o3 vs DeepSeek V4 Pro
Side-by-side comparison of pricing, context window, modalities, licensing, and strengths — with practical guidance on which model fits which workload.
| Spec | o3 | DeepSeek V4 Pro |
|---|---|---|
| Provider | OpenAI | DeepSeek |
| Context window | 200K tokens | 1M tokens |
| Input price (per 1M tokens) | $2.00 | $0.44 |
| Output price (per 1M tokens) | $8.00 | $0.87 |
| Example workload (10M in + 2M out) | $36.00 / month | $6.14 / month |
| Modalities | text, image, code | text, code |
| Knowledge cutoff | Apr 2024 | Not disclosed |
| Release date | Apr 2025 | Apr 2026 |
| License | Proprietary | MIT |
| Strengths | Deep reasoning, Math, Science, Complex code | 1.6T MoE (49B active), Code & math, Ultra-low cost, Open weights |
| Open weights | No | Yes |
Verdict
Affordable deep reasoning, two ways. OpenAI’s o3 — once premium-priced, now $2/$8 after aggressive cuts — remains a benchmark leader on hard math and science with image understanding included. DeepSeek V4 Pro costs a fraction even of that ($0.44/$0.87), offers a 1M context versus o3’s 200K, supports switchable thinking/non-thinking modes, and its MIT weights can be self-hosted or distilled. o3 buys you managed OpenAI infrastructure and vision; V4 Pro buys you scale.
When to choose o3
Pick o3 for maximum accuracy on math/science reasoning, vision input, and OpenAI ecosystem compatibility.
When to choose DeepSeek V4 Pro
Pick DeepSeek V4 Pro for cheap reasoning at scale, million-token context, and self-hosting freedom.
Prices and specs reflect published provider information and change frequently — always confirm on the provider's pricing page before committing to a workload.
Frequently asked questions
Which is cheaper: o3 or DeepSeek V4 Pro?
DeepSeek V4 Pro is cheaper per token: $0.44 input / $0.87 output per 1M tokens, versus $2.00 / $8.00 for o3. Actual costs depend on your input-to-output ratio — try the AI cost calculator for your own numbers.
Which has the larger context window?
DeepSeek V4 Pro supports 1M tokens versus 200K for o3 — roughly 5.0× more room for documents, code, and conversation history.
Can I self-host either model?
DeepSeek V4 Pro has downloadable weights (MIT), so you can run it on your own hardware. o3 is proprietary and only available through its API.
How should I test which model is better for my use case?
Benchmarks are a starting point, not an answer. Run both models on 20–50 examples of your real task and compare outputs blind. Use our token counter to estimate prompt sizes and the cost calculator to project monthly spend before committing.