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Model A
Gemini 2.5 Pro

Google

57.12/100

Supported · Public rank #81

90% interval 42.771.6

Gemini 2.5 Pro vs o3-mini

Updated September 10, 2026. Public scores include evidence status and uncertainty. They are not guarantees for a specific workload.

OpenAI logo
Model B
o3-mini

OpenAI

46.83/100

Supported · Public rank #150

90% interval 34.659.1

Decision reading

Gemini 2.5 Pro has the higher public score estimate, 57.12 versus 46.83, but the 90% score intervals overlap. Treat that as a lead, not a settled winner.

2 results are shared. Category rows resting on Estimated evidence or different benchmark sets are marked directional and do not name a winner.

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Which one for your work

Recommendations appear only when a shared evidence basis or an explicit operating constraint supports the call. Secondary and unsupported use cases stay disclosed below the initial list.

  • Long documents

    Prompts that approach the documented context limit

    Gemini 2.5 Pro

    Gemini 2.5 Pro has the larger documented context window.

    Confidence: documented

  • Chat turn cost

    1K fresh input + 500 output tokens

    o3-mini

    o3-mini has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

Show secondary and unsupported calls
  • Repository review cost

    50K fresh input + 3K output tokens

    o3-mini

    o3-mini has the lower estimated token cost for this stated workload. Costs use the listed standard API rates.

    Confidence: listed-rates

  • Coding work

    Code generation, repair, and software-engineering tasks

    Not enough matched evidence

    O3-mini is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like.

    Confidence: limited

  • Agentic work

    Tool use, computer use, and multi-step task completion

    Not enough matched evidence

    O3-mini is not ranked on the public lane for agentic, so no winner is named for agentic.

    Confidence: limited

  • Cache-heavy agent loop cost

    200K cached + 20K fresh input + 10K output tokens

    Not enough matched evidence

    The page does not recommend a cost winner because at least one model cannot fit the stated workload in one request. o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.

    Confidence: rate-fallback

What is actually comparable

Shared results can support a head-to-head reading. Results present for only one model describe coverage, not superiority.

Shared results
2
Gemini 2.5 Pro only
5
o3-mini only
3
Like-for-like categories
1 / 8

1 category rests on Estimated evidence or different evidence sets. Those rows remain visible for coverage context but do not name a winner.

Category results, on a stated basis

Each row shows the public-lane category score for both models: the BenchAlign lane for agentic, coding, and knowledge, and the weighted public rows elsewhere, with the same rank each model page reports. A row is like-for-like only when both scores rest on Supported evidence or the same weighted set. Directional and not-comparable rows remain visible, but they never receive a winner.

Knowledge

Like-for-like
Gemini 2.5 Pro
50.6
Supported · #76/183
o3-mini
39.7
Supported · #134/183
Basis
BenchAlign lane · 2 vs 2 public rows
Reading
Gemini 2.5 Pro leads · intervals overlap

Coding

Directional only
Gemini 2.5 Pro
31.7
Supported · #134/151
o3-mini
45.3
Estimated · #88/151
Basis
BenchAlign lane · 2 vs 1 public rows
Reading
Directional only

Agentic

Not comparable
Gemini 2.5 Pro
47.7
Estimated · #68/152
o3-mini
Not ranked
Basis
BenchAlign lane · 1 vs 0 public rows
Reading
Not comparable

Reasoning

Not comparable
Gemini 2.5 Pro
68.4
Unranked · 2 rankable rows
o3-mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Math

Not comparable
Gemini 2.5 Pro
35.2
Unranked · 2 rankable rows
o3-mini
Not ranked
Basis
Provisional lane · 2 vs 0 weighted rows
Reading
Not comparable

Multilingual

Not comparable
Gemini 2.5 Pro
Not ranked
o3-mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Multimodal

Not comparable
Gemini 2.5 Pro
70.3
Unranked · 1 rankable row
o3-mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Instruction following

Not comparable
Gemini 2.5 Pro
57.9
#74/123
o3-mini
Not ranked
Basis
Provisional lane · 0 vs 0 weighted rows
Reading
Not comparable

Ranks count the models scored in each category’s lane, so the agentic, coding, and knowledge denominators (BenchAlign) differ from the provisional-lane categories. Unranked scores sit on the lane’s scale but never name a winner. Like-for-like rows within 0.5 points read as a practical tie.

Shape of the matched evidence

Only shared public evidence is shown. Sparse evidence stays a ruled list rather than being closed into a radar shape.

Too few matched category axes support a radar. The ruled list below shows only shared benchmark results; positions use each benchmark’s normalized display scale when available.

What each workload costs

Three fixed token mixes turn per-token rates into comparable decisions. Each scenario states context fit and whether cached input had to fall back to the published list-input rate.

Chat turn

1K fresh input + 500 output tokens

Gemini 2.5 Pro
$0.00625
Fits in one request
o3-mini
$0.0033
Fits in one request

o3-mini has the lower modeled cost

Costs use the listed standard API rates.

