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Comparison · 3 models · Updated Oct 4, 2026

Gemini 2.5 Pro vs DeepSeek-R1 vs o3

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.

  1. Our pick

    Google

    Gemini 2.5 Pro

    Released Jun 17, 2025

    63/100
    • ECI145.3
    • Price$1.25 / $10.00
    • Context1.05M
    Full specs Remove Gemini 2.5 Pro

  2. DeepSeek

    DeepSeek-R1

    Released Jan 20, 2025

    51/100
    • ECI139.0
    • Price$0.70 / $2.60
    • Context128K
    Full specs Remove DeepSeek-R1

  3. OpenAI

    o3

    Released Apr 16, 2025

    58/100
    • ECI146.9
    • Price$2.00 / $8.00
    • Context200K
    Full specs Remove o3
01 — Verdict

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. DeepSeek-R1 wins on price. o3 wins on capability. 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 ProGemini 2.5 Pro: Text, Images, PDFs, Audio, Video · DeepSeek-R1: Text · o3: Text, Images, PDFs
  • Self-hostingDeepSeek-R1Publishes downloadable weights
How the score is built
MeasureWeightGemini 2.5 ProDeepSeek-R1o3
CapabilityCapabilities Index (ECI)50%726474
Price25%244724
Inputs & features15%1003580
Context window10%612432
Overall100%63/10051/10058/100
02 — Side by side

Every spec in one table

Highlighted cells lead their row. Dashes mean the data is not published.

Gemini 2.5 Pro vs DeepSeek-R1 vs o3 specifications side by side
SpecificationGemini 2.5 ProGoogleDeepSeek-R1DeepSeeko3OpenAI
Capability
Capabilities Index (ECI)145.3139.0146.9 (best)
ECI rank#78 of 148#104 of 148#63 of 148 (best)
GPQA DiamondGraduate-level science questions85.3% (best)71.7%81.8%
FrontierMath Tiers 1–3Research-level mathematics24.6%—33.3% (best)
OTIS Mock AIME 2024–2025Competition mathematics84.7% (best)53.3%84.4%
SWE-bench VerifiedFixing real GitHub issues57.6%—62.3% (best)
SimpleQA VerifiedShort factual questions——49.4%
Price per million tokens
Input$1.25$0.70 (best)$2.00
Output$10.00$2.60 (best)$8.00
Cached input$0.125 (best)—$0.50
Blended (3:1)$3.44$1.18 (best)$3.50
Long-context rateOver 200K: $2.50 / $15.00Same rateSame rate
Price sourceOfficial Google APIMedian of 11 providersOfficial OpenAI API
Limits
Context window1,048,576 tokens (best)128,000 tokens200,000 tokens
Max output65,536 tokens32,768 tokens100,000 tokens (best)
Inputs and features
TextYesYesYes
ImagesYesNoYes
PDFsYesNoYes
AudioYesNoNo
VideoYesNoNo
ReasoningYesYesYeslow · medium · high
Tool callingYesYesYes
Structured outputYesNoYes
Availability
WeightsProprietaryOpenProprietary
API model IDgemini-2.5-pro—o3
API providers22 (best)1218
ReleasedJun 17, 2025Jan 20, 2025Apr 16, 2025
Knowledge cutoffJan 2025Jul 2024May 2024
03 — Cost

What would a month cost?

Enter your expected volume in millions of tokens. List prices only; caching and batch discounts would lower these.

  • Gemini 2.5 Pro$32.50
  • DeepSeek-R1$12.20
  • o3$36.00
04 — Questions

Which should you choose?

Which is better: Gemini 2.5 Pro, DeepSeek-R1 or o3?

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. DeepSeek-R1 wins on price. o3 wins on capability. The score weighs capability 50%, price 25%, inputs & features 15%, context window 10%.

Which is cheaper, Gemini 2.5 Pro, DeepSeek-R1 or o3?

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: Gemini 2.5 Pro up to 65,536, DeepSeek-R1 up to 32,768, o3 up to 100,000 tokens.

Which can read images, PDFs, audio or video?

Gemini 2.5 Pro accepts text, images, PDFs, audio and video; DeepSeek-R1 accepts text; o3 accepts text, images and PDFs. 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; Gemini 2.5 Pro and o3 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: Gemini 2.5 Pro Jan 2025, 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.

05 — Keep comparing

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