Command R vs Ministral 8B Instruct vs Llama-3.2-11B-Vision-Instruct
Too close to call on our weighted score (Llama-3.2-11B-Vision-Instruct 58, Ministral 8B Instruct 57, Command R 51). The right pick depends on what you value most.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
Mistral AIMinistral 8B Instruct
57/100- ECI—
- Price$0.15 / $0.15
- 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 1 points (Llama-3.2-11B-Vision-Instruct 58/100, Ministral 8B Instruct 57/100, Command R 51/100), so choose by what matters most for your work: Ministral 8B Instruct on price and Ministral 8B Instruct 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 priceMinistral 8B InstructMinistral 8B Instruct $0.15 · Command R $0.263 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextMinistral 8B InstructMinistral 8B Instruct 131,072 · Command R 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructCommand R: Text · Ministral 8B Instruct: 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 | Ministral 8B Instruct | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 77 | 89 | 76 |
| Inputs & features | 30% | 25 | 25 | 50 |
| Context window | 20% | 24 | 24 | 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 | Ministral 8B InstructMistral AI | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| GPQA DiamondGraduate-level science questions | — | 27.2% | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.15 (best) | $0.197 |
| Output | $0.60 | $0.15 (best) | $0.51 |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 | $0.15 (best) | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 1 providers | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 4,000 tokens | 8,192 tokens (best) | 4,096 tokens |
| 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 | OpenMistral Research License | Open |
| API model ID | command-r-08-2024 | — | — |
| API providers | 5 (best) | 1 | 2 |
| Released | Aug 30, 2024 | Oct 16, 2024 | Sep 25, 2024 |
| Knowledge cutoff | 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
- Ministral 8B Instruct$1.80
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Command R, Ministral 8B Instruct 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, Ministral 8B Instruct 57/100, Command R 51/100), so choose by what matters most for your work: Ministral 8B Instruct on price and Ministral 8B Instruct 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, Ministral 8B Instruct or Llama-3.2-11B-Vision-Instruct?
Ministral 8B Instruct is cheaper at $0.15 input / $0.15 output per million tokens (median across 1 API provider). 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 $0.15 per million tokens for Ministral 8B Instruct versus $0.263 for Command R (1.8× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (1.8× 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, Ministral 8B Instruct 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, Ministral 8B Instruct 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?
Ministral 8B Instruct 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, Ministral 8B Instruct up to 8,192, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Ministral 8B Instruct 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 (Mistral Research License), so you can self-host them.
Which is newer?
Ministral 8B Instruct is the newest, released Oct 16, 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, 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.
