Compare AI models
| Specifications | ||
Context windowHow much information the model can work with in one request, measured in tokens — pieces of words or other data. This includes your question, attachments, conversation and reply. Limits can vary by model variant and provider. | 1M | 400k |
| API pricingUSD per 1M tokens · lower wins | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $0.30 | — |
Output priceWhat you pay for the text the model writes back. It is normally the dearer half: producing an answer costs more than reading one. | $2.50 | — |
Cached input priceA reduced rate for text you send over and over. If every request starts with the same instructions or the same document, the provider keeps a copy ready and charges less to read it again. | $0.03 | — |
Cheapest inputLowest input rate across every provider on OpenRouter, the lab included. The cheapest endpoint may run a quantised build or a shorter context — see "Available from" on the model page. | $0.15Google AI Studio | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $1.25Google AI Studio | — |
| $185Google list price | — | |
| Benchmarks | ||
BullshitBench v2Nonsense detection — Given a confidently-worded but nonsensical prompt, does the AI spot that it makes no sense and push back — instead of playing along and inventing an answer? The score is how often it clearly called out the nonsense. Higher is better. | 19% | 45% |
Next.js EvalsNext.js coding — Vercel's open eval of how well AI coding agents build and migrate real Next.js apps — measured as the share of tasks the agent completes successfully. Higher is better. | — | 46% |
LiveCodeBenchCompetitive coding — Coding problems published so recently the AI can't have seen them in training — a contamination-free test of raw programming skill. Higher is better. | 63.5% | — |
Aider Polyglot (diff)Coding — editing code in six languages using the diff edit format | 44.2% | — |
Humanity's Last Exam · no toolsMultidisciplinary reasoning — Humanity's Last Exam — extremely hard expert questions across many subjects, written so you can't just look up the answer. “No tools” means the AI answers on its own. Higher is better. | 12.1% | — |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | 78% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 78.3% | — |
SimpleQAKnowledge — accuracy on short, fact-seeking questions in the original SimpleQA set | 29.7% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 76.7% | — |
| Overview | ||
| Company | OpenAI | |
| Release date | Apr 17 2025 | Dec 18 2025 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
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GPT-5.2-Codex leads Gemini 2.5 Flash on 1 of the 1 benchmark they both report (BullshitBench v2). Only Gemini 2.5 Flash has a verified first-party price for an available API: $0.30 per million input tokens and $2.50 per million output tokens. No currently available pay-as-you-go API rate is tracked for GPT-5.2-Codex. Gemini 2.5 Flash shipped 245 days before GPT-5.2-Codex, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.5 Flash) vs 400k (GPT-5.2-Codex).
On BullshitBench v2, GPT-5.2-Codex leads at 45% vs Gemini 2.5 Flash at 19%.