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. | 128k | — |
| API pricingUSD per 1M tokens · lower wins · base tier | ||
Input priceWhat you pay for everything you send the model — your question, plus any documents or earlier conversation you include with it. | $2.50 | $1.25 |
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. | $10.00 | $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. | $1.25 | $0.20 |
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. | $2.50Azure | — |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | $10.00Azure | — |
| $1,000OpenAI list price | $375SpaceXSI 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. | 12% | 56% |
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. | — | 76.7% |
ARC-AGI-2Abstract reasoning — Puzzle-style tests of abstract reasoning and pattern-finding — the kind of thing people find easy but AIs often struggle with. Higher is better. | — | 53.3% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 49.9% | — |
| Overview | ||
| Company | OpenAI | SpaceXSI |
| Release date | May 13 2024 | Feb 17 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
GPT-4ovsClaude Haiku 5.5Grok 4.20 BetavsClaude Haiku 5.5GPT-4ovsGemini 4 CarbonGrok 4.20 BetavsGemini 4 CarbonGPT-4ovsMuse Spark OpenGrok 4.20 BetavsMuse Spark OpenGPT-4ovsDeepSeek-V4.1-Flash-1017Grok 4.20 BetavsDeepSeek-V4.1-Flash-1017GPT-4ovsMistral Large 4Grok 4.20 BetavsMistral Large 4GPT-4ovsKimi K3.1 ProGrok 4.20 BetavsKimi K3.1 ProFrequently asked questions
Grok 4.20 Beta leads GPT-4o on 1 of the 1 benchmark they both report (BullshitBench v2). Grok 4.20 Beta is cheaper on both input and output: $1.25 vs $2.50 per million input tokens, and $2.50 vs $10.00 per million output tokens. Figures are base-tier rates. GPT-4o shipped 645 days before Grok 4.20 Beta, so benchmark comparisons should account for the intervening progress.
Published specifications for these two models are limited — see each model page for the latest details.
On BullshitBench v2, Grok 4.20 Beta leads at 56% vs GPT-4o at 12%.