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. | 200k | 1M |
| 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 |
Cheapest outputLowest output rate across every provider on OpenRouter, the lab included. May come from a different provider than the cheapest input. | — | $1.25Google |
| — | $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. | 49% | 66% |
SWE-Bench ProAgentic coding — Can the AI fix real bugs in real software? It's handed actual problems from open-source projects and has to write code that genuinely solves them. Higher is better. | — | 54.2% |
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. | 62.3% | — |
Terminal-Bench 2.1Agentic terminal coding — Can the AI work in a command-line terminal — running commands and finishing technical setup tasks the way a developer would? Higher is better. | — | 54% |
BU BenchBrowser agent — Can the AI drive a real web browser to finish tasks — clicking, filling forms, and navigating sites the way a person would? Run by Browser Use on their BU Bench task set. Higher is better. | — | 49% |
GPQA DiamondScience — Graduate-level science questions in biology, physics, and chemistry — hard enough that subject-matter PhDs score around 65%. Higher is better. | 68% | — |
OSWorld-VerifiedAgentic computer use — Can the AI actually operate a computer — clicking, typing, and using real apps — to finish tasks on its own? Higher is better. | — | 74% |
GDPval-AA v2Knowledge work — economically valuable knowledge work (v2, re-based Elo) | — | 1140 |
| Overview | ||
| Company | Anthropic | |
| Release date | Feb 24 2025 | Jul 21 2026 |
| Access | Closed | Closed |
| Model details | View model | View model |
Other comparisons
Claude 3.7 SonnetvsGPT-6.1 SolGemini 3.5 Flash-LitevsGPT-6.1 SolClaude 3.7 SonnetvsMuse Spark OpenGemini 3.5 Flash-LitevsMuse Spark OpenClaude 3.7 SonnetvsGrok 4.7Gemini 3.5 Flash-LitevsGrok 4.7Claude 3.7 SonnetvsDeepSeek-V4.1-Flash-1017Gemini 3.5 Flash-LitevsDeepSeek-V4.1-Flash-1017Claude 3.7 SonnetvsMistral Large 4Gemini 3.5 Flash-LitevsMistral Large 4Claude 3.7 SonnetvsKimi K3Gemini 3.5 Flash-LitevsKimi K3Frequently asked questions
Gemini 3.5 Flash-Lite leads Claude 3.7 Sonnet on 1 of the 1 benchmark they both report (BullshitBench v2). Only Gemini 3.5 Flash-Lite has a verified first-party API price: $0.30 per million input tokens and $2.50 per million output tokens. No pay-as-you-go API rate is tracked for Claude 3.7 Sonnet. Claude 3.7 Sonnet shipped 512 days before Gemini 3.5 Flash-Lite, so benchmark comparisons should account for the intervening progress.
Context windows are 200k (Claude 3.7 Sonnet) vs 1M (Gemini 3.5 Flash-Lite).
On BullshitBench v2, Gemini 3.5 Flash-Lite leads at 66% vs Claude 3.7 Sonnet at 49%.