DeepSeek-R1 vs o3
o3 comes out ahead, 58 to 51 on our weighted score, though DeepSeek-R1 is 3× cheaper per token.
DeepSeek
DeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
- Our pick
OpenAI
o3
58/100- ECI146.9
- Price$2.00 / $8.00
- Context200K
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Make it a three-way comparison.
o3 is our pick
o3 is the better all-round choice, scoring 58/100 against DeepSeek-R1 (51). It leads on capability, inputs & features and context window. DeepSeek-R1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- Capabilityo3Capabilities Index (ECI): o3 146.9 · DeepSeek-R1 139.0
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · o3 $3.50 per 1M tokens (3:1 blend)
- Longest contexto3o3 200,000 · DeepSeek-R1 128,000 tokens
- Widest inputso3DeepSeek-R1: Text · o3: Text, Images, PDFs
- Self-hostingDeepSeek-R1Publishes downloadable weights
| Measure | Weight | DeepSeek-R1 | o3 |
|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 64 | 74 |
| Price | 25% | 47 | 24 |
| Inputs & features | 15% | 35 | 80 |
| Context window | 10% | 24 | 32 |
| Overall | 100% | 51/100 | 58/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | ||
|---|---|---|
| Capability | ||
| Capabilities Index (ECI) | 139.0 | 146.9 (best) |
| ECI rank | #104 of 148 | #63 of 148 (best) |
| GPQA DiamondGraduate-level science questions | 71.7% | 81.8% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | — | 33.3% |
| OTIS Mock AIME 2024–2025Competition mathematics | 53.3% | 84.4% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | 62.3% |
| SimpleQA VerifiedShort factual questions | — | 49.4% |
| Price per million tokens | ||
| Input | $0.70 (best) | $2.00 |
| Output | $2.60 (best) | $8.00 |
| Cached input | — | $0.50 |
| Blended (3:1) | $1.18 (best) | $3.50 |
| Long-context rate | Same rate | Same rate |
| Price source | Median of 11 providers | Official OpenAI API |
| Limits | ||
| Context window | 128,000 tokens | 200,000 tokens (best) |
| Max output | 32,768 tokens | 100,000 tokens (best) |
| Inputs and features | ||
| Text | Yes | Yes |
| Images | No | Yes |
| PDFs | No | Yes |
| Audio | No | No |
| Video | No | No |
| Reasoning | Yes | Yeslow · medium · high |
| Tool calling | Yes | Yes |
| Structured output | No | Yes |
| Availability | ||
| Weights | Open | Proprietary |
| API model ID | — | o3 |
| API providers | 12 | 18 (best) |
| Released | Jan 20, 2025 | Apr 16, 2025 |
| Knowledge cutoff | Jul 2024 | 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.
DeepSeek-R1$12.20
o3$36.00
Which should you choose?
Which is better: DeepSeek-R1 or o3?
o3 is the better all-round choice, scoring 58/100 against DeepSeek-R1 (51). It leads on capability, inputs & features and context window. DeepSeek-R1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, DeepSeek-R1 or o3?
DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). o3 costs $2.00 input / $8.00 output per million tokens (official OpenAI API price). At a typical mix of three input tokens to one output token, that is $1.18 per million tokens for DeepSeek-R1 versus $3.50 for o3 (3× as much).
Which scores higher on benchmarks?
o3 scores higher on the Capabilities Index (ECI): o3 146.9 (#63 of 148) and DeepSeek-R1 139.0 (#104 of 148). Their confidence ranges do not overlap (144.9–148.6 vs 136.2–140.4), so the gap is a real one. On individual benchmarks: GPQA Diamond — o3 81.8%, DeepSeek-R1 71.7%; OTIS Mock AIME 2024–2025 — o3 84.4%, DeepSeek-R1 53.3%.
Which is better for coding?
There are no published SWE-bench Verified results for DeepSeek-R1 yet, so there is no like-for-like coding score. On overall capability, o3 leads, which tends to carry over to coding, but test on your own codebase. Both support tool calling for agent workflows.
Which has the bigger context window?
o3 has the largest context window at 200,000 tokens, against 128,000 for DeepSeek-R1. Maximum output per response: DeepSeek-R1 up to 32,768, o3 up to 100,000 tokens.
Which can read images, PDFs, audio or video?
DeepSeek-R1 accepts text; o3 accepts text, images and PDFs. o3 handles the widest range of inputs.
Are any of these open source?
DeepSeek-R1 publishes its weights and can be self-hosted; o3 is proprietary.
Which is newer?
o3 is the newest, released Apr 16, 2025. DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: DeepSeek-R1 Jul 2024, o3 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.