text, Images · Saba: Text, PDFs
- Self-hostingCommand R and Llama-3.2-11B-Vision-InstructPublishes downloadable weights
| Measure | Weight | Command R | Llama-3.2-11B-Vision-Instruct | Saba |
|---|---|---|---|---|
| Price | 50% | 74 | 88 | 72 |
| Inputs & features | 30% | 35 | 50 | 35 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 52/100 | 64/100 | 46/100 |
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 | SabaMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | $0.055 (best) | $0.20 |
| Output | $0.60 | $0.345 (best) | $0.60 |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 | $0.128 (best) | $0.30 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 3 providers | Median of 4 providers |
| Limits | |||
| Context window | 128,000 tokens (best) | 128,000 tokens (best) | 32,768 tokens |
| Max output | 4,000 tokens | 4,096 tokens | 26,214 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | Yes |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Open | Open | Proprietary |
| API model ID | command-r-08-2024 | — | — |
| API providers | 6 (best) | 4 | 4 |
| Released | Aug 30, 2024 | Sep 25, 2024 | Feb 17, 2025 |
| Knowledge cutoff | Jun 1, 2024 | Dec 2023 | Sep 30, 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$1.24
- Saba$3.20
Which should you choose?
Which is better: Command R, Llama-3.2-11B-Vision-Instruct or Saba?
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 64/100 against Command R (52) and Saba (46). It leads on price and 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, Llama-3.2-11B-Vision-Instruct or Saba?
Llama-3.2-11B-Vision-Instruct is cheaper at $0.055 input / $0.345 output per million tokens (median across 3 API providers). Command R costs $0.15 input / $0.60 output per million tokens (official Cohere API price); Saba costs $0.20 input / $0.60 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $0.128 per million tokens for Llama-3.2-11B-Vision-Instruct versus $0.263 for Command R (2.1× as much) and $0.30 for Saba (2.4× 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 Saba has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command R, Llama-3.2-11B-Vision-Instruct and Saba 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 and Llama-3.2-11B-Vision-Instruct have the largest context windows (128,000 and 128,000 tokens), against 32,768 for Saba. Maximum output per response: Command R up to 4,000, Llama-3.2-11B-Vision-Instruct up to 4,096, Saba up to 26,214 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Llama-3.2-11B-Vision-Instruct accepts text and images; Saba accepts text and PDFs. Llama-3.2-11B-Vision-Instruct 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; Saba is proprietary.
Which is newer?
Saba is the newest, released Feb 17, 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, Saba Sep 30, 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 Nov 2, 2026.
