o3-deep-research vs Vision Small
Vision Small comes out ahead, 84 to 49 on our weighted score.
OpenAIo3-deep-research
49/100- ECI—
- Price—
- Context200K
- Our pick
VisparkVision Small
84/100- ECI—
- Price—
- Context1M
Add a model
Make it a three-way comparison.
Vision Small is our pick
Vision Small is the better all-round choice, scoring 84/100 against o3-deep-research (49). It leads on inputs & features and 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
- Longest contextVision SmallVision Small 1,000,000 · o3-deep-research 200,000 tokens
- Widest inputsVision Smallo3-deep-research: Text, Images · Vision Small: Text, Images, PDFs, Audio, Video
- Self-hostingNo open weightsBoth are available only through APIs
| Measure | Weight | o3-deep-research | Vision Small |
|---|---|---|---|
| Inputs & features | 60% | 60 | 100 |
| Context window | 40% | 32 | 60 |
| Overall | 100% | 49/100 | 84/100 |
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 | o3-deep-researchOpenAI | Vision SmallVispark |
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | — | — |
| ECI rank | — | — |
| Price per million tokens | ||
| Input | — | — |
| Output | — | — |
| Cached input | — | — |
| Blended (3:1) | — | — |
| Long-context rate | — | — |
| Price source | — | — |
| Limits | ||
| Context window | 200,000 tokens | 1,000,000 tokens (best) |
| Max output | 100,000 tokens (best) | 65,536 tokens |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | Yes | Yes |
| PDFs | No | Yes |
| Audio | No | Yes |
| Video | No | Yes |
| Reasoning | Yes | Yes |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Proprietary | Proprietary |
| API model ID | — | — |
| API providers | — | — |
| Released | Jun 26, 2024 | May 15, 2024 |
| Knowledge cutoff | May 2024 | — |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o3-deep-research—
- Vision Small—
Which should you choose?
Which is better: o3-deep-research or Vision Small?
Vision Small is the better all-round choice, scoring 84/100 against o3-deep-research (49). It leads on inputs & features and 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, o3-deep-research or Vision Small?
None of these models has a published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers both models yet. o3-deep-research has not been scored yet and Vision Small has not been scored yet.
Which is better for coding?
There are no published SWE-bench Verified results for o3-deep-research and Vision Small 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?
Vision Small has the largest context window at 1,000,000 tokens, against 200,000 for o3-deep-research. Maximum output per response: o3-deep-research up to 100,000, Vision Small up to 65,536 tokens.
Which can read images, PDFs, audio or video?
o3-deep-research accepts text and images; Vision Small accepts text, images, PDFs, audio and video. Vision Small handles the widest range of inputs.
Are any of these open source?
No. o3-deep-research and Vision Small are proprietary and only available through APIs and apps.
Which is newer?
o3-deep-research is the newest, released Jun 26, 2024. Vision Small came out May 15, 2024. Knowledge cutoff: o3-deep-research May 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.
