o1-pro vs Qwen3-Coder 480B-A35B Instruct vs Aya Vision 32B
o1-pro comes out ahead, 55 to 30 and 15 on our weighted score, though Qwen3-Coder 480B-A35B Instruct is 88× cheaper per token.
- Our pick
OpenAIo1-pro
55/100- ECI—
- Price$150.00 / $600.00
- Context200K
Alibaba (Qwen)Qwen3-Coder 480B-A35B Instruct
30/100- ECI—
- Price$1.50 / $7.50
- Context262K
CohereAya Vision 32B
15/100- ECI—
- Price—
- Context16K
o1-pro is our pick
o1-pro is the better all-round choice, scoring 55/100 against Qwen3-Coder 480B-A35B Instruct (30) and Aya Vision 32B (15). It leads on inputs & features. Qwen3-Coder 480B-A35B Instruct wins on context window. The score weighs inputs & features 60%, context window 40%. 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-Coder 480B-A35B InstructQwen3-Coder 480B-A35B Instruct $3.00 · o1-pro $262.50 per 1M tokens (3:1 blend) · Aya Vision 32B unpriced
- Longest contextQwen3-Coder 480B-A35B InstructQwen3-Coder 480B-A35B Instruct 262,144 · o1-pro 200,000 · Aya Vision 32B 16,000 tokens
- Widest inputso1-pro and Aya Vision 32Bo1-pro: Text, Images · Qwen3-Coder 480B-A35B Instruct: Text · Aya Vision 32B: Text, Images
- Self-hostingQwen3-Coder 480B-A35B Instruct and Aya Vision 32BPublishes downloadable weights (CC-BY-NC-4.0)
| Measure | Weight | o1-pro | Qwen3-Coder 480B-A35B Instruct | Aya Vision 32B |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 25 | 25 |
| Context window | 40% | 32 | 37 | 0 |
| Overall | 100% | 55/100 | 30/100 | 15/100 |
Left out because at least one model lacks the data: capability and price. The remaining weights were rescaled.
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | o1-proOpenAI | Qwen3-Coder 480B-A35B InstructAlibaba (Qwen) | Aya Vision 32BCohere |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | — | — | — |
| ECI rank | — | — | — |
| Price per million tokens | |||
| Input | $150.00 | $1.50 (best) | — |
| Output | $600.00 | $7.50 (best) | — |
| Cached input | — | — | — |
| Blended (3:1) | $262.50 | $3.00 (best) | — |
| Long-context rate | Same rate | Over 32K: $2.70 / $13.50 | — |
| Price source | Official OpenAI API | Official Alibaba API | — |
| Limits | |||
| Context window | 200,000 tokens | 262,144 tokens (best) | 16,000 tokens |
| Max output | 100,000 tokens (best) | 65,536 tokens | 4,000 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | No | No | No |
| Audio | No | No | No |
| Video | No | No | No |
| Reasoning | Yeslow · medium · high | No | No |
| Tool calling | Yes | Yes | No |
| Structured output | Yes | No | No |
| Availability | |||
| Weights | Proprietary | Open | OpenCC-BY-NC-4.0 |
| API model ID | o1-pro | qwen3-coder-480b-a35b-instruct | c4ai-aya-vision-32b |
| API providers | 5 | 7 (best) | 1 |
| Released | Mar 19, 2025 | Apr 2025 | Mar 4, 2025 |
| Knowledge cutoff | Sep 2023 | Apr 2025 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o1-pro$2,700
- Qwen3-Coder 480B-A35B Instruct$30.00
- Aya Vision 32B—
Which should you choose?
Which is better: o1-pro, Qwen3-Coder 480B-A35B Instruct or Aya Vision 32B?
o1-pro is the better all-round choice, scoring 55/100 against Qwen3-Coder 480B-A35B Instruct (30) and Aya Vision 32B (15). It leads on inputs & features. Qwen3-Coder 480B-A35B Instruct wins on context window. The score weighs inputs & features 60%, context window 40%. None of the shared benchmarks cover every model here yet, so capability is left out of this verdict.
Which is cheaper, o1-pro, Qwen3-Coder 480B-A35B Instruct or Aya Vision 32B?
Qwen3-Coder 480B-A35B Instruct is cheaper at $1.50 input / $7.50 output per million tokens (official Alibaba API price). o1-pro costs $150.00 input / $600.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $3.00 per million tokens for Qwen3-Coder 480B-A35B Instruct versus $262.50 for o1-pro (88× as much). Aya Vision 32B has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. o1-pro has not been scored yet, Qwen3-Coder 480B-A35B Instruct has not been scored yet and Aya Vision 32B has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for o1-pro, Qwen3-Coder 480B-A35B Instruct and Aya Vision 32B yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. Note that Aya Vision 32B does not support tool calling, which most coding agents need.
Which has the bigger context window?
Qwen3-Coder 480B-A35B Instruct has the largest context window at 262,144 tokens, against 200,000 for o1-pro and 16,000 for Aya Vision 32B. Maximum output per response: o1-pro up to 100,000, Qwen3-Coder 480B-A35B Instruct up to 65,536, Aya Vision 32B up to 4,000 tokens.
Which can read images, PDFs, audio or video?
o1-pro accepts text and images; Qwen3-Coder 480B-A35B Instruct accepts text; Aya Vision 32B accepts text and images. o1-pro handles the widest range of inputs.
Are any of these open source?
Qwen3-Coder 480B-A35B Instruct and Aya Vision 32B publishes its weights (CC-BY-NC-4.0) and can be self-hosted; o1-pro is proprietary.
Which is newer?
Qwen3-Coder 480B-A35B Instruct is the newest, released Apr 2025. o1-pro came out Mar 19, 2025; Aya Vision 32B came out Mar 4, 2025. Knowledge cutoff: o1-pro Sep 2023, Qwen3-Coder 480B-A35B Instruct Apr 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 Oct 4, 2026.
