o3 vs DeepSeek-R1 vs Gemini 2.5 Pro
Gemini 2.5 Pro comes out ahead, 63 to 58 and 51 on our weighted score, though DeepSeek-R1 is 2.9× cheaper per token.
OpenAIo3
58/100- ECI146.9
- Price$2.00 / $8.00
- Context200K
DeepSeekDeepSeek-R1
51/100- ECI139.0
- Price$0.70 / $2.60
- Context128K
- Our pick
GoogleGemini 2.5 Pro
63/100- ECI145.3
- Price$1.25 / $10.00
- Context1.05M
Gemini 2.5 Pro is our pick
Gemini 2.5 Pro is the better all-round choice, scoring 63/100 against o3 (58) and DeepSeek-R1 (51). It leads on inputs & features and context window. o3 wins on capability. DeepSeek-R1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
- Capabilityo3Capabilities Index (ECI): o3 146.9 · Gemini 2.5 Pro 145.3 · DeepSeek-R1 139.0
- Lowest priceDeepSeek-R1DeepSeek-R1 $1.18 · Gemini 2.5 Pro $3.44 · o3 $3.50 per 1M tokens (3:1 blend)
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · o3 200,000 · DeepSeek-R1 128,000 tokens
- Widest inputsGemini 2.5 Proo3: Text, Images, PDFs · DeepSeek-R1: Text · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video
- Self-hostingDeepSeek-R1Publishes downloadable weights
| Measure | Weight | o3 | DeepSeek-R1 | Gemini 2.5 Pro |
|---|---|---|---|---|
| CapabilityCapabilities Index (ECI) | 50% | 74 | 64 | 72 |
| Price | 25% | 24 | 47 | 24 |
| Inputs & features | 15% | 80 | 35 | 100 |
| Context window | 10% | 32 | 24 | 61 |
| Overall | 100% | 58/100 | 51/100 | 63/100 |
Every spec in one table
Highlighted cells lead their row. Dashes mean the data is not published.
| Specification | o3OpenAI | DeepSeek-R1DeepSeek | Gemini 2.5 ProGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 146.9 (best) | 139.0 | 145.3 |
| ECI rank | #63 of 148 (best) | #104 of 148 | #78 of 148 |
| GPQA DiamondGraduate-level science questions | 81.8% | 71.7% | 85.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 33.3% (best) | — | 24.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 84.4% | 53.3% | 84.7% (best) |
| SWE-bench VerifiedFixing real GitHub issues | 62.3% (best) | — | 57.6% |
| SimpleQA VerifiedShort factual questions | 49.4% | — | — |
| Price per million tokens | |||
| Input | $2.00 | $0.70 (best) | $1.25 |
| Output | $8.00 | $2.60 (best) | $10.00 |
| Cached input | $0.50 | — | $0.125 (best) |
| Blended (3:1) | $3.50 | $1.18 (best) | $3.44 |
| Long-context rate | Same rate | Same rate | Over 200K: $2.50 / $15.00 |
| Price source | Official OpenAI API | Median of 11 providers | Official Google API |
| Limits | |||
| Context window | 200,000 tokens | 128,000 tokens | 1,048,576 tokens (best) |
| Max output | 100,000 tokens (best) | 32,768 tokens | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | No | Yes |
| PDFs | Yes | No | Yes |
| Audio | No | No | Yes |
| Video | No | No | Yes |
| Reasoning | Yeslow · medium · high | Yes | Yes |
| Tool calling | Yes | Yes | Yes |
| Structured output | Yes | No | Yes |
| Availability | |||
| Weights | Proprietary | Open | Proprietary |
| API model ID | o3 | — | gemini-2.5-pro |
| API providers | 18 | 12 | 22 (best) |
| Released | Apr 16, 2025 | Jan 20, 2025 | Jun 17, 2025 |
| Knowledge cutoff | May 2024 | Jul 2024 | Jan 2025 |
What would a month cost?
Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.
- o3$36.00
- DeepSeek-R1$12.20
- Gemini 2.5 Pro$32.50
Which should you choose?
Which is better: o3, DeepSeek-R1 or Gemini 2.5 Pro?
Gemini 2.5 Pro is the better all-round choice, scoring 63/100 against o3 (58) and DeepSeek-R1 (51). It leads on inputs & features and context window. o3 wins on capability. DeepSeek-R1 wins on price. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.
Which is cheaper, o3, DeepSeek-R1 or Gemini 2.5 Pro?
DeepSeek-R1 is cheaper at $0.70 input / $2.60 output per million tokens (median across 11 API providers). Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price); 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 $3.44 for Gemini 2.5 Pro (2.9× as much) and $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), Gemini 2.5 Pro 145.3 (#78 of 148) and DeepSeek-R1 139.0 (#104 of 148). The confidence ranges of the top two overlap (144.9–148.6 vs 143.6–146.9), so treat the gap as small. On individual benchmarks: GPQA Diamond — Gemini 2.5 Pro 85.3%, o3 81.8%, DeepSeek-R1 71.7%; OTIS Mock AIME 2024–2025 — Gemini 2.5 Pro 84.7%, 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. All three support tool calling for agent workflows.
Which has the bigger context window?
Gemini 2.5 Pro has the largest context window at 1,048,576 tokens, against 200,000 for o3 and 128,000 for DeepSeek-R1. Maximum output per response: o3 up to 100,000, DeepSeek-R1 up to 32,768, Gemini 2.5 Pro up to 65,536 tokens.
Which can read images, PDFs, audio or video?
o3 accepts text, images and PDFs; DeepSeek-R1 accepts text; Gemini 2.5 Pro accepts text, images, PDFs, audio and video. Gemini 2.5 Pro handles the widest range of inputs.
Are any of these open source?
DeepSeek-R1 publishes its weights and can be self-hosted; o3 and Gemini 2.5 Pro is proprietary.
Which is newer?
Gemini 2.5 Pro is the newest, released Jun 17, 2025. o3 came out Apr 16, 2025; DeepSeek-R1 came out Jan 20, 2025. Knowledge cutoff: o3 May 2024, DeepSeek-R1 Jul 2024, Gemini 2.5 Pro Jan 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.
