Command R vs Llama-3.2-11B-Vision-Instruct vs Voxtral Small 24B 2507
Too close to call on our weighted score (Llama-3.2-11B-Vision-Instruct 58, Voxtral Small 24B 2507 55, Command R 51). The right pick depends on what you value most.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
Mistral AIVoxtral Small 24B 2507
55/100- ECI—
- Price$0.10 / $0.30
- Context33K
Too close to call
It is close. Our weighted score puts them within 3 points (Llama-3.2-11B-Vision-Instruct 58/100, Voxtral Small 24B 2507 55/100, Command R 51/100), so choose by what matters most for your work: Voxtral Small 24B 2507 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 priceVoxtral Small 24B 2507Voxtral Small 24B 2507 $0.15 · 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 · Voxtral Small 24B 2507 32,768 tokens
- Widest inputsLlama-3.2-11B-Vision-Instruct and Voxtral Small 24B 2507Command R: Text · Llama-3.2-11B-Vision-Instruct: Text, Images · Voxtral Small 24B 2507: Text, Audio
- 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 | Voxtral Small 24B 2507 |
|---|---|---|---|---|
| Price | 50% | 77 | 76 | 89 |
| Inputs & features | 30% | 25 | 50 | 35 |
| Context window | 20% | 24 | 24 | 0 |
| Overall | 100% | 51/100 | 58/100 | 55/100 |
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 | Voxtral Small 24B 2507Mistral AI |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 | $0.197 | $0.10 (best) |
| Output | $0.60 | $0.51 | $0.30 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 | $0.275 | $0.15 (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) | 32,768 tokens |
| Max output | 4,000 tokens | 4,096 tokens | 32,768 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | Yes | No |
| PDFs | No | No | No |
| Audio | No | No | Yes |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Open | OpenApache 2.0 |
| API model ID | command-r-08-2024 | — | voxtral-small-latest |
| API providers | 5 | 2 | 7 (best) |
| Released | Aug 30, 2024 | Sep 25, 2024 | Jul 15, 2025 |
| 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
- Llama-3.2-11B-Vision-Instruct$2.99
- Voxtral Small 24B 2507$1.60
Which should you choose?
Which is better: Command R, Llama-3.2-11B-Vision-Instruct or Voxtral Small 24B 2507?
It is close. Our weighted score puts them within 3 points (Llama-3.2-11B-Vision-Instruct 58/100, Voxtral Small 24B 2507 55/100, Command R 51/100), so choose by what matters most for your work: Voxtral Small 24B 2507 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, Llama-3.2-11B-Vision-Instruct or Voxtral Small 24B 2507?
Voxtral Small 24B 2507 is cheaper at $0.10 input / $0.30 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 $0.15 per million tokens for Voxtral Small 24B 2507 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, Llama-3.2-11B-Vision-Instruct has not been scored yet and Voxtral Small 24B 2507 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 Voxtral Small 24B 2507 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 Voxtral Small 24B 2507. Maximum output per response: Command R up to 4,000, Llama-3.2-11B-Vision-Instruct up to 4,096, Voxtral Small 24B 2507 up to 32,768 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Llama-3.2-11B-Vision-Instruct accepts text and images; Voxtral Small 24B 2507 accepts text and audio. Llama-3.2-11B-Vision-Instruct handles the widest range of inputs.
Are any of these open source?
Yes, all three publish their weights (Apache 2.0), so you can self-host them.
Which is newer?
Voxtral Small 24B 2507 is the newest, released Jul 15, 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.
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.
