Command R7B vs Nova Micro vs Qwen2.5-VL 72B Instruct
Too close to call on our weighted score (Command R7B 62, Nova Micro 62, Qwen2.5-VL 72B Instruct 30). The right pick depends on what you value most.
CohereCommand R7B
62/100- ECI—
- Price$0.037 / $0.15
- Context128K
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
Too close to call
It is close. Our weighted score puts them within a point (Command R7B 62/100, Nova Micro 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 · Command R7B 128,000 · Nova Micro 128,000 tokens
- Widest inputsQwen2.5-VL 72B InstructCommand R7B: Text · Nova Micro: Text · Qwen2.5-VL 72B Instruct: Text, Images
- Self-hostingCommand R7B and Qwen2.5-VL 72B InstructPublishes downloadable weights
| Measure | Weight | Command R7B | Nova Micro | Qwen2.5-VL 72B Instruct |
|---|---|---|---|---|
| Price | 50% | 100 | 100 | 20 |
| Inputs & features | 30% | 25 | 25 | 50 |
| Context window | 20% | 24 | 24 | 24 |
| Overall | 100% | 62/100 | 62/100 | 30/100 |
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 | Nova MicroAmazon | Qwen2.5-VL 72B InstructAlibaba (Qwen) |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $0.037 | $0.035 (best) | $2.80 |
| Output | $0.15 | $0.14 (best) | $8.40 |
| Cached input | — | $0.0088 | — |
| Blended (3:1) | $0.066 | $0.061 (best) | $4.20 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Official Cohere API | Official Amazon Bedrock API | Official Alibaba API |
| Limits | |||
| Context window | 128,000 tokens | 128,000 tokens | 131,072 tokens (best) |
| Max output | 4,000 tokens | 10,000 tokens (best) | 8,192 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 | No | No |
| Tool calling | Yes | Yes | Yes |
| Structured output | No | No | No |
| Availability | |||
| Weights | Open | Proprietary | Open |
| API model ID | command-r7b-12-2024 | amazon.nova-micro-v1:0 | qwen2-5-vl-72b-instruct |
| API providers | 5 (best) | 3 | 1 |
| Released | Dec 2, 2024 | Dec 3, 2024 | Sep 2024 |
| Knowledge cutoff | Jun 1, 2024 | Oct 2024 | Apr 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
- Nova Micro$0.63
- Qwen2.5-VL 72B Instruct$44.80
Which should you choose?
Which is better: Command R7B, Nova Micro or Qwen2.5-VL 72B Instruct?
It is close. Our weighted score puts them within a point (Command R7B 62/100, Nova Micro 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, Command R7B, Nova Micro or Qwen2.5-VL 72B Instruct?
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. Command R7B has not been scored yet, Nova Micro has not been scored yet and Qwen2.5-VL 72B Instruct has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command R7B, Nova Micro and Qwen2.5-VL 72B 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?
Qwen2.5-VL 72B Instruct 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, Nova Micro up to 10,000, Qwen2.5-VL 72B Instruct up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Command R7B accepts text; Nova Micro accepts text; Qwen2.5-VL 72B Instruct accepts text and images. Qwen2.5-VL 72B Instruct handles the widest range of inputs.
Are any of these open source?
Command R7B and Qwen2.5-VL 72B Instruct 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: Command R7B Jun 1, 2024, Nova Micro Oct 2024, Qwen2.5-VL 72B Instruct Apr 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.
