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. | $0.10 | $12.50 |
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. | $0.40 | $75.00 |
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. | $0.01 | $1.25 |
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. | $0.05Google AI Studio | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $0.20Google AI Studio | — |
| $40.00Google list price | $6,250OpenAI list price | |
| These models have no shared benchmark scores. | ||
SWE-Bench VerifiedCoding — Real coding tasks pulled from open-source projects — the AI has to find and fix actual bugs. A human-checked version of the original SWE-Bench. Higher is better. | 31.6% | — |
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. | 33.7% | — |
Advanced Cybersecurity Completion Rate (Internal)Agentic tasks — share of advanced cyber requests the model responds to; measures willingness to answer, not exploit success | — | 95% |
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. | 5.1% | — |
AIME 2025Reasoning — single-answer accuracy on the 2025 invitational mathematics exam without tools | 49.8% | — |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 64.6% | — |
SimpleQAKnowledge — accuracy on short, fact-seeking questions in the original SimpleQA set | 10.7% | — |
MMMUMultimodal — Tests the AI on understanding images and text together across many college subjects. Higher is better. | 72.9% | — |
MRCR v2 (8-needle) · 128k averageLong context — Tests whether the AI can find specific details buried inside a very long document (around 128k tokens — roughly a long book). Higher is better. | 16.6% | — |
MRCR v2 (8-needle) · 1M pointwiseLong context — Tests whether the AI can find specific details buried inside an enormous document (around 1 million tokens — many books). Higher is better. | 4.1% | — |
| Overview | ||
| Company | OpenAI | |
| Release date | Jun 17 2025 | Aug 10 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Gemini 2.5 Flash-LitevsClaude Fable 5.2GPT-5.6 CybervsClaude Fable 5.2Gemini 2.5 Flash-LitevsMuse EmberGPT-5.6 CybervsMuse EmberGemini 2.5 Flash-LitevsGrok 4.7GPT-5.6 CybervsGrok 4.7Gemini 2.5 Flash-LitevsDeepSeek-V4.1-Flash-1017GPT-5.6 CybervsDeepSeek-V4.1-Flash-1017Gemini 2.5 Flash-LitevsMistral Large 4GPT-5.6 CybervsMistral Large 4Gemini 2.5 Flash-LitevsKimi K3.1 ProGPT-5.6 CybervsKimi K3.1 ProFrequently asked questions
Gemini 2.5 Flash-Lite and GPT-5.6 Cyber don't publish scores on any of the same benchmarks, so there's no direct head-to-head comparison. Gemini 2.5 Flash-Lite is cheaper on both input and output: $0.10 vs $12.50 per million input tokens, and $0.40 vs $75.00 per million output tokens. Gemini 2.5 Flash-Lite shipped 419 days before GPT-5.6 Cyber, so benchmark comparisons should account for the intervening progress.
Context windows are 1M (Gemini 2.5 Flash-Lite) vs 400k (GPT-5.6 Cyber).
Direct benchmark comparisons are unavailable — Gemini 2.5 Flash-Lite and GPT-5.6 Cyber don't publish scores on any of the same benchmarks.