Command R vs Mistral Small 3.1 24B vs Llama-3.2-11B-Vision-Instruct
Too close to call on our weighted score (Mistral Small 3.1 24B 61, Llama-3.2-11B-Vision-Instruct 58, Command R 51). The right pick depends on what you value most.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
Mistral AIMistral Small 3.1 24B
61/100- ECI127.5
- Price$0.229 / $0.436
- Context128K
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 3 points (Mistral Small 3.1 24B 61/100, Llama-3.2-11B-Vision-Instruct 58/100, Command R 51/100), so choose by what matters most for your work: Command R on price. 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 · Mistral Small 3.1 24B $0.281 per 1M tokens (3:1 blend)
- Longest contextAbout the sameCommand R 128,000 · Mistral Small 3.1 24B 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 tokens
- Widest inputsMistral Small 3.1 24B and Llama-3.2-11B-Vision-InstructCommand R: Text · Mistral Small 3.1 24B: Text, Images · 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 | Mistral Small 3.1 24B | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 77 | 76 | 76 |
| Inputs & features | 30% | 25 | 60 | 50 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 51/100 | 61/100 | 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 | Mistral Small 3.1 24BMistral AI | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 127.5 | — |
| ECI rank | — | #132 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 47.5% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 5.8% | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.229 | $0.197 |
| Output | $0.60 | $0.436 (best) | $0.51 |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 (best) | $0.281 | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 2 providers | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 128,000 tokens |
| Max output | 4,000 tokens | 16,384 tokens (best) | 4,096 tokens |
| 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 | Yes | No |
| Availability | |||
| Weights | Open | Open | Open |
| API model ID | command-r-08-2024 | — | — |
| API providers | 5 (best) | 2 | 2 |
| Released | Aug 30, 2024 | Mar 17, 2025 | Sep 25, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Jun 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
- Mistral Small 3.1 24B$3.16
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Command R, Mistral Small 3.1 24B or Llama-3.2-11B-Vision-Instruct?
It is close. Our weighted score puts them within 3 points (Mistral Small 3.1 24B 61/100, Llama-3.2-11B-Vision-Instruct 58/100, Command R 51/100), so choose by what matters most for your work: Command R on price. 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, Mistral Small 3.1 24B or Llama-3.2-11B-Vision-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); Mistral Small 3.1 24B costs $0.229 input / $0.436 output per million tokens (median across 2 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 $0.281 for Mistral Small 3.1 24B (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, Mistral Small 3.1 24B has an ECI of 127.5 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, Mistral Small 3.1 24B 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?
Command R, Mistral Small 3.1 24B and Llama-3.2-11B-Vision-Instruct share the same 128,000-token context window. Maximum output per response: Command R up to 4,000, Mistral Small 3.1 24B up to 16,384, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Mistral Small 3.1 24B accepts text and images; Llama-3.2-11B-Vision-Instruct accepts text and images. Mistral Small 3.1 24B 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?
Mistral Small 3.1 24B is the newest, released Mar 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, Mistral Small 3.1 24B Jun 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.
