o4-mini vs o4-mini-deep-research vs Gemini 2.5 Pro
Gemini 2.5 Pro comes out ahead, 84 to 55 and 49 on our weighted score, though o4-mini is 44% cheaper per token.
OpenAIo4-mini
55/100- ECI145.6
- Price$1.10 / $4.40
- Context200K
- OpenAI
o4-mini-deep-research
49/100- ECI—
- Price—
- Context200K
- Our pick
GoogleGemini 2.5 Pro
84/100- 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 84/100 against o4-mini (55) and o4-mini-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
- Lowest priceo4-minio4-mini $1.93 · Gemini 2.5 Pro $3.44 per 1M tokens (3:1 blend) · o4-mini-deep-research unpriced
- Longest contextGemini 2.5 ProGemini 2.5 Pro 1,048,576 · o4-mini 200,000 · o4-mini-deep-research 200,000 tokens
- Widest inputsGemini 2.5 Proo4-mini: Text, Images · o4-mini-deep-research: Text, Images · Gemini 2.5 Pro: Text, Images, PDFs, Audio, Video
- Self-hostingNo open weightsAll three are available only through APIs
| Measure | Weight | o4-mini | o4-mini-deep-research | Gemini 2.5 Pro |
|---|---|---|---|---|
| Inputs & features | 60% | 70 | 60 | 100 |
| Context window | 40% | 32 | 32 | 61 |
| Overall | 100% | 55/100 | 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 | o4-miniOpenAI | o4-mini-deep-researchOpenAI | Gemini 2.5 ProGoogle |
|---|---|---|---|
| Capability | |||
| Capabilities Index (ECI) | 145.6 (best) | — | 145.3 |
| ECI rank | #76 of 148 (best) | — | #78 of 148 |
| GPQA DiamondGraduate-level science questions | 79.6% | — | 85.3% (best) |
| FrontierMath Tiers 1–3Research-level mathematics | 36.1% (best) | — | 24.6% |
| OTIS Mock AIME 2024–2025Competition mathematics | 81.7% | — | 84.7% (best) |
| SWE-bench VerifiedFixing real GitHub issues | — | — | 57.6% |
| SimpleQA VerifiedShort factual questions | 19.6% | — | — |
| Price per million tokens | |||
| Input | $1.10 (best) | — | $1.25 |
| Output | $4.40 (best) | — | $10.00 |
| Cached input | $0.275 | — | $0.125 (best) |
| Blended (3:1) | $1.93 (best) | — | $3.44 |
| Long-context rate | Same rate | — | Over 200K: $2.50 / $15.00 |
| Price source | Official OpenAI API | — | Official Google API |
| Limits | |||
| Context window | 200,000 tokens | 200,000 tokens | 1,048,576 tokens (best) |
| Max output | 100,000 tokens (best) | 100,000 tokens (best) | 65,536 tokens |
| Inputs and features | |||
| Text | Yes | Yes | Yes |
| Images | Yes | Yes | Yes |
| PDFs | No | 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 | Proprietary | Proprietary |
| API model ID | o4-mini | — | gemini-2.5-pro |
| API providers | 19 | — | 22 (best) |
| Released | Apr 16, 2025 | Jun 26, 2024 | Jun 17, 2025 |
| Knowledge cutoff | May 2024 | May 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.
- o4-mini$19.80
- o4-mini-deep-research—
- Gemini 2.5 Pro$32.50
Which should you choose?
Which is better: o4-mini, o4-mini-deep-research or Gemini 2.5 Pro?
Gemini 2.5 Pro is the better all-round choice, scoring 84/100 against o4-mini (55) and o4-mini-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, o4-mini, o4-mini-deep-research or Gemini 2.5 Pro?
o4-mini is cheaper at $1.10 input / $4.40 output per million tokens (official OpenAI API price). Gemini 2.5 Pro costs $1.25 input / $10.00 output per million tokens (official Google API price). At a typical mix of three input tokens to one output token, that is $1.93 per million tokens for o4-mini versus $3.44 for Gemini 2.5 Pro (1.8× as much). o4-mini-deep-research has no published per-token price.
Which scores higher on benchmarks?
There is no independent benchmark that covers all three models yet. o4-mini has an ECI of 145.6, o4-mini-deep-research has not been scored yet and Gemini 2.5 Pro has an ECI of 145.3.
Which is better for coding?
There are no published SWE-bench Verified results for o4-mini and o4-mini-deep-research yet, so there is no like-for-like coding score. Test both on a sample of your own tasks. 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 o4-mini and 200,000 for o4-mini-deep-research. Maximum output per response: o4-mini up to 100,000, o4-mini-deep-research up to 100,000, Gemini 2.5 Pro up to 65,536 tokens.
Which can read images, PDFs, audio or video?
o4-mini accepts text and images; o4-mini-deep-research accepts text and images; 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?
No. o4-mini, o4-mini-deep-research and Gemini 2.5 Pro are proprietary and only available through APIs and apps.
Which is newer?
Gemini 2.5 Pro is the newest, released Jun 17, 2025. o4-mini came out Apr 16, 2025; o4-mini-deep-research came out Jun 26, 2024. Knowledge cutoff: o4-mini May 2024, o4-mini-deep-research May 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.