Repository review

50K fresh input + 3K output tokens

Gemini 2.5 Pro
$0.0925
Fits in one request
o3-mini
$0.0682
Fits in one request

o3-mini has the lower modeled cost

Costs use the listed standard API rates.

Cache-heavy agent loop

200K cached + 20K fresh input + 10K output tokens

Gemini 2.5 Pro
$0.15
Fits in one request
o3-mini
$0.286
Does not fit in one request
Cached input priced at the published list-input rate

o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.

Specification differences

Sourced differences are shown directly. Missing facts stay explicit instead of being inferred from a model name or family.

Context window

Maximum documented context; output-token limits may be lower.

o3-mini

200K

Cached-input rate

A missing cached-input rate falls back to the listed input rate only in the stated workload estimate.

Gemini 2.5 Pro

$0.125 per 1M cached input tokens

Google Gemini API pricing

o3-mini

Not published

Documented inputs

Gemini 2.5 Pro

Not sourced

o3-mini

Not sourced

Documented outputs

Gemini 2.5 Pro

Not sourced

o3-mini

Not sourced

Provider availability

Gemini 2.5 Pro

Not sourced

o3-mini

Not sourced

Reasoning profile

Gemini 2.5 Pro

Non-Reasoning

o3-mini

Reasoning

Weight access

Gemini 2.5 Pro

Proprietary

o3-mini

Proprietary

License

Gemini 2.5 Pro

Proprietary

o3-mini

Proprietary

Release date

Gemini 2.5 Pro

2025-03-01

o3-mini

2025-01-31

If you already use one of these models
Deployment change
The models list different providers, so authentication, endpoint behavior, limits, and feature support may change.
Quality signal
Gemini 2.5 Pro has the higher public score estimate, 57.12 versus 46.83, but the 90% score intervals overlap.
Workload cost
Repository review: $0.0925 vs $0.0682. Cache-heavy agent loop: $0.15 vs $0.286.
Context tradeoff
Gemini 2.5 Pro has the larger documented window (1M).

Run the same representative tasks against both endpoints before changing production traffic.

Benchmark evidence

The full public result ledger is available for audit without forcing a wide desktop table onto a phone.

Browse raw public benchmark evidence10 rows

Agentic

  • Gert Labs

    Gemini 2.5 Pro42.01%
    Source
    o3-mini

    Not directly comparable

Coding

  • SWE-bench Verified

    Gemini 2.5 Pro63.8%
    Source
    o3-mini49.3%
    Source

    Gemini 2.5 Pro leads this result

  • Vibe Code Bench

    Gemini 2.5 Pro0.40%
    Source
    o3-mini

    Not directly comparable

Knowledge

  • GPQA

    Gemini 2.5 Pro83%
    Source
    o3-mini77.2%
    Source

    Gemini 2.5 Pro leads this result

  • HLE

    Gemini 2.5 Pro18.8%
    Source
    o3-mini

    Not directly comparable

  • MMLU

    Gemini 2.5 Pro
    o3-mini86.9%
    Source

    Not directly comparable

Math

  • FrontierMath v2 (Tiers 1-3)

    Gemini 2.5 Pro14.138%
    Source
    o3-mini

    Not directly comparable

  • FrontierMath v2 (Tier 4)

    Gemini 2.5 Pro4.167%
    Source
    o3-mini

    Not directly comparable

  • AIME 2024

    Gemini 2.5 Pro
    o3-mini87.3%
    Source

    Not directly comparable

Instruction following

  • IFEval

    Gemini 2.5 Pro
    o3-mini93.9%
    Source

    Not directly comparable

Frequently asked questions

Which is better, Gemini 2.5 Pro or o3-mini?

Gemini 2.5 Pro has the higher public score estimate, 57.12 versus 46.83, but the 90% score intervals overlap. The higher estimate is not a decisive winner because the uncertainty ranges overlap.

Which is better for coding, Gemini 2.5 Pro or o3-mini?

o3-mini scores higher for coding on the public lane, 45.3 to 31.7. O3-mini is scored on Estimated evidence for coding, so the reading is directional rather than like-for-like. BenchLM does not name a winner for coding from a directional reading; read the shared benchmark rows directly and test the models on the same task set.

Which is better for agentic tasks, Gemini 2.5 Pro or o3-mini?

O3-mini is not ranked on the public lane for agentic tasks, so no winner is named for agentic tasks.

Which costs less, Gemini 2.5 Pro or o3-mini?

For the stated presets, chat costs $0.00625 on Gemini 2.5 Pro and $0.0033 on o3-mini; repository review costs $0.0925 and $0.0682; the cache-heavy agent loop costs $0.15 and $0.286. o3-mini does not fit this workload in one request. o3-mini has no published cached-input rate, so cached tokens use its listed input rate.

Which has the larger context window, Gemini 2.5 Pro or o3-mini?

Gemini 2.5 Pro has the larger documented context window: 1M, compared with 200K.

Related comparisons

Last updated September 10, 2026

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