Command R vs Llama-3.2-11B-Vision-Instruct vs Mistral 7B
Llama-3.2-11B-Vision-Instruct comes out ahead, 58 to 51 and 47 on our weighted score, though Mistral 7B is 9% cheaper per token.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
- Our pick
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
Mistral AIMistral 7B
47/100- ECI—
- Price$0.25 / $0.25
- Context8K
Llama-3.2-11B-Vision-Instruct is our pick
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Command R (51) and Mistral 7B (47). It leads 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 priceMistral 7BMistral 7B $0.25 · Command R $0.263 · Llama-3.2-11B-Vision-Instruct $0.275 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 · Mistral 7B 8,000 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructCommand R: Text · Llama-3.2-11B-Vision-Instruct: Text, Images · Mistral 7B: Text
- 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 | Mistral 7B |
|---|---|---|---|---|
| Price | 50% | 77 | 76 | 78 |
| Inputs & features | 30% | 25 | 50 | 25 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 51/100 | 58/100 | 47/100 |
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 | Mistral 7BMistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.197 | $0.25 |
| Output | $0.60 | $0.51 | $0.25 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 | $0.275 | $0.25 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 2 providers | Official Mistral API |
| Limits | |||
| Context window | 128,000 tokens (best) | 128,000 tokens (best) | 8,000 tokens |
| Max output | 4,000 tokens | 4,096 tokens | 8,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| 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 | — | open-mistral-7b |
| API providers | 5 (best) | 2 | 1 |
| Released | Aug 30, 2024 | Sep 25, 2024 | Sep 27, 2023 |
| Knowledge cutoff | Jun 1, 2024 | Dec 2023 | 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
- Llama-3.2-11B-Vision-Instruct$2.99
- Mistral 7B$3.00
Which should you choose?
Which is better: Command R, Llama-3.2-11B-Vision-Instruct or Mistral 7B?
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Command R (51) and Mistral 7B (47). It leads 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, Llama-3.2-11B-Vision-Instruct or Mistral 7B?
Mistral 7B is cheaper at $0.25 input / $0.25 output per million tokens (official Mistral 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 Mistral 7B 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, Llama-3.2-11B-Vision-Instruct has not been scored yet and Mistral 7B 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 Mistral 7B 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 8,000 for Mistral 7B. Maximum output per response: Command R up to 4,000, Llama-3.2-11B-Vision-Instruct up to 4,096, Mistral 7B up to 8,000 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Llama-3.2-11B-Vision-Instruct accepts text and images; Mistral 7B accepts text. 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-3.2-11B-Vision-Instruct is the newest, released Sep 25, 2024. Command R came out Aug 30, 2024; Mistral 7B came out Sep 27, 2023. Knowledge cutoff: Command R Jun 1, 2024, Llama-3.2-11B-Vision-Instruct Dec 2023, Mistral 7B 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.
