Command R7B vs Llama-3.2-1B vs Nova Micro
Too close to call on our weighted score (Command R7B 62, Nova Micro 62, Llama-3.2-1B 55). The right pick depends on what you value most.
CohereCommand R7B
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
MetaLlama-3.2-1B
55/100- ECI102.0
- Price$0.064 / $0.15
- Context131K
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 62/100, Nova Micro 62/100, Llama-3.2-1B 55/100), so choose by what matters most for your work: Nova Micro on price and Llama-3.2-1B for long inputs. 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 $0.066 · Llama-3.2-1B $0.085 per 1M tokens (3:1 blend)
- Longest contextLlama-3.2-1BLlama-3.2-1B 131,072 · Command R7B 128,000 · Nova Micro 128,000 tokens
- Widest inputsSame inputsCommand R7B: Text · Llama-3.2-1B: Text · Nova Micro: Text
- Self-hostingCommand R7B and Llama-3.2-1BPublishes downloadable weights (Llama 3.2 Community License)
| Measure | Weight | Command R7B | Llama-3.2-1B | Nova Micro |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 100 |
| Inputs & features | 30% | 25 | 0 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 62/100 | 55/100 | 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 R7BCohere | Llama-3.2-1BMeta | Nova MicroAmazon |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | 102.0 | — |
| ECI rank | — | #147 of 148 | — |
| GPQA DiamondGraduate-level science questions | — | 23.9% | — |
| OTIS Mock AIME 2024–2025Competition mathematics | — | 0.6% | — |
| Price per million tokens | |||
| Input | $0.037 | $0.064 | $0.035 (best) |
| Output | $0.15 | $0.15 | $0.14 (best) |
| Cached input | — | — | $0.0088 |
| Blended (3:1) | $0.066 | $0.085 | $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 | 131,072 tokens (best) | 128,000 tokens |
| Max output | 4,000 tokens | 8,192 tokens | 10,000 tokens (best) |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | No | No | No |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | No | No | No |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | OpenLlama 3.2 Community License | Proprietary |
| API model ID | command-r7b-12-2024 | — | amazon.nova-micro-v1:0 |
| API providers | 5 (best) | 2 | 3 |
| Released | Dec 2, 2024 | 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$0.675
- Llama-3.2-1B$0.936
- Nova Micro$0.63
Which should you choose?
Which is better: Command R7B, Llama-3.2-1B or Nova Micro?
It is close. Our weighted score puts them within a point (Command R7B 62/100, Nova Micro 62/100, Llama-3.2-1B 55/100), so choose by what matters most for your work: Nova Micro on price and Llama-3.2-1B for long inputs. 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, Llama-3.2-1B or Nova Micro?
Nova Micro is cheaper at $0.035 input / $0.14 output per million tokens (official Amazon Bedrock API price). Command R7B costs $0.037 input / $0.15 output per million tokens (official Cohere API price); Llama-3.2-1B costs $0.064 input / $0.15 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 (1.1× as much) and $0.085 for Llama-3.2-1B (1.4× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Command R7B has not been scored yet, Llama-3.2-1B has an ECI of 102.0 and Nova Micro has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command R7B, Llama-3.2-1B and Nova Micro yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Llama-3.2-1B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Llama-3.2-1B has the largest context window at 131,072 tokens, against 128,000 for Command R7B and 128,000 for Nova Micro. Maximum output per response: Command R7B up to 4,000, Llama-3.2-1B up to 8,192, Nova Micro up to 10,000 tokens.
Which can read images, PDFs, audio or video?
Command R7B accepts text; Llama-3.2-1B accepts text; Nova Micro accepts text. They handle the same number of input types.
Are any of these open source?
Command R7B and Llama-3.2-1B publishes its weights (Llama 3.2 Community License) and can be self-hosted; Nova Micro is proprietary.
Which is newer?
Nova Micro is the newest, released Dec 3, 2024. Command R7B came out Dec 2, 2024; Llama-3.2-1B came out Sep 25, 2024. Knowledge cutoff: Command R7B Jun 1, 2024, Llama-3.2-1B 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.
