Models & Pricing

GPT-5 vs Gemini 2.5 Pro

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ComparisonGPT-5
gpt-5
Gemini 2.5 Pro
gemini-2.5-pro
TypeChatChat
Best forChat, writing, coding and reasoning tasksComplex reasoning, multi-step analysis and difficult coding
TokenRex price$1.25 in / $10.00 out per 1M tokens$1.25 in / $10.00 out per 1M tokens
Cached input / 1M tokens$0.125 per 1M tokens$0.125 per 1M tokens
Cache write / 1M tokensNo extra feeNo extra fee
Cached outputNot offered · standard output rate appliesNot offered · standard output rate applies
Market price$1.25 in / $10.00 out per 1M tokens$1.25 in / $10.00 out per 1M tokens
Context window (provider)400,000 tokensNot applicable
Input token limit (provider)Within the context window1,048,576 tokens
Maximum output128,000 tokens65,536 tokens
Knowledge cutoffSep 30, 2024January 2025
Vendor input modalitiesText, ImageText, Image, Audio, Video
Vendor output modalitiesTextText
TokenRex endpointsPOST /v1/chat/completionsPOST /v1/chat/completions
TokenRex inputText, Image/PDF content through the chat bridge—
TokenRex outputText—
Benchmarks
Benchmark evidence—6 source-linked results
GPQA Diamond—
85.3%

Official Google-reported score at launch (Gemini 2.5 Pro Experimental, Mar 2025); single-attempt/pass@1 methodology per Gemini technical report conventions. Published source text: GPQA Diamond | Reasoning | 85.3% | accuracy | Official | Mar 25, 2025

evals.report – Gemini 2.5 Pro benchmark scores (sourced from Google's official Mar 25, 2025 launch materials)
Humanity's Last Exam—
21.64%

Official Google-reported score, no tools, Mar 2025 launch. Published source text: Humanity's Last Exam | Reasoning | 21.64% | accuracy | Official | Mar 25, 2025

evals.report – Gemini 2.5 Pro benchmark scores
SWE-bench Verified—
57.6%

Official Google-reported single-attempt agentic coding score, Mar 2025 launch. Published source text: SWE-bench Verified | Coding | 57.6% | % resolved | Official | Mar 25, 2025

evals.report – Gemini 2.5 Pro benchmark scores
AIME 2025 (OTIS Mock)—
84.2%

Official Google-reported score, no tools, Mar 2025 launch. Published source text: AIME (OTIS Mock) | Reasoning | 84.2% | accuracy | Official | Mar 25, 2025

evals.report – Gemini 2.5 Pro benchmark scores
LMArena Elo—
1457elo

Human-preference blind evaluation, LMArena leaderboard, Mar 2025. Published source text: LMArena | Chat preference | 1457 | source-defined rating | Official | Mar 25, 2025

evals.report – Gemini 2.5 Pro benchmark scores
Artificial Analysis Intelligence Index—
16index

Artificial Analysis Intelligence Index v4.3.2 (current composite methodology as re-scored on AA's exact-model page, default reasoning effort), not the original v1 score at launch. Published source text: Gemini 2.5 Pro scores 16 on the Artificial Analysis Intelligence Index

Artificial Analysis – Gemini 2.5 Pro model page

No overall score, winner or cost-versus-score frontier is calculated without comparable, attributable benchmark evidence. Different test configurations appear separately. Vendor capabilities are distinct from TokenRex API support.

— means no published value or no matching capability. Provider token limits are not tested gateway guarantees; TokenRex media controls and response formats are listed separately.

GPT-5

Sources and verification

Provider specifications describe the upstream model, not a guarantee of every feature through TokenRex. Prices come from the enabled catalog; benchmark results belong to the exact tested model.

Research notes (1)
  • Published provider limits; maximum-capacity requests have not been independently exercised through TokenRex. Thinking-level variants use the same provider model; reasoning and answer tokens share the output budget.

Gemini 2.5 Pro

Sources and verification

Provider specifications describe the upstream model, not a guarantee of every feature through TokenRex. Prices come from the enabled catalog; benchmark results belong to the exact tested model.

Research notes (1)
  • Published provider limits; maximum-capacity requests have not been independently exercised through TokenRex. Thinking-level variants use the same provider model; reasoning and answer tokens share the output budget.

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