Nova Micro vs Qwen2.5-VL 72B Instruct vs Command R7B
Too close to call on our weighted score (Nova Micro 62, Command R7B 62, Qwen2.5-VL 72B Instruct 30). The right pick depends on what you value most.
AmazonNova Micro
62/100- ECI—
- Price$0.035 / $0.14
- Context128K
Alibaba (Qwen)Qwen2.5-VL 72B Instruct
30/100- ECI—
- Price$2.80 / $8.40
- Context131K
CohereCommand R7B
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
Too close to call
It is close. Our weighted score puts them within a point (Nova Micro 62/100, Command R7B 62/100, Qwen2.5-VL 72B Instruct 30/100), so choose by what matters most for your work: Nova Micro on price and Qwen2.5-VL 72B Instruct 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 · Qwen2.5-VL 72B Instruct $4.20 per 1M tokens (3:1 blend)
- Longest contextQwen2.5-VL 72B InstructQwen2.5-VL 72B Instruct 131,072 · Nova Micro 128,000 · Command R7B 128,000 tokens
- Widest inputsQwen2.5-VL 72B InstructNova Micro: Text · Qwen2.5-VL 72B Instruct: Text, Images · Command R7B: Text
- Self-hostingQwen2.5-VL 72B Instruct and Command R7BPublishes downloadable weights
| Measure | Weight | Nova Micro | Qwen2.5-VL 72B Instruct | Command R7B |
|---|---|---|---|---|
| Price | 50% | 100 | 20 | 100 |
| Inputs & features | 30% | 25 | 50 | 25 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 62/100 | 30/100 | 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 | Nova MicroAmazon | Qwen2.5-VL 72B InstructAlibaba (Qwen) | Command R7BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.035 (best) | $2.80 | $0.037 |
| Output | $0.14 (best) | $8.40 | $0.15 |
| Cached input | $0.0088 | — | — |
| Blended (3:1) | $0.061 (best) | $4.20 | $0.066 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Amazon Bedrock API | Official Alibaba API | Official Cohere API |
| Limits | |||
| Context window | 128,000 tokens | 131,072 tokens (best) | 128,000 tokens |
| Max output | 10,000 tokens (best) | 8,192 tokens | 4,000 tokens |
| 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 | Proprietary | Open | Open |
| API model ID | amazon.nova-micro-v1:0 | qwen2-5-vl-72b-instruct | command-r7b-12-2024 |
| API providers | 3 | 1 | 5 (best) |
| Released | Dec 3, 2024 | Sep 2024 | Dec 2, 2024 |
| Knowledge cutoff | Oct 2024 | Apr 2024 | Jun 1, 2024 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- Nova Micro$0.63
- Qwen2.5-VL 72B Instruct$44.80
- Command R7B$0.675
Which should you choose?
Which is better: Nova Micro, Qwen2.5-VL 72B Instruct or Command R7B?
It is close. Our weighted score puts them within a point (Nova Micro 62/100, Command R7B 62/100, Qwen2.5-VL 72B Instruct 30/100), so choose by what matters most for your work: Nova Micro on price and Qwen2.5-VL 72B Instruct 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, Nova Micro, Qwen2.5-VL 72B Instruct or Command R7B?
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); Qwen2.5-VL 72B Instruct costs $2.80 input / $8.40 output per million tokens (official Alibaba API price). 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 $4.20 for Qwen2.5-VL 72B Instruct (69× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. Nova Micro has not been scored yet, Qwen2.5-VL 72B Instruct has not been scored yet and Command R7B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Nova Micro, Qwen2.5-VL 72B Instruct and Command R7B 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?
Qwen2.5-VL 72B Instruct has the largest context window at 131,072 tokens, against 128,000 for Nova Micro and 128,000 for Command R7B. Maximum output per response: Nova Micro up to 10,000, Qwen2.5-VL 72B Instruct up to 8,192, Command R7B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
Nova Micro accepts text; Qwen2.5-VL 72B Instruct accepts text and images; Command R7B accepts text. Qwen2.5-VL 72B Instruct handles the widest range of inputs.
Are any of these open source?
Qwen2.5-VL 72B Instruct and Command R7B publishes its weights 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; Qwen2.5-VL 72B Instruct came out Sep 2024. Knowledge cutoff: Nova Micro Oct 2024, Qwen2.5-VL 72B Instruct Apr 2024, Command R7B Jun 1, 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.
