GPT-5.3 Chat vs Qwen3.5 9B Base vs Command A Plus
Qwen3.5 9B Base comes out ahead, 50 to 35 and 28 on our weighted score, and it is the cheaper option too.
OpenAIGPT-5.3 Chat
28/100- ECI—
- Price$1.75 / $14.00
- Context128K
- Our pick
Alibaba (Qwen)Qwen3.5 9B Base
50/100- ECI—
- Price$0.30 / $0.807
- Context262K
CohereCommand A Plus
35/100- ECI—
- Price$2.50 / $10.00
- Context128K
Qwen3.5 9B Base is our pick
Qwen3.5 9B Base is the better all-round choice, scoring 50/100 against Command A Plus (35) and GPT-5.3 Chat (28). It leads on price and context window. Command A Plus wins on inputs & features. 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 priceQwen3.5 9B BaseQwen3.5 9B Base $0.427 · Command A Plus $4.38 · GPT-5.3 Chat $4.81 per 1M tokens (3:1 blend)
- Longest contextQwen3.5 9B BaseQwen3.5 9B Base 262,144 · GPT-5.3 Chat 128,000 · Command A Plus 128,000 tokens
- Widest inputsQwen3.5 9B BaseGPT-5.3 Chat: Text, Images · Qwen3.5 9B Base: Text, Images, Video · Command A Plus: Text, Images
- Self-hostingQwen3.5 9B Base and Command A PlusPublishes downloadable weights (Apache-2.0)
| Measure | Weight | GPT-5.3 Chat | Qwen3.5 9B Base | Command A Plus |
|---|---|---|---|---|
| Price | 50% | 17 | 65 | 19 |
| Inputs & features | 30% | 50 | 35 | 70 |
| Context window | 20% | 24 | 37 | 24 |
| Overall | 100% | 28/100 | 50/100 | 35/100 |
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 | GPT-5.3 ChatOpenAI | Qwen3.5 9B BaseAlibaba (Qwen) | Command A PlusCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $1.75 | $0.30 (best) | $2.50 |
| Output | $14.00 | $0.807 (best) | $10.00 |
| Cached input | — | — | — |
| Blended (3:1) | $4.81 | $0.427 (best) | $4.38 |
| Long-context rate | Same rate | Same rate | Same rate |
| Price source | Median of 4 providers | Median of 1 providers | Official Cohere API |
| Limits | |||
| Context window | 128,000 tokens | 262,144 tokens (best) | 128,000 tokens |
| Max output | 16,384 tokens | 65,536 tokens (best) | 64,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | Yes | No |
| Reasoning | No | No | Yes |
| Tool calling | Yes | No | Yes |
| Structured output | No | No | Yes |
| Availability | |||
| Weights | Proprietary | OpenApache-2.0 | Open |
| API model ID | — | — | command-a-plus-05-2026 |
| API providers | 4 (best) | 1 | 3 |
| Released | Mar 3, 2026 | Feb 26, 2026 | May 20, 2026 |
| Knowledge cutoff | Aug 31, 2025 | — | Apr 1, 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- GPT-5.3 Chat$45.50
- Qwen3.5 9B Base$4.61
- Command A Plus$45.00
Which should you choose?
Which is better: GPT-5.3 Chat, Qwen3.5 9B Base or Command A Plus?
Qwen3.5 9B Base is the better all-round choice, scoring 50/100 against Command A Plus (35) and GPT-5.3 Chat (28). It leads on price and context window. Command A Plus wins on inputs & features. 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, GPT-5.3 Chat, Qwen3.5 9B Base or Command A Plus?
Qwen3.5 9B Base is cheaper at $0.30 input / $0.807 output per million tokens (median across 1 API provider). Command A Plus costs $2.50 input / $10.00 output per million tokens (official Cohere API price); GPT-5.3 Chat costs $1.75 input / $14.00 output per million tokens (median across 4 API providers). At a typical mix of three input tokens to one output token, that is $0.427 per million tokens for Qwen3.5 9B Base versus $4.38 for Command A Plus (10× as much) and $4.81 for GPT-5.3 Chat (11× as much).
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. GPT-5.3 Chat has not been scored yet, Qwen3.5 9B Base has not been scored yet and Command A Plus has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for GPT-5.3 Chat, Qwen3.5 9B Base and Command A Plus yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Qwen3.5 9B Base does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen3.5 9B Base has the largest context window at 262,144 tokens, against 128,000 for GPT-5.3 Chat and 128,000 for Command A Plus. Maximum output per response: GPT-5.3 Chat up to 16,384, Qwen3.5 9B Base up to 65,536, Command A Plus up to 64,000 tokens.
Which can read images, PDFs, audio or video?
GPT-5.3 Chat accepts text and images; Qwen3.5 9B Base accepts text, images and video; Command A Plus accepts text and images. Qwen3.5 9B Base handles the widest range of inputs.
Are any of these open source?
Qwen3.5 9B Base and Command A Plus publishes its weights (Apache-2.0) and can be self-hosted; GPT-5.3 Chat is proprietary.
Which is newer?
Command A Plus is the newest, released May 20, 2026. GPT-5.3 Chat came out Mar 3, 2026; Qwen3.5 9B Base came out Feb 26, 2026. Knowledge cutoff: GPT-5.3 Chat Aug 31, 2025, Command A Plus Apr 1, 2025.
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 Nov 2, 2026.
