Command R vs Jamba Mini vs Llama-3.2-11B-Vision-Instruct
Too close to call on our weighted score (Llama-3.2-11B-Vision-Instruct 58, Jamba Mini 57, Command R 51). The right pick depends on what you value most.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
AI21 LabsJamba Mini
57/100- ECI—
- Price$0.20 / $0.40
- Context256K
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 1 points (Llama-3.2-11B-Vision-Instruct 58/100, Jamba Mini 57/100, Command R 51/100), so choose by what matters most for your work: Jamba Mini on price and Jamba Mini 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 priceJamba MiniJamba Mini $0.25 · Command R $0.263 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextJamba MiniJamba Mini 256,000 · Command R 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructCommand R: Text · Jamba Mini: 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 | Jamba Mini | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 77 | 78 | 76 |
| Inputs & features | 30% | 25 | 35 | 50 |
| Context window | 20% | 24 | 36 | 24 |
| Overall | 100% | 51/100 | 57/100 | 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 | Jamba MiniAI21 Labs | 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 | $0.40 (best) | $0.51 |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 | $0.25 (best) | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Official AI21 Labs API | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 256,000 tokens (best) | 128,000 tokens |
| Max output | 4,000 tokens | 4,096 tokens (best) | 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 | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | command-r-08-2024 | jamba-mini | — |
| API providers | 5 (best) | 1 | 2 |
| Released | Aug 30, 2024 | Jan 1, 2026 | Sep 25, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Aug 22, 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
- Jamba Mini$2.80
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Command R, Jamba Mini or Llama-3.2-11B-Vision-Instruct?
It is close. Our weighted score puts them within 1 points (Llama-3.2-11B-Vision-Instruct 58/100, Jamba Mini 57/100, Command R 51/100), so choose by what matters most for your work: Jamba Mini on price and Jamba Mini 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, Jamba Mini or Llama-3.2-11B-Vision-Instruct?
Jamba Mini is cheaper at $0.20 input / $0.40 output per million tokens (official AI21 Labs 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 $0.25 per million tokens for Jamba Mini versus $0.263 for Command R (1.1× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (1.1× 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, Jamba Mini 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, Jamba Mini 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?
Jamba Mini has the largest context window at 256,000 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, Jamba Mini up to 4,096, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Jamba Mini 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?
Jamba Mini is the newest, released Jan 1, 2026. 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, Jamba Mini Aug 22, 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.
