Command R vs Solar Pro 4 vs Llama-3.2-11B-Vision-Instruct
Llama-3.2-11B-Vision-Instruct comes out ahead, 58 to 55 and 51 on our weighted score.
CohereCommand R
51/100- ECI—
- Price$0.15 / $0.60
- Context128K
Upstage
Solar Pro 4
55/100- ECI—
- Price$0.30 / $1.20
- Context524K
- Our pick
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
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 Solar Pro 4 (55) and Command R (51). It leads on inputs & features. Solar Pro 4 wins on context window. 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 · Solar Pro 4 $0.525 per 1M tokens (3:1 blend)
- Longest contextSolar Pro 4Solar Pro 4 524,288 · Command R 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructCommand R: Text · Solar Pro 4: Text · Llama-3.2-11B-Vision-Instruct: Text, Images
- Self-hostingCommand R and Llama-3.2-11B-Vision-InstructPublishes downloadable weights
| Measure | Weight | Command R | Solar Pro 4 | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Price | 50% | 77 | 63 | 76 |
| Inputs & features | 30% | 25 | 45 | 50 |
| Context window | 20% | 24 | 49 | 24 |
| Overall | 100% | 51/100 | 55/100 | 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 | Solar Pro 4Upstage | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 (best) | $0.30 | $0.197 |
| Output | $0.60 | $1.20 | $0.51 (best) |
| Cached input | — | $0.06 | — |
| Blended (3:1) | $0.263 (best) | $0.525 | $0.275 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Official Upstage API | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 524,288 tokens (best) | 128,000 tokens |
| Max output | 4,000 tokens | 131,072 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 | Yesminimal · low · medium · high · xhigh · max | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | Yes | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | command-r-08-2024 | solar-pro4 | — |
| API providers | 5 (best) | 3 | 2 |
| Released | Aug 30, 2024 | Aug 6, 2026 | Sep 25, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Feb 2026 | 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
- Solar Pro 4$5.40
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Command R, Solar Pro 4 or Llama-3.2-11B-Vision-Instruct?
Llama-3.2-11B-Vision-Instruct is the better all-round choice, scoring 58/100 against Solar Pro 4 (55) and Command R (51). It leads on inputs & features. Solar Pro 4 wins on context window. 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, Solar Pro 4 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); Solar Pro 4 costs $0.30 input / $1.20 output per million tokens (official Upstage API price). 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 $0.525 for Solar Pro 4 (2× 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, Solar Pro 4 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, Solar Pro 4 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?
Solar Pro 4 has the largest context window at 524,288 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, Solar Pro 4 up to 131,072, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; Solar Pro 4 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?
Command R and Llama-3.2-11B-Vision-Instruct publishes its weights and can be self-hosted; Solar Pro 4 is proprietary.
Which is newer?
Solar Pro 4 is the newest, released Aug 6, 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, Solar Pro 4 Feb 2026, 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.
