Command R7B Arabic vs Llama-3.2-11B-Vision-Instruct vs Nova Micro
Too close to call on our weighted score (Command R7B Arabic 62, Nova Micro 62, Llama-3.2-11B-Vision-Instruct 58). The right pick depends on what you value most.
CohereCommand R7B Arabic
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
MetaLlama-3.2-11B-Vision-Instruct
58/100- ECI—
- Price$0.197 / $0.51
- Context128K
AmazonNova Micro
62/100- ECI—
- Price$0.035 / $0.14
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (Command R7B Arabic 62/100, Nova Micro 62/100, Llama-3.2-11B-Vision-Instruct 58/100), so choose by what matters most for your work: Nova Micro 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 priceNova MicroNova Micro $0.061 · Command R7B Arabic $0.066 · Llama-3.2-11B-Vision-Instruct $0.275 per 1M tokens (3:1 blend)
- Longest contextAbout the sameCommand R7B Arabic 128,000 · Llama-3.2-11B-Vision-Instruct 128,000 · Nova Micro 128,000 tokens
- Widest inputsLlama-3.2-11B-Vision-InstructCommand R7B Arabic: Text · Llama-3.2-11B-Vision-Instruct: Text, Images · Nova Micro: Text
- Self-hostingCommand R7B Arabic and Llama-3.2-11B-Vision-InstructPublishes downloadable weights
| Measure | Weight | Command R7B Arabic | Llama-3.2-11B-Vision-Instruct | Nova Micro |
|---|---|---|---|---|
| Price | 50% | 100 | 76 | 100 |
| Inputs & features | 30% | 25 | 50 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 62/100 | 58/100 | 62/100 |
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 R7B ArabicCohere | Llama-3.2-11B-Vision-InstructMeta | Nova MicroAmazon |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.037 | $0.197 | $0.035 (best) |
| Output | $0.15 | $0.51 | $0.14 (best) |
| Cached input | — | — | $0.0088 |
| Blended (3:1) | $0.066 | $0.275 | $0.061 (best) |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Median of 2 providers | Official Amazon Bedrock API |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 128,000 tokens |
| Max output | 4,000 tokens | 4,096 tokens | 10,000 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 | Proprietary |
| API model ID | command-r7b-arabic-02-2025 | — | amazon.nova-micro-v1:0 |
| API providers | 1 | 2 | 3 (best) |
| Released | Feb 27, 2025 | Sep 25, 2024 | Dec 3, 2024 |
| Knowledge cutoff | Jun 1, 2024 | Dec 2023 | Oct 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Command R7B Arabic$0.675
- Llama-3.2-11B-Vision-Instruct$2.99
- Nova Micro$0.63
Which should you choose?
Which is better: Command R7B Arabic, Llama-3.2-11B-Vision-Instruct or Nova Micro?
It is close. Our weighted score puts them within a point (Command R7B Arabic 62/100, Nova Micro 62/100, Llama-3.2-11B-Vision-Instruct 58/100), so choose by what matters most for your work: Nova Micro 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 R7B Arabic, Llama-3.2-11B-Vision-Instruct or Nova Micro?
Nova Micro is cheaper at $0.035 input / $0.14 output per million tokens (official Amazon Bedrock API price). Command R7B Arabic costs $0.037 input / $0.15 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 $0.061 per million tokens for Nova Micro versus $0.066 for Command R7B Arabic (1.1× as much) and $0.275 for Llama-3.2-11B-Vision-Instruct (4.5× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command R7B Arabic has not been scored yet, Llama-3.2-11B-Vision-Instruct has not been scored yet and Nova Micro has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command R7B Arabic, Llama-3.2-11B-Vision-Instruct and Nova Micro 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 R7B Arabic, Llama-3.2-11B-Vision-Instruct and Nova Micro share the same 128,000-token context window. Maximum output per response: Command R7B Arabic up to 4,000, Llama-3.2-11B-Vision-Instruct up to 4,096, Nova Micro up to 10,000 tokens.
Which can read images, PDFs, audio or video?
Command R7B Arabic accepts text; Llama-3.2-11B-Vision-Instruct accepts text and images; Nova Micro accepts text. Llama-3.2-11B-Vision-Instruct handles the widest range of inputs.
Are any of these open source?
Command R7B Arabic and Llama-3.2-11B-Vision-Instruct publishes its weights and can be self-hosted; Nova Micro is proprietary.
Which is newer?
Command R7B Arabic is the newest, released Feb 27, 2025. Nova Micro came out Dec 3, 2024; Llama-3.2-11B-Vision-Instruct came out Sep 25, 2024. Knowledge cutoff: Command R7B Arabic Jun 1, 2024, Llama-3.2-11B-Vision-Instruct Dec 2023, Nova Micro Oct 2024.
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.
