Command R vs o4-mini-deep-research vs Llama-3.2-11B-Vision-Instruct
o4-mini-deep-research comes out ahead, 49 to 40 and 25 on our weighted score.
CohereCommand R
25/100- ECI—
- Price$0.15 / $0.60
- Context128K
- Our pick
OpenAIo4-mini-deep-research
49/100- ECI—
- Price—
- Context200K
MetaLlama-3.2-11B-Vision-Instruct
40/100- ECI—
- Price$0.197 / $0.51
- Context128K
o4-mini-deep-research is our pick
o4-mini-deep-research is the better all-round choice, scoring 49/100 against Llama-3.2-11B-Vision-Instruct (40) and Command R (25). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. 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 per 1M tokens (3:1 blend) · o4-mini-deep-research unpriced
- Longest contexto4-mini-deep-researcho4-mini-deep-research 200,000 · Command R 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 tokens
- Widest inputso4-mini-deep-research and Llama-3.2-11B-Vision-InstructCommand R: Text · o4-mini-deep-research: Text, Images · Llama-3.2-11B-Vision-Instruct: Text, Images
- Self-hostingCommand R and Llama-3.2-11B-Vision-InstructPublishes downloadable weights
| Measure | Weight | Command R | o4-mini-deep-research | Llama-3.2-11B-Vision-Instruct |
|---|---|---|---|---|
| Inputs & features | 60% | 25 | 60 | 50 |
| Context window | 40% | 24 | 32 | 24 |
| Overall | 100% | 25/100 | 49/100 | 40/100 |
Left out because at least one model lacks the data: capability and price. 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 | o4-mini-deep-researchOpenAI | Llama-3.2-11B-Vision-InstructMeta |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.15 (best) | — | $0.197 |
| Output | $0.60 | — | $0.51 (best) |
| Cached input | — | — | — |
| Blended (3:1) | $0.263 (best) | — | $0.275 |
| Long-context rate | Same rate | — | Same rate |
| Price source | Official Cohere API | — | Median of 2 providers |
| Limits | |||
| Context window | 128,000 tokens | 200,000 tokens (best) | 128,000 tokens |
| Max output | 4,000 tokens | 100,000 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 | Yes | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | command-r-08-2024 | — | — |
| API providers | 5 (best) | — | 2 |
| Released | Aug 30, 2024 | Jun 26, 2024 | Sep 25, 2024 |
| Knowledge cutoff | Jun 1, 2024 | May 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
- o4-mini-deep-research—
- Llama-3.2-11B-Vision-Instruct$2.99
Which should you choose?
Which is better: Command R, o4-mini-deep-research or Llama-3.2-11B-Vision-Instruct?
o4-mini-deep-research is the better all-round choice, scoring 49/100 against Llama-3.2-11B-Vision-Instruct (40) and Command R (25). It leads on inputs & features and context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, Command R, o4-mini-deep-research 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). 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). o4-mini-deep-research has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command R has not been scored yet, o4-mini-deep-research 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, o4-mini-deep-research 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?
o4-mini-deep-research has the largest context window at 200,000 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, o4-mini-deep-research up to 100,000, Llama-3.2-11B-Vision-Instruct up to 4,096 tokens.
Which can read images, PDFs, audio or video?
Command R accepts text; o4-mini-deep-research accepts text and images; Llama-3.2-11B-Vision-Instruct accepts text and images. o4-mini-deep-research 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; o4-mini-deep-research is proprietary.
Which is newer?
Llama-3.2-11B-Vision-Instruct is the newest, released Sep 25, 2024. Command R came out Aug 30, 2024; o4-mini-deep-research came out Jun 26, 2024. Knowledge cutoff: Command R Jun 1, 2024, o4-mini-deep-research May 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.
