Command A vs QVQ Max
QVQ Max comes out ahead, 40 to 24 on our weighted score, and it is the cheaper option too.
QVQ Max is our pick
QVQ Max is the better all-round choice, scoring 40/100 against Command A (24). It leads on price and inputs & features. Command A wins on context window. 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 priceQVQ MaxQVQ Max $2.10 · Command A $4.38 per 1M tokens (3:1 blend)
- Longest contextCommand ACommand A 256,000 · QVQ Max 131,072 tokens
- Widest inputsQVQ MaxCommand A: Text · QVQ Max: Text, Images
- Self-hostingCommand APublishes downloadable weights
| Measure | Weight | Command A | QVQ Max |
|---|---|---|---|
| Price | 50% | 19 | 35 |
| Inputs & features | 30% | 25 | 60 |
| Context window | 20% | 36 | 24 |
| Overall | 100% | 24/100 | 40/100 |
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 ACohere |  QVQ MaxAlibaba (Qwen) |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | $2.50 | $1.20 (best) |
| Output | $10.00 | $4.80 (best) |
| Cached input | — | — |
| Blended (3:1) | $4.38 | $2.10 (best) |
| Long-context rate | Same rate | Same rate |
| Price source | Official Cohere API | Official Alibaba API |
| Limits | ||
| Context window | 256,000 tokens (best) | 131,072 tokens |
| Max output | 8,000 tokens | 8,192 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | No |
| Audio | No | No |
| Video | No | No |
| Reasoning | No | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | No |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | command-a-03-2025 | qvq-max |
| API providers | 3 (best) | 1 |
| Released | Mar 13, 2025 | Mar 25, 2025 |
| Knowledge cutoff | Jun 1, 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 A$45.00
- QVQ Max$21.60
Which should you choose?
Which is better: Command A or QVQ Max?
QVQ Max is the better all-round choice, scoring 40/100 against Command A (24). It leads on price and inputs & features. Command A wins on context window. 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 A or QVQ Max?
QVQ Max is cheaper at $1.20 input / $4.80 output per million tokens (official Alibaba API price). Command A costs $2.50 input / $10.00 output per million tokens (official Cohere API price). At a typical mix of three input tokens to one output token, that is $2.10 per million tokens for QVQ Max versus $4.38 for Command A (2.1× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. Command A has not been scored yet and QVQ Max has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for Command A and QVQ Max yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Both support tool calling for agent workflows.
Which has the bigger context window?
Command A has the largest context window at 256,000 tokens, against 131,072 for QVQ Max. Maximum output per response: Command A up to 8,000, QVQ Max up to 8,192 tokens.
Which can read images, PDFs, audio or video?
Command A accepts text; QVQ Max accepts text and images. QVQ Max handles the widest range of inputs.
Are any of these open source?
Command A publishes its weights and can be self-hosted; QVQ Max is proprietary.
Which is newer?
QVQ Max is the newest, released Mar 25, 2025. Command A came out Mar 13, 2025. Knowledge cutoff: Command A Jun 1, 2024, QVQ Max 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.
