Gemini 2.5 Pro vs o3

Across our 12-test internal benchmark suite, Gemini 2.5 Pro and o3 produce a genuinely close matchup: Gemini 2.5 Pro wins 3 tests, o3 wins 3, and they tie on 6.

Where Gemini 2.5 Pro leads:

• Long context (5 vs 4): Gemini 2.5 Pro ties for 1st among 55 models tested; o3 ranks 38th of 55. With a 1M-token context window, this is where Gemini 2.5 Pro has a structural advantage for retrieval tasks at 30K+ tokens.
• Creative problem solving (5 vs 4): Gemini 2.5 Pro ties for 1st among 8 models out of 54; o3 ranks 9th of 54 among 21 models sharing its score. For generating non-obvious, feasible ideas, Gemini 2.5 Pro has a meaningful edge in our testing.
• Classification (4 vs 3): Gemini 2.5 Pro ties for 1st among 30 models out of 53; o3 ranks 31st of 53. This matters for routing and categorization pipelines where accuracy directly affects downstream logic.

Where o3 leads:

• Strategic analysis (5 vs 4): o3 ties for 1st among 26 models out of 54; Gemini 2.5 Pro ranks 27th of 54. For nuanced tradeoff reasoning with real numbers — financial modeling, strategic planning documents — o3 has the edge in our tests.
• Agentic planning (5 vs 4): o3 ties for 1st among 15 models out of 54; Gemini 2.5 Pro ranks 16th of 54. Goal decomposition and failure recovery in multi-step agent workflows favor o3.
• Constrained rewriting (4 vs 3): o3 ranks 6th of 53; Gemini 2.5 Pro ranks 31st of 53. For compression tasks with hard character limits — ad copy, headlines, SMS content — o3 is noticeably more reliable in our testing.

Where they tie (both score equally): Structured output (5/5), tool calling (5/5), faithfulness (5/5), persona consistency (5/5), multilingual (5/5), and safety calibration (1/1) are all matched. The tie on safety calibration is notable — both score 1/5, ranking 32nd of 55, well below the field median of 2. Neither model excels at refusing harmful requests while permitting legitimate ones in our testing.

External benchmarks (Epoch AI): On SWE-bench Verified (real GitHub issue resolution), o3 scores 62.3% (rank 9 of 12 models tested) vs Gemini 2.5 Pro's 57.6% (rank 10 of 12). Both sit below the field median of 70.8% among models with this score, meaning neither is a standout coding model by this external measure — though o3 has a 4.7 percentage point lead.

On AIME 2025 (math olympiad), Gemini 2.5 Pro scores 84.2% (rank 11 of 23) vs o3's 83.9% (rank 12 of 23) — essentially identical, and both near the field median of 83.9%.

o3 also has a Math Level 5 score of 97.8% (rank 2 of 14, tied with 2 others), placing it among the top competition math performers by that external measure. Gemini 2.5 Pro has no Math Level 5 score in the payload for direct comparison.

In summary: o3 holds a modest but real edge on coding tasks per SWE-bench, and leads on advanced math per Math Level 5. Gemini 2.5 Pro wins on long context and creative problem solving in our internal tests. The two are nearly indistinguishable on math olympiad problems.

Gemini 2.5 Pro is priced at $1.25/MTok input and $10.00/MTok output. o3 costs $2.00/MTok input and $8.00/MTok output. The price ratio between the two models is approximately 1.25x overall, but the direction flips depending on whether your workload is input-heavy or output-heavy.

For an input-heavy workload (e.g., long-document analysis where you send 10 tokens for every 1 you receive):

• At 10M input tokens/month: Gemini 2.5 Pro costs ~$12.50 vs o3's ~$20.00 — a $7.50 monthly savings.
• At 100M input tokens/month: Gemini 2.5 Pro saves ~$75 on input alone.

For an output-heavy workload (e.g., bulk content generation with short prompts):

• At 10M output tokens/month: o3 costs ~$80 vs Gemini 2.5 Pro's ~$100 — o3 saves $20/month.
• At 100M output tokens/month: o3 saves ~$200 on output.

For balanced workloads at roughly 1:1 input/output ratio, the costs converge: at 10M tokens each, Gemini 2.5 Pro totals ~$112.50 vs o3's ~$100. At that ratio, o3 is modestly cheaper. The cost gap only becomes significant at high output volumes — developers running large-scale generation pipelines will favor o3 on price, while those doing RAG or document analysis over long contexts will save with Gemini 2.5 Pro.

Choose Gemini 2.5 Pro if:

• Your application involves documents, codebases, or transcripts exceeding 200K tokens — its 1M-token context window is a hard structural advantage.
• You need audio or video input processing, which o3 does not support per the payload.
• Your workload is input-heavy (large prompts, long documents) and you want cheaper input costs at $1.25/MTok vs o3's $2.00/MTok.
• Creative ideation, brainstorming, or classification accuracy are central to your use case — Gemini 2.5 Pro outscores o3 on both in our testing.
• You want the temperature and top_p parameters available, which are listed in Gemini 2.5 Pro's supported parameters but absent from o3's.

Choose o3 if:

• You're building multi-step agents that require strong goal decomposition and failure recovery — o3 scores 5/5 on agentic planning vs Gemini 2.5 Pro's 4/5 in our testing.
• Your use case involves high-stakes strategic analysis or constrained text production (e.g., copy within character limits) where o3 outperforms in our benchmarks.
• You generate significantly more output tokens than input tokens, making o3's $8.00/MTok output rate cheaper than Gemini 2.5 Pro's $10.00.
• Coding accuracy matters: o3 scores 62.3% on SWE-bench Verified vs 57.6% for Gemini 2.5 Pro (Epoch AI), and 97.8% on Math Level 5 (Epoch AI) for math-heavy applications.
• Your context requirements fit within 200K tokens and you don't need audio/video modalities.